Google fragen

Sie suchten nach: specify (Persisch - Englisch)

Menschliche Beiträge

Von professionellen Übersetzern, Unternehmen, Websites und kostenlos verfügbaren Übersetzungsdatenbanken.

Übersetzung hinzufügen

Persisch

Englisch

Info

Persisch

Please, specify two different languages

Englisch

Structural analysis of social behavior

Letzte Aktualisierung: 2017-06-04
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

https://wiki.cyanogenmod.org/w/Doc:_porting_intro

Letzte Aktualisierung: 2016-10-22
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

persian

Letzte Aktualisierung: 2016-05-08
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

kaichou wa maid-sama

Letzte Aktualisierung: 2015-01-25
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

shovel-truck system

Letzte Aktualisierung: 2014-12-01
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

A Fantastic Fear of Everything

Letzte Aktualisierung: 2014-11-04
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

Energy intensities of countries are easy to establish and, therefore, published by financial institutions

Letzte Aktualisierung: 2014-07-22
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

i got you now lol was up

Letzte Aktualisierung: 2012-12-21
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

pactbop

Letzte Aktualisierung: 2011-12-09
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

containers samples contents examples

Letzte Aktualisierung: 2010-02-05
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

pen

Letzte Aktualisierung: 2010-01-23
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

استاندارد های بین المللی حسابداری

Letzte Aktualisierung: 2009-11-30
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Please, specify two different languages

Englisch

IFRS

Letzte Aktualisierung: 2009-11-30
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

One of the more powerful LayoutManager implementations is the GridBagLayout class which requires the use of the GridBagConstraints class to specify how layout control occurs.

Englisch

One of the more powerful LayoutManager implementations is the GridBagLayout class which requires the use of the GridBagConstraints class to specify how layout control occurs.

Letzte Aktualisierung: 2016-03-03
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

A Taxonomy of Job Scheduling on Distributed Computing Systems Raquel V. Lopes, Member, IEEE, and Daniel Menasce´, Fellow, IEEE Abstract—Hundreds of papers on job scheduling for distributed systems are published every year and it becomes increasingly difficult to classify them. Our analysis revealed that half of these papers are barely cited. This paper presents a general taxonomy for scheduling problems and solutions in distributed systems. This taxonomy was used to classify and make publicly available the classification of 109 scheduling problems and their solutions. These 109 problems were further clustered into ten groups based on the features of the taxonomy. The proposed taxonomy will facilitate researchers to build on prior art, increase new research visibility, and minimize redundant effort. Index Terms—Taxonomy, scheduling, distributed jobs, cluster, grid computing, cloud computing. ◆ 1 INTRODUCTION N the last decade, cluster computing emerged as the main platform for high performance, grid, and cloud computing. Together, these three different, yet very simi- lar platforms, emerged as important sources of computing power. They all consist of distributed computers (or nodes) connected through high speed networks. Most of the scheduling problems are computationally hard [1], [2], [3], and they have been attracting the attention of researchers for decades. Thousands of solutions have been published, dealing with slightly different versions of a scheduling problem. Indeed, there are many knobs that may be tuned in order to clearly specify a scheduling problem of this nature. To the best of our knowledge, these knobs have not been defined for general scheduling problems, leading an important researcher to clamor for the need of a proper definition of scheduling problems: At the very minimum, we wish that all papers about job schedulers, either real or paper design, make clear their assumptions about the workload, the permissible actions allowed by the system, and the metric that is being optimized. [4] Twenty years later, the situation has not improved. So far, the many knobs needed to define a scheduling problem have been tuned on an ad hoc individual basis. It is time for change. While hundreds of papers on scheduling are published every year, it becomes increasingly difficult to easily identify scheduling problems and solutions. We are not aware of any general taxonomy to define job scheduling problems and solutions in distributed systems. This paper aims at shedding light on this scenario by defining such a R. Lopes is with the Departmento de Sistemas e Computac¸a˜o, Universi- dade Federal de Campina Grande, Paraiba, Brazil. E-mail: raquel@dsc.ufcg.edu.br D. Menasce´ is with Department of Computer Science, George Mason University, Fairfax, VA 22030. E-mail: menasce@gmu.edu. Manuscript received September 00, 2015 taxonomy and classifiying a great deal of papers through the use of this taxonomy. Early seminal work aimed at defining taxonomies to classify scheduling problems and solutions exist. An impor- tant work defines a taxonomy for distributed job scheduling solutions [5]. Another inspiring work defines a language to specify scheduling problems [6]. In spite of the inspiring nature of these seminal propositions, a general taxonomy that takes into account the new generation of distributed systems and scheduling problems and solutions is required. More recently, some researchers have defined tax- onomies for specific types of distributed platforms. How- ever, none try to cover a distributed system in general, as we argue is the most appropriate solution. The authors of [7] define a taxonomy of scheduling problems in grid computing platforms. Smanchat and Viriyapant [8] extend the grid taxonomy to define a taxonomy of scheduling problems in cloud computing. These taxonomies overlap in some aspects, especially those describing workload and solution, and at the same time, they are over-fitting models, not general to be applied to any kind of distributed platform known today. They consider properties that represent very specific details of each resource platform. For example, the grid taxonomy [7] only considers scheduling problems that target multi-criteria decision analysis involving cost. This excludes many scheduling problems in which cost is not considered or in which the scheduling goal considers one criterion, like minimization of makespan, that is historically the most popular scheduling goal. Some properties are highly coupled with grid environments such as the cost model flexibility, and intra and interdependence among scheduling criteria. The taxonomies of workflow scheduling techniques in the cloud assume that resources are virtual machines, which is not true for all distributed platforms, even for the cloud1. Some properties of the cloud taxonomy 1. Metal as a Service has recently arisen as a new model in which the cloud user deploys directly onto bare metal for optimum per- formance. OpenStack, for instance, is considering this new model (https://wiki.openstack.org/wiki/Ironic). are highly coupled with traditional cloud environments, such as VM startup latency and provisioning model (on- demand, reservation or spot). It is also important to point out that the taxonomies mentioned above fail to consider some properties that are important to clearly define scheduling problems and solu- tions. For instance, they do not define workload compo- sition in a complete fashion, neither resource sharing or scaling. They also do not consider important requirements such as data locality and failure model. Finally, they do not include properties that characterize the quality of service required by the workload. We argue that these and other features must be considered. We conclude that prior work in scheduling taxonomies is not generic or complete enough for classifying scheduling problems and solutions in distributed platforms. They either focus on specific resource categories and not distributed resources in general. We argue that a unified taxonomy is possible and, in fact, needed, in opposition to many specific overlapping taxonomies for each type of distributed platform. Moreover, hybrid infrastructures are increasingly common, in which different cloud or grid computing infras- tructures inter-operate [9], [10]; cases that can be modeled by a unified taxonomy. Finally and most importantly, it is easier to maintain a single taxonomy over the years than to maintain many different, overlapping ones. For these reasons, we have defined our own taxonomy to classify existing (and future) scheduling problems and solutions. The taxonomy targets the scheduling of jobs in distributed systems. The solution is clearly meaningless without the associated problem. The problem, however, can be useful alone for comparison reasons. So, we organize the taxonomy in such a way that the problem and the solution can be easily separated. We propose the use of the taxonomy to (i) instantiate different scheduling problems and (ii) classify different scheduling solutions. The contributions of this paper are four-fold. First, a comprehensive taxonomy for classifying scheduling prob- lems and solutions is defined. This taxonomy allows a researcher to define what is claimed, i.e., which portion of the scheduling problem space is being addressed and to define the properties of the scheduling solution in a com- prehensible fashion. This taxonomy provides a snapshot of the state-of-the-art of job scheduling in distributed systems. Second, we perform an analysis of the impact of a subset of 1058 papers related to job scheduling in distributed systems from 2005 to 2015 (May, 1st). Third, we apply the taxonomy to classify 109 scheduling problems and solutions published in the top-102 papers in the area, considering the number of citations per year. Finally, we publish an online scheduling archive, collaboratively constructed, in which classified scheduling problems and solutions may be found and others may be added. We found that almost 22% of the papers related to job scheduling in distributed systems are never cited; 12% of the papers in the area are responsible for 66% of all citations, and 40% of the papers are cited at most twice in their entire life. This is a sad indication that we are still crawling towards a real scientific methodology. We hope that by classifying the papers using a well-known taxonomy, researchers will be able to clearly indicate what kinds of problems and solutions they are claiming. As a consequence, the classification will allow new research to be built on top of the prior art and it will be easier to know the state-of-the- art regarding specific instantiations of scheduling problems. Richard Hamming detected a central problem of Com- puter Science during his Turning Award Lecture: Perhaps the central problem we face in all of com- puter science is how we are to get to the situation where we build on top of the work of others rather than redoing so much of it in a trivially different way. Science is supposed to be cumulative, not almost endless duplication of the same kind of things. [11] We believe that building an adequate taxonomy consti- tutes a first step towards the direction pointed by Ham- ming. Without proper mechanisms to classify work we are doomed to ignore what others have done. Other steps are still necessary. In particular, the discipline to use the taxonomy from now on and to maintain it up-to-date. An important action in this regard is to maintain an archive of scheduling problems and solutions based on the taxonomies. For that purpose, we created a web site, the DSS Archive (Distributed Systems Scheduling)2. We initially populated the site with the classification of 109 problems and their solutions. The idea is to collaboratively increase the number of papers cataloged. The site offers a form to fa- cilitate the inclusion of new scheduling problems/solutions in the archive. Researchers can download the data set with all the problems and solutions classified so far and then ma- nipulate the data using their statistical tools of preference3. The rest of this paper is organized as follows. Section 2 presents a background on scheduling theory and defines a scheduling problem. Section 3 introduces a taxonomy for scheduling in distributed systems that contemplates problems and solutions. Section 4 summarizes the research method and underlying review protocol, which was used to collect 1058 papers published in the last decade on job scheduling in distributed systems. The next section presents statistics about these papers including popularity and re- source categories considered. The taxonomy was used to classify 109 scheduling problems and respective solutions. The results are summarized in Section 6. Related work is discussed in Section 7. Section 8 concludes with recommen- dations for future research on the topic. 2 BACKGROUND ON SCHEDULING THEORY This section provides a conceptual model of scheduling problems and solutions in distributed computer systems. Some definitions in this section are based on previous work [2], [12]. We do not consider in this paper single- node scheduling problems, which have been thoroughly investigated in the field of operating systems. Scheduling is the assignment of resources to consumers in time. In general, every instance of a scheduling problem must clearly specify three components: • Workload, defines the consumers of the resources. In the context of this paper a workload is composed of 2. http://lsd.ufcg.edu.br/˜dssarchive 3. We provide R scripts to facilitate data manipulation. jobs, defined as a collection of computational tasks. Thus, a job j has nj tasks Tj, . . . , Tj . 1 nj • Resources, required to execute the workload, consist of a set of distributed nodes or computers, with one or more processing cores, connected by a, typically high- speed, network. These resources may be organized in computing clusters in a local environment or in widely distributed and scalable data centers [13]. Resources are assumed to be able to execute any type of computational task and consist of whole computing units, with main memory, storage devices and network access. We assume that nodes can only communicate through message exchange. • Scheduling requirements determine the scheduling goal and other requirements that must be met by the solution. Typically, the scheduling goal is to optimize one or a combination of performance metrics affected by scheduling decisions. Another important schedul- ing requirement is the scheduling level. It determines the granularity or the level of detail considered when making a scheduling decision. We consider two lev- els of scheduling decisions: job and task4. Scheduling is typically a dynamic activity: workload and resources may vary over time. In order to model these dy- namic aspects, we consider R+ to denote the set of time instants of interest, which may be discrete or continuous. At any time t the workload is composed by a set t of jobs. At any time t the resources consist of a set t of resources. Nevertheless, there are static properties of the workload and/or resources that do not change over time and are the core of our taxonomy. Let and represent the static aspects of the workload and resources respectively. Let be the set of scheduling requirements that must be satisfied. We define a scheduling problem as a tuple ( , , ). A scheduling solution is associated with a given scheduling problem. There may be more than one solution to the same problem. 3 SCHEDULING TAXONOMY IN DISTRIBUTED SYS- TEMS The proposed taxonomy is organized into two parts: one characterizes a scheduling problem and another a schedul- ing solution. The problem part (see Figure 1) consists of 17 static features that fall into three groups: workload (W), resources (R), and requirements (Q). 3.1 Workload description Seven features characterize the workload . 1 - Job source. Defines if jobs come from multiple users single user and if the workload consists of multiple-jobs or a single-job. Reasonable combinations are: single user/single- job, single-user/multi-job and multi-user/multi-job. When the workload comes from many users, scheduling is often per- formed from the provider standpoint. 4. Each task consists of one or more (lightweight) processes that must be scheduled at the computing node assigned to run the task. This constitutes a third level of scheduling, i.e., process-level, typically managed by the operating system. This level of scheduling is outside the scope of this paper. Fig. 1. Summary of static features related to a scheduling problem. 2 - Job structure. Defines the allowed number of tasks per job and the dependency relations and communication needs among the tasks. First, this feature defines if jobs are multi- or single-task. For multi-task jobs, one has to determine the task homogeneity. Tasks are homogeneous when they require similar resource demands and are hetero- geneous otherwise. The tasks of a job may have precedence constraints and communication needs to be satisfied, in which case they are dependent. Dependency between tasks often brings to the scheduling problem the challenge of data locality, since data transfers come at a cost. When there are neither precedence relations among the tasks nor communication needs, tasks are independent. Based on this discussion, the job structure may be: single-task, independent homogeneous multi-task, independent heterogeneous multi-task, dependent homogeneous multi-task or dependent heterogeneous multi-task. The trivial case of a single-job and single-task workload is not interesting and is not considered here. 3 - Job flexibility. Rigid jobs require a fixed quantity of resources and cannot execute on fewer or more resources. This quantity is defined by the user at job submission time. Other classes of jobs exist [4]: moldable, malleable and evolving. When a moldable job is submitted, some entity, possibly a scheduler, decides on the quantity of resources to provide the job. This quantity cannot be reconfigured during the job execution. Malleable jobs are moldable jobs whose computing requirements can change during execution by the scheduler or other system entity. Finally, evolving jobs are similar to malleable jobs, but the user decides, on the fly, about the quantity of resources to assign to the job. 4 - Arrival process. Determines the set of jobs consid- ered by the scheduler when making scheduling decisions. In an open workload model, jobs come to the system at any time and leave the system after being executed, i.e., the number of jobs in the system is not constant. In a closed workload, the number of jobs to be scheduled is fixed. 5 - Workload COMPOSITION. This feature is determined by the programming model, which drives the kinds of relation- ships that must hold between the tasks of a job. Some exam- ples include bags of tasks, in which all tasks are independent from one another, and MapReduce jobs, in which all map tasks must finish before the reduce tasks start execution. A workload may be formed by jobs that follow the same programming model or may be heterogeneous. A workload that consists of jobs of the same programming model may be classified as: same model/homogeneous, when jobs are similar in terms of structure, number of tasks and in terms of demands required; same model/same structure, when jobs are similar in terms of structure, number of tasks but differ in terms of demands required; or same model/diverse, when jobs use the same programming model but have different struc- ture, number of tasks, and resource demands. Dependence relations and communication patterns do not exist if jobs are single-task. As a consequence, when the workload consists of multiple single-task jobs, the workload composition must be same model/homogeneous or same model/same structure. 6 - Quality of service. Jobs may be associated to service level agreements (SLAs). Penalties may be imposed when SLAs are violated. These jobs are SLO aware, since they require service level objectives (SLOs) to be met. Jobs that are not associated to SLAs are considered best effort jobs. 7 - Real TIME. The workload may consist of real time jobs or non real time jobs. For the former case, we distinguish between real time jobs with hard deadlines and soft deadlines. We also consider whether tasks are periodic or aperiodic. A hard or soft real time workload is necessarily SLO aware. 3.2 Resource description We identified five features that characterize the resources. 1 - Resource heterogeneity. Homogeneous resource plat- forms consist of similar nodes in terms of processing power, storage, and networking capabilities. Heterogeneous resource platforms consist of nodes with different computing powers, in terms of processing, storage, or communication speeds. 2 - Resource scaling. The scheduler can see the re- sources it can use as a fixed or dynamic infrastructure in terms of processing capacity. Some infrastructures allow rapid capacity changes in response to variations in the work- load. The total capacity of a fixed-capacity resource platform does not vary in the short term. On the other hand, some distributed systems allow dynamic scaling. Three common situations lead to dynamically scalable infrastructures: (i) shutdown resources, when some nodes are turned off to save energy, temporarily reducing the online capacity of the infrastructure. The total capacity is rapidly restored by turning on the machines; (ii) outsourcing, when it is possible to rapidly acquire resources from other resource providers, such as infrastructure as a service (IaaS) providers or grid peers; (iii) DVFS, when Dynamic Voltage and Frequen

Englisch

A Taxonomy of Job Scheduling on Distributed Computing Systems Raquel V. Lopes, Member, IEEE, and Daniel Menasce´, Fellow, IEEE Abstract—Hundreds of papers on job scheduling for distributed systems are published every year and it becomes increasingly difficult to classify them. Our analysis revealed that half of these papers are barely cited. This paper presents a general taxonomy for scheduling problems and solutions in distributed systems. This taxonomy was used to classify and make publicly available the classification of 109 scheduling problems and their solutions. These 109 problems were further clustered into ten groups based on the features of the taxonomy. The proposed taxonomy will facilitate researchers to build on prior art, increase new research visibility, and minimize redundant effort. Index Terms—Taxonomy, scheduling, distributed jobs, cluster, grid computing, cloud computing. ◆ 1 INTRODUCTION N the last decade, cluster computing emerged as the main platform for high performance, grid, and cloud computing. Together, these three different, yet very simi- lar platforms, emerged as important sources of computing power. They all consist of distributed computers (or nodes) connected through high speed networks. Most of the scheduling problems are computationally hard [1], [2], [3], and they have been attracting the attention of researchers for decades. Thousands of solutions have been published, dealing with slightly different versions of a scheduling problem. Indeed, there are many knobs that may be tuned in order to clearly specify a scheduling problem of this nature. To the best of our knowledge, these knobs have not been defined for general scheduling problems, leading an important researcher to clamor for the need of a proper definition of scheduling problems: At the very minimum, we wish that all papers about job schedulers, either real or paper design, make clear their assumptions about the workload, the permissible actions allowed by the system, and the metric that is being optimized. [4] Twenty years later, the situation has not improved. So far, the many knobs needed to define a scheduling problem have been tuned on an ad hoc individual basis. It is time for change. While hundreds of papers on scheduling are published every year, it becomes increasingly difficult to easily identify scheduling problems and solutions. We are not aware of any general taxonomy to define job scheduling problems and solutions in distributed systems. This paper aims at shedding light on this scenario by defining such a R. Lopes is with the Departmento de Sistemas e Computac¸a˜o, Universi- dade Federal de Campina Grande, Paraiba, Brazil. E-mail: raquel@dsc.ufcg.edu.br D. Menasce´ is with Department of Computer Science, George Mason University, Fairfax, VA 22030. E-mail: menasce@gmu.edu. Manuscript received September 00, 2015 taxonomy and classifiying a great deal of papers through the use of this taxonomy. Early seminal work aimed at defining taxonomies to classify scheduling problems and solutions exist. An impor- tant work defines a taxonomy for distributed job scheduling solutions [5]. Another inspiring work defines a language to specify scheduling problems [6]. In spite of the inspiring nature of these seminal propositions, a general taxonomy that takes into account the new generation of distributed systems and scheduling problems and solutions is required. More recently, some researchers have defined tax- onomies for specific types of distributed platforms. How- ever, none try to cover a distributed system in general, as we argue is the most appropriate solution. The authors of [7] define a taxonomy of scheduling problems in grid computing platforms. Smanchat and Viriyapant [8] extend the grid taxonomy to define a taxonomy of scheduling problems in cloud computing. These taxonomies overlap in some aspects, especially those describing workload and solution, and at the same time, they are over-fitting models, not general to be applied to any kind of distributed platform known today. They consider properties that represent very specific details of each resource platform. For example, the grid taxonomy [7] only considers scheduling problems that target multi-criteria decision analysis involving cost. This excludes many scheduling problems in which cost is not considered or in which the scheduling goal considers one criterion, like minimization of makespan, that is historically the most popular scheduling goal. Some properties are highly coupled with grid environments such as the cost model flexibility, and intra and interdependence among scheduling criteria. The taxonomies of workflow scheduling techniques in the cloud assume that resources are virtual machines, which is not true for all distributed platforms, even for the cloud1. Some properties of the cloud taxonomy 1. Metal as a Service has recently arisen as a new model in which the cloud user deploys directly onto bare metal for optimum per- formance. OpenStack, for instance, is considering this new model (https://wiki.openstack.org/wiki/Ironic). are highly coupled with traditional cloud environments, such as VM startup latency and provisioning model (on- demand, reservation or spot). It is also important to point out that the taxonomies mentioned above fail to consider some properties that are important to clearly define scheduling problems and solu- tions. For instance, they do not define workload compo- sition in a complete fashion, neither resource sharing or scaling. They also do not consider important requirements such as data locality and failure model. Finally, they do not include properties that characterize the quality of service required by the workload. We argue that these and other features must be considered. We conclude that prior work in scheduling taxonomies is not generic or complete enough for classifying scheduling problems and solutions in distributed platforms. They either focus on specific resource categories and not distributed resources in general. We argue that a unified taxonomy is possible and, in fact, needed, in opposition to many specific overlapping taxonomies for each type of distributed platform. Moreover, hybrid infrastructures are increasingly common, in which different cloud or grid computing infras- tructures inter-operate [9], [10]; cases that can be modeled by a unified taxonomy. Finally and most importantly, it is easier to maintain a single taxonomy over the years than to maintain many different, overlapping ones. For these reasons, we have defined our own taxonomy to classify existing (and future) scheduling problems and solutions. The taxonomy targets the scheduling of jobs in distributed systems. The solution is clearly meaningless without the associated problem. The problem, however, can be useful alone for comparison reasons. So, we organize the taxonomy in such a way that the problem and the solution can be easily separated. We propose the use of the taxonomy to (i) instantiate different scheduling problems and (ii) classify different scheduling solutions. The contributions of this paper are four-fold. First, a comprehensive taxonomy for classifying scheduling prob- lems and solutions is defined. This taxonomy allows a researcher to define what is claimed, i.e., which portion of the scheduling problem space is being addressed and to define the properties of the scheduling solution in a com- prehensible fashion. This taxonomy provides a snapshot of the state-of-the-art of job scheduling in distributed systems. Second, we perform an analysis of the impact of a subset of 1058 papers related to job scheduling in distributed systems from 2005 to 2015 (May, 1st). Third, we apply the taxonomy to classify 109 scheduling problems and solutions published in the top-102 papers in the area, considering the number of citations per year. Finally, we publish an online scheduling archive, collaboratively constructed, in which classified scheduling problems and solutions may be found and others may be added. We found that almost 22% of the papers related to job scheduling in distributed systems are never cited; 12% of the papers in the area are responsible for 66% of all citations, and 40% of the papers are cited at most twice in their entire life. This is a sad indication that we are still crawling towards a real scientific methodology. We hope that by classifying the papers using a well-known taxonomy, researchers will be able to clearly indicate what kinds of problems and solutions they are claiming. As a consequence, the classification will allow new research to be built on top of the prior art and it will be easier to know the state-of-the- art regarding specific instantiations of scheduling problems. Richard Hamming detected a central problem of Com- puter Science during his Turning Award Lecture: Perhaps the central problem we face in all of com- puter science is how we are to get to the situation where we build on top of the work of others rather than redoing so much of it in a trivially different way. Science is supposed to be cumulative, not almost endless duplication of the same kind of things. [11] We believe that building an adequate taxonomy consti- tutes a first step towards the direction pointed by Ham- ming. Without proper mechanisms to classify work we are doomed to ignore what others have done. Other steps are still necessary. In particular, the discipline to use the taxonomy from now on and to maintain it up-to-date. An important action in this regard is to maintain an archive of scheduling problems and solutions based on the taxonomies. For that purpose, we created a web site, the DSS Archive (Distributed Systems Scheduling)2. We initially populated the site with the classification of 109 problems and their solutions. The idea is to collaboratively increase the number of papers cataloged. The site offers a form to fa- cilitate the inclusion of new scheduling problems/solutions in the archive. Researchers can download the data set with all the problems and solutions classified so far and then ma- nipulate the data using their statistical tools of preference3. The rest of this paper is organized as follows. Section 2 presents a background on scheduling theory and defines a scheduling problem. Section 3 introduces a taxonomy for scheduling in distributed systems that contemplates problems and solutions. Section 4 summarizes the research method and underlying review protocol, which was used to collect 1058 papers published in the last decade on job scheduling in distributed systems. The next section presents statistics about these papers including popularity and re- source categories considered. The taxonomy was used to classify 109 scheduling problems and respective solutions. The results are summarized in Section 6. Related work is discussed in Section 7. Section 8 concludes with recommen- dations for future research on the topic. 2 BACKGROUND ON SCHEDULING THEORY This section provides a conceptual model of scheduling problems and solutions in distributed computer systems. Some definitions in this section are based on previous work [2], [12]. We do not consider in this paper single- node scheduling problems, which have been thoroughly investigated in the field of operating systems. Scheduling is the assignment of resources to consumers in time. In general, every instance of a scheduling problem must clearly specify three components: • Workload, defines the consumers of the resources. In the context of this paper a workload is composed of 2. http://lsd.ufcg.edu.br/˜dssarchive 3. We provide R scripts to facilitate data manipulation. jobs, defined as a collection of computational tasks. Thus, a job j has nj tasks Tj, . . . , Tj . 1 nj • Resources, required to execute the workload, consist of a set of distributed nodes or computers, with one or more processing cores, connected by a, typically high- speed, network. These resources may be organized in computing clusters in a local environment or in widely distributed and scalable data centers [13]. Resources are assumed to be able to execute any type of computational task and consist of whole computing units, with main memory, storage devices and network access. We assume that nodes can only communicate through message exchange. • Scheduling requirements determine the scheduling goal and other requirements that must be met by the solution. Typically, the scheduling goal is to optimize one or a combination of performance metrics affected by scheduling decisions. Another important schedul- ing requirement is the scheduling level. It determines the granularity or the level of detail considered when making a scheduling decision. We consider two lev- els of scheduling decisions: job and task4. Scheduling is typically a dynamic activity: workload and resources may vary over time. In order to model these dy- namic aspects, we consider R+ to denote the set of time instants of interest, which may be discrete or continuous. At any time t the workload is composed by a set t of jobs. At any time t the resources consist of a set t of resources. Nevertheless, there are static properties of the workload and/or resources that do not change over time and are the core of our taxonomy. Let and represent the static aspects of the workload and resources respectively. Let be the set of scheduling requirements that must be satisfied. We define a scheduling problem as a tuple ( , , ). A scheduling solution is associated with a given scheduling problem. There may be more than one solution to the same problem. 3 SCHEDULING TAXONOMY IN DISTRIBUTED SYS- TEMS The proposed taxonomy is organized into two parts: one characterizes a scheduling problem and another a schedul- ing solution. The problem part (see Figure 1) consists of 17 static features that fall into three groups: workload (W), resources (R), and requirements (Q). 3.1 Workload description Seven features characterize the workload . 1 - Job source. Defines if jobs come from multiple users single user and if the workload consists of multiple-jobs or a single-job. Reasonable combinations are: single user/single- job, single-user/multi-job and multi-user/multi-job. When the workload comes from many users, scheduling is often per- formed from the provider standpoint. 4. Each task consists of one or more (lightweight) processes that must be scheduled at the computing node assigned to run the task. This constitutes a third level of scheduling, i.e., process-level, typically managed by the operating system. This level of scheduling is outside the scope of this paper. Fig. 1. Summary of static features related to a scheduling problem. 2 - Job structure. Defines the allowed number of tasks per job and the dependency relations and communication needs among the tasks. First, this feature defines if jobs are multi- or single-task. For multi-task jobs, one has to determine the task homogeneity. Tasks are homogeneous when they require similar resource demands and are hetero- geneous otherwise. The tasks of a job may have precedence constraints and communication needs to be satisfied, in which case they are dependent. Dependency between tasks often brings to the scheduling problem the challenge of data locality, since data transfers come at a cost. When there are neither precedence relations among the tasks nor communication needs, tasks are independent. Based on this discussion, the job structure may be: single-task, independent homogeneous multi-task, independent heterogeneous multi-task, dependent homogeneous multi-task or dependent heterogeneous multi-task. The trivial case of a single-job and single-task workload is not interesting and is not considered here. 3 - Job flexibility. Rigid jobs require a fixed quantity of resources and cannot execute on fewer or more resources. This quantity is defined by the user at job submission time. Other classes of jobs exist [4]: moldable, malleable and evolving. When a moldable job is submitted, some entity, possibly a scheduler, decides on the quantity of resources to provide the job. This quantity cannot be reconfigured during the job execution. Malleable jobs are moldable jobs whose computing requirements can change during execution by the scheduler or other system entity. Finally, evolving jobs are similar to malleable jobs, but the user decides, on the fly, about the quantity of resources to assign to the job. 4 - Arrival process. Determines the set of jobs consid- ered by the scheduler when making scheduling decisions. In an open workload model, jobs come to the system at any time and leave the system after being executed, i.e., the number of jobs in the system is not constant. In a closed workload, the number of jobs to be scheduled is fixed. 5 - Workload COMPOSITION. This feature is determined by the programming model, which drives the kinds of relation- ships that must hold between the tasks of a job. Some exam- ples include bags of tasks, in which all tasks are independent from one another, and MapReduce jobs, in which all map tasks must finish before the reduce tasks start execution. A workload may be formed by jobs that follow the same programming model or may be heterogeneous. A workload that consists of jobs of the same programming model may be classified as: same model/homogeneous, when jobs are similar in terms of structure, number of tasks and in terms of demands required; same model/same structure, when jobs are similar in terms of structure, number of tasks but differ in terms of demands required; or same model/diverse, when jobs use the same programming model but have different struc- ture, number of tasks, and resource demands. Dependence relations and communication patterns do not exist if jobs are single-task. As a consequence, when the workload consists of multiple single-task jobs, the workload composition must be same model/homogeneous or same model/same structure. 6 - Quality of service. Jobs may be associated to service level agreements (SLAs). Penalties may be imposed when SLAs are violated. These jobs are SLO aware, since they require service level objectives (SLOs) to be met. Jobs that are not associated to SLAs are considered best effort jobs. 7 - Real TIME. The workload may consist of real time jobs or non real time jobs. For the former case, we distinguish between real time jobs with hard deadlines and soft deadlines. We also consider whether tasks are periodic or aperiodic. A hard or soft real time workload is necessarily SLO aware. 3.2 Resource description We identified five features that characterize the resources. 1 - Resource heterogeneity. Homogeneous resource plat- forms consist of similar nodes in terms of processing power, storage, and networking capabilities. Heterogeneous resource platforms consist of nodes with different computing powers, in terms of processing, storage, or communication speeds. 2 - Resource scaling. The scheduler can see the re- sources it can use as a fixed or dynamic infrastructure in terms of processing capacity. Some infrastructures allow rapid capacity changes in response to variations in the work- load. The total capacity of a fixed-capacity resource platform does not vary in the short term. On the other hand, some distributed systems allow dynamic scaling. Three common situations lead to dynamically scalable infrastructures: (i) shutdown resources, when some nodes are turned off to save energy, temporarily reducing the online capacity of the infrastructure. The total capacity is rapidly restored by turning on the machines; (ii) outsourcing, when it is possible to rapidly acquire resources from other resource providers, such as infrastructure as a service (IaaS) providers or grid peers; (iii) DVFS, when Dynamic Voltage and Frequen

Letzte Aktualisierung: 2019-05-14
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Contents محتویات[ edit ] Julian Date [ ویرایش ] تاریخ میلادیHistorical Julian dates were recorded relative to GMT or Ephemeris Time , but the International Astronomical Union now recommends that Julian Dates be specified in Terrestrial Time , and that when necessary to specify Julian Dates using a different time scale, that the time scale used be indicated when required, such as JD(UT1).

Englisch

== Time scales ==Historical Julian dates were recorded relative to GMT or Ephemeris Time, but the International Astronomical Union now recommends that Julian dates be specified in Terrestrial Time, and that when necessary to specify Julian dates using a different time scale, that the time scale used be indicated when required, such as JD(UT1).

Letzte Aktualisierung: 2016-03-03
Nutzungshäufigkeit: 1
Qualität:

Referenz: Anonym

Persisch

Media Airtime Buying Agreement THIS AGREEMENT MADE THIS ___Day of ______ 2014 BY and BETWEEN KaPUL Group of Companies, having its registered office at House No. 15, Street 11, Taimani, Kabul, Afghanistan, hereafter called (“the Agency”), represented by the CEO of the KaPUL Group, Nazifullah Shaheen And One Group, having its registered office at House No.---------------------------------------------------------, Kabul, Afghanistan, hereafter called (“the Broadcaster”), represented by the CEO of the Group One, ---------------------------- WHEREAS: KaPUL Group and One Group are desirous of entering into a commercial relationship. The Agency is engaged in the business of providing brand communication advisory services and solutions, planning and buying media spots for advertising and related activities to various clients in Afghanistan, Pakistan and other countries including but not limited to release of advertising material and commercial administration and processes. One Group, is engaged in the business of broadcast operations through its TV channel, 1TV in Afghanistan. KaPUL group and One Group have agreed to enter into an agreement for advertising on the Television network, 1TV of One Group The parties to this Agreement shall be individually referred to as AGENCY and BRAODCASTER respectively and shall be referred to as the “Parties” collectively. AGENCY has agreed to place and BROADCASTER has agreed to telecast advertisements of the brands and products of various advertisers (Clients of the AGENCY) on the negotiated rates, terms and conditions as set out below. AGENCY conveys and BROADCASTER appreciates that timely and regular telecasting of the advertisements as per the schedule released by AGENCY is critical to the success of the brands and business of the advertisers. BROADCASTER conveys and AGENCY acknowledges that timely and regular release of advertising materials, release orders and payments to the BROADCASTER are critical to the success of the business of the BROADCASTER. The parties agree to the terms and conditions as set out below : 1. The Parties agree to the general terms and conditions to the agreement attached in Annexure 1. 2. The Parties agree to the Rates, Entitlements and other commercial terms as set out below and Sponsorships and Other entitlements, if any. 3. The Terms and conditions and the Rates and Entitlements will prevail during the term of this agreement . 4. The Parties hereto shall not be entitled to unilaterally change, alter or modify any terms and conditions and/or rates and/or entitlements. Any revision or amendment or modification shall be mutually discussed and agreed by all the Parties in writing. 5. This Agreement represents the entire understanding between the Parties and supersedes any and all previous discussions, correspondence, understandings and communications (whether written or oral) between the Parties with respect to the subject matter hereof. This Agreement may not be amended, supplemented or otherwise modified, except by an instrument in writing signed by all the Parties. IN WITNESS WHEREOF the parties hereto have hereunto executed these presents at the hands through their duly authorized representatives on the day and year first herein above written. ANNEXURE 1 GENERAL TERMS AND CONDITIONS 1 DEFINITIONS The following words and expressions shall, unless the context otherwise requires, have the following meanings: i. “Agency” shall have meaning assigned to it as per the relevant clause of the Principal terms of the agreement between the parties. ii. “Broadcaster” shall have meaning assigned to it as per the relevant clause of the Principal terms of the agreement between the parties. iii. “Channels” shall mean the specific TV channel which is broadcasted by the BROADCASTER and on which the AGENCY desires to advertise the products, services and/or brands of its clients. iv. “Commercials” shall mean the advertisements(s) on the time slot of the Channel covered by this agreement. v. “Spots” shall mean time or program slots for advertisements on the channels occupying the full TV Screen vi. “Other Advertising slots” shall mean time or program slots for advertisements occupying part of TV screen such as scrollers, PIP, logo placement, Clients’ product placement in the programs, web banners on Channels website etc as define and agreed between Agency and Broadcaster from time to time. vii. “Delivery Material" shall mean one DVDs or Digital Betacam cassette or the medium of the advertisement of technical specifications as provided by BROADCASTER and as amended from time to time. viii. “Advertising Commercials Airtime” shall mean the actual duration and times for which Advertisements are aired. ix. “Rates” shall mean the rates and prices as agreed and contained in Clause 3 of the principal Terms of the agreement. x. "Entitlements" shall have meaning assigned to it as per the relevant clause 3 of the Principal terms of the agreement between the parties. xi. “Sponsorships” shall have meaning assigned to it as per the relevant clause of the Principal terms of the agreement between the parties. xii. “Product(s)” shall mean the products and/or services manufactured and/or provided and/or marketed by the Clients of the Agency. xiii. “Advertisement Commercials” shall mean the advertisement on the agreed time slots or programs on the Channels as provided in the Agreement. xiv. “Release/Traffic Order” shall mean the document providing details of advertisements to be aired such as time /slot, duration, caption, brand name, Rate etc. 2 BOOKING & SCHEDULING The AGENCY shall book advertisements of various advertisers for their products/brands and shall advise such booking through a Release Order to be sent in writing to BROADCASTER. The BROADCASTER shall telecast the advertisements booked by AGENCY as per the activity schedule desired by the clients of the AGENCY through the Release/Traffic order. In the exceptional event that BROADCASTER is not able to carry the spots on a particular slot or as per the Release order, BROACASTER shall intimate AGENCY within 4 working hours of receiving the Release Orders. All such spots which are not carried by BROADCASTER shall be made good 24 hours of such non-carriage in the same time slot as the original spot so missed or not carried after taking prior approval from AGENCY. Any alternate schedule shall be carried by the BROADCASTER only with the prior approval of the AGENCY. The AGENCY may reschedule the spots with a minimum of 1 day notice and the BROADCASTER shall, at its sole discretion, agree to Rescheduling. Once the BROADCASTER has agreed to the Rescheduling the revised schedule shall be deemed as the original schedule. The AGENCY undertakes and warrants that the spots booked for Advertising Commercials for the brands and products shall be of duration in multiple of 5 [five] seconds. The BROADCASTER shall, at its sole discretion, accept Commercials shorter than 5 [five] seconds duration for telecast. The BROADCASTER may, at its sole discretion, refuse to carry any advertisement or commercial that is found to be violating any laws in force at the relevant time. The AGENCY may, at its sole discretion, cancel its Advertising Commercials spots on a particular slot or across all slots if any of the programs aired by the BROADCASTER is found to be violating any laws in force at the relevant time or any actions by the BROADCASTER which tantamount to any Unfair / Monopolistic or Restrictive Trade Practice. The AGENCY and the BROADCASTER agree that spots will run as per the Release/Traffic Order and the airing will be verified by the 3rd Party Media Tracking and Monitoring Company. 3 CONSIDERATION The consideration for Advertising Commercials air-time and the other entitlements agreed by the Parties shall be binding during the term of the agreement, unless otherwise expressively agreed in writing between the AGENCY and the BROADCASTER. BROADCASTER agrees to make good the pro-rata value in the event of significant distribution reduction due to either blocking of advertisements or programmes by Cable Operator/s or power failures. All Rates and prices covered by this agreement are inclusive of 2.5% agency or Credit Notes for Free Minutes (in prime and non-prime time) for the AGENCY. AGENCY can utilize the Free Minutes, for any of its Clients, in the same contract year or the following year depending upon the understanding between the AGENCY and the BROADCASTER. The Contract is for one year i.e. from 15th January 2014 to 14th January 2015. The Agency plans to spend from US $150,000/ to US $ 175,000/ with the BROADCASTER during the period of Contract for running the advertising campaigns of its various national and international clients. The minimum committed annual spend is US $ 150,000/. The BROADCASTER has offered following airtime, entitlements and Sponsorships to the AGENCY against the commitment of annual minimum US $ 150,000/ to the range of US $175,000 spending: Commercial Airtime Prime time: Non-prime Time: Sponsorships Promos Scrolls Web banners Logo on Screen Free Minutes Prime time: Non-prime Time: AGENCY agrees to fulfil the value commitment under this agreement in entirety within the term/period, failing which the ADVERTISER shall be liable to pay the BROADCASTER 5% of the unfulfilled portion of the commitment without any commensurate utilization of air-time/spots/entitlements. 4 INVOICING The BROADCASTER shall raise an invoice, ADVERTISER/BRAND wise, in the name of the AGENCY in respect of the Advertising Commercials air-time and/or Entitlements utilised by the AGENCY at the end of every calendar month or at any other frequency agreed in the Principal terms of the Agreement. The invoices so raised will be based on Rates and Prices agreed between the BROADCASTER and AGENCY in writing. The same agreed Rates will be applicable to all the ADVERTISERS (Clients of the AGENCY). For invoices to be considered valid for payment, the invoices shall contain all relevant details viz. Company name, Brand name, Commercial caption, Duration of the commercial, Advertising Commercials rate, Payable amount, Date and Time of airing, Agency commission and Taxes. Invoices are NOT the proof of telecast of spots and BROADCASTER will provide proof of telecast of spots as per Release/Traffic Order. Upon request of the AGENCY, the BROADCASTER will provide a summary of the spots aired on weekly basis. The AGENCY and the CLIENTS of AGENCY reserves the complete right to get the airing of spots and other entitlements checked through independent media tracking and monitoring company. The AGENCY will cross check the airing report of the BROADCASTER (attached with the invoice) with the media tracking and monitoring report of independent company for that particular duration. In case of any dispute, discrepancy and/or deviation, the AGENCY will inform the BROADCASTER and the Parties shall mutually resolve such disputes regarding non-airing through a reconciliation of the two information sets and mutual discussion. After reconciliation of such droppages, deviations etc, the BROADCASTER will bring the changes in invoice as payment will be made for those spots only which has been aired as per Release/Traffic Order. Invoices will be sent by the BROADCASTER no later than 15 days after the end of the calendar month. In case of incorrect and/or incomplete invoices the receipt date will be taken as the date on which the corrected/revised invoices are submitted and acknowledged by the ADVERTISER as being factually complete and correct in all respects. Any discrepancies in the Invoice shall be brought to the notice of BROADCASTER by the ADVERTISER within thirty days of receipt of the invoice, in writing. On the expiry of the said period no such request shall be entertained by the BROADCASTER. The BROADCASTER shall raise a Revised Invoice or supplementary Invoice or Credit note as the case may be to rectify any discrepancies in the original invoice. The credit period of 75 days shall commence from the date of such revised bill. 5 PAYMENT TERMS The final invoice received by the AGENCY will be paid within 80 days from the date of receiving the final/revised invoice. In case of disputed invoices (in part or whole), the entire invoice will remain pending until such time the dispute is resolved. The BROADCASTER and the AGENCY undertake to resolve disputes within 30 days of receipt of the invoice. In the event of non payment of the Invoices by the AGENCY, the BROADCASTER shall have the express, irrevocable right to withhold any future carriage of Advertising Commercials spots and any future Entitlements whether booked or not by the AGENCY. Any such an action on the part of the BROADCASTER shall not constitute breach of the agreement by the BROADCASTER. The BROADCASTER shall commence further activity on full settlement of the outstanding invoices or earlier at its sole discretion. The BROADCASTER shall not accept any payments made by cash. All payments to be made by Account payable cheques or demand drafts drawn in favour of ‘BROADCASTER’ or any other party nominated in writing by the BROADCASTER to receive payments on its behalf. Payments will be made by AGENCY to BROADCASTER for 97.5% of the Gross value of the invoices raised by BROADCASTER after deducting Agency Commission All payments will be made by AGENCY after deduction of tax at source as per applicable laws and regulations. AGENCY reserves the right to withhold payment in the event of a breach of any of the terms of this agreement by BROADCASTER. In the event of the breach of any of the terms of this agreement, AGENCY may at its sole discretion agree the waiver of such a breach and make payments for the period under which the breach was done by BROADCASTER. 6 WARRANTIES, OBLIGATIONS AND UNDERTAKING The Parties warrant and undertake that throughout the Term each of them has and will continue to have full authority to enter into this Agreement and to undertake each and all of the particular obligations on their respective parts contained herein. The AGENCY affirms that the contents of the advertisements provided to BROADCASTER for airing shall be in conformity with the laws prevailing in Afghanistan The BROADCASTER undertakes and the AGENCY consents that the BROADCASTER shall make recordings of the advertising material for archive in order to comply with the provisions of all applicable statutes and/or codes when required. The Parties shall in the fulfilment of their obligations comply with all applicable laws, byelaws and regulations of the Government and other concerned authorities. 7 TERMINATION This agreement is non-terminable unless agreed upon by the parties. Notwithstanding anything contained herein, either Party may terminate the Agreement with a notice of 30 days to the other party only in the following circumstances- (a) if the other party commits a breach of any term of this Agreement. (b) if an event of force majeure has lasted more than one month. 8 EFFECT OF TERMINATION In the event of termination due to Force Majeure, consideration due under the Agreement shall be payable subject to other provisions of the Agreement and shall be paid by the AGENCY within 100 days of termination having come into effect or on the payable date as per this agreement whichever is earlier. In the event of termination due to Force Majeure, all the bookings of the Advertising Commercials shall stand cancelled with effect from 1 day of the date of the notice and no payments shall be made by the AGENCY for any advertisements aired by the BROADCASTER beyond the 1 day. In the event of termination due to breach by either party, the Parties shall not be liable to fulfil any obligations under this agreement, including but not limited to payments, sponsorships or other obligations to be fulfilled under this agreement. 9 INTELLECTUAL PROPERTY AND TRADEMARKS BROADCASTER acknowledge that all the commercial and technical data, information, documentation made available by AGENCY is the intellectual property of ADVERTISERS (Clients of Agency) and further that the ADVERTISERS are the absolute owner/ registered user of all trademarks, trade names, copyright, designs, artistic works in the data, information, documentation and other work made available or communicated or provided by the AGENCY to the BROADCASTER. BROADCASTER shall not, at any time and under any circumstance: a. do anything which shall or may impair the right, title or interest of AGENCY & ADVERTISERs in its Intellectual Property or create any right, title or interest therein or thereto adverse to the interest of ADVERTISERS ; b. use or permit the Intellectual Property of ADVERTISERS to be used by any person; c. use the Intellectual Property of ADVERTISERS with any other mark or marks or any other marks unless for the purpose of specific and limited use allowed under this agreement for sponsorships or promotion activities; d. infringe, copy, initiate or otherwise interfere with the Intellectual Property Rights of ADVERTISERS or otherwise prejudice the same in any manner whatsoever AGENCY acknowledges that BROADCASTER is the trademark owners and copyright owners or licensees for the programs aired on the channels. The AGENCY shall not use or cause to be used the name and trademark of the programs, BROADCASTER, the Channels or any other Channels of BROADCASTER without prior written approval of BROADCASTER. In case BROADCASTER agrees to such use they shall have the sole right to specify the manner and the way in which the same shall be used by the AGENCY. BROADCASTER acknowledges and agrees that ADVERTISERS (Clients of Agency) are the trademark owners and the copyright owners of the advertisements aired either by way of advertising or sponsorship. ADVERTISERS hereby grants, through the AGENCY, to BROADCASTER the right to use advertiser’s logos and marks in the performance of its obligations under this Agreement and in BROADCASTERS ad sales marketing materials (eg: trade ad publications, promotions etc) and warrants that it has the authority to grant such rights. However, this limited licence is granted only for the purpose of fulfilling BROADCASTERS obligations under this agreement and for no other purpose and the licence shall automatically get terminated on termination of this arrangement. The BROADCASTER shall not use or cause to be used the name and trademark of the ADVERTISERS or any other names and trademarks owned or licenced by the ADVERTISER without prior written approval of ADVERTISERs though the AGENCY, unless for the performance of obligations under this agreement. In case ADVERTISER agrees to such use they shall have the sole right to specify the manner and the way in which the same shall be used by the BROADCASTER. 10 CONFIDENTIALITY The parties agree to keep the terms of this Agreement strictly confidential at all times. Except to the extent authorized by this Agreement and any requirement under law, during the term and following the expiration or termination of the agreement, the parties shall not disclose, publish or make available any proprietary information including but not limited to rates, time bands, costs etc. to any third party and shall not sell, transfer or otherwise use or exploit any such Proprietary Information disclosed to them. 11 INDEMNITY BROADCASTER owns the sole marketing rights and copyrights to all programs aired on the channels concerned and shall hold and continue to hold AGENCY fully indemnified without any limit against any claim, cost, expenses, damages, and /or penalty that ADVERTISERS (Clients of the AGENCY) may suffer on account of the program in which their Advertising Commercials are aired and/or sponsorships of any program on the chann

Englisch

Translation

Letzte Aktualisierung: 2014-04-03
Nutzungshäufigkeit: 1
Qualität:

Referenz: Wikipedia
Warnung: Enthält unsichtbare HTML-Formatierung

Eine bessere Übersetzung mit
4,401,923,520 menschlichen Beiträgen

Benutzer bitten jetzt um Hilfe:



Wir verwenden Cookies zur Verbesserung Ihrer Erfahrung. Wenn Sie den Besuch dieser Website fortsetzen, erklären Sie sich mit der Verwendung von Cookies einverstanden. Erfahren Sie mehr. OK