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Anglais

guideline .

Perse

راهبرد .

Dernière mise à jour : 2011-10-24
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Anglais

Show Putter & Guideline

Perse

نمایش & رهنمون گلف‌

Dernière mise à jour : 2011-10-23
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Anglais

please follow that guideline

Perse

لطفا از دستورات پیروی کنید

Dernière mise à jour : 2011-10-24
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Anglais

This is a comprehensive evidence-based guideline to address the management and prevention of overweight and obesity in adults and children.

Perse

این راهنما، یک راهنمای مستند گسترده برای دست یافتن به نحوه مدیریت و پیشگیری از اضافه و چاقی در بزرگسالان و کودکان می‌باشد.

Dernière mise à jour : 2016-03-03
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Anglais

There, they exchanged views with the head of IIO, Lu Wei, who presented the seven-point guideline.

Perse

در این برنامه فعالین به بحث و تبادل نظر با لو وی رئیس سازمان مربوطه که این دستورالعمل هفت موردی را ارائه کرد، پرداختند.

Dernière mise à jour : 2016-02-24
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Anglais

The new guideline, issued by the Supreme People's Court, defines the criteria for convicting and sentencing offenders.

Perse

این راهبرد که توسط دادگاه عالی مردم به تصویب رسیده معیارهای محکومیت و میزان محکومیت متخلفان را شرح داده است.

Dernière mise à jour : 2016-02-24
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Anglais

In short, people are asked to use the guideline, "...if you did not write it yourself, you must give credit.

Perse

از این رو تعداد مقالات اندک و کار کسانی که به داوری مقالات می‌پرداختند چندان سنگین نبود.

Dernière mise à jour : 2016-03-03
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Anglais

We show how EE SE trade off studies can be performed systematically with respect to different architectures, levels of analysis, and capacity metrics. Three representative examples are given to illustrate how EE SE trade off analysis can lead to important insights and useful design guidelines for future cognitive cellular networks.

Perse

ما نشان می دهیم که چگونه مطالعات خارج از تجارت EE SE بطور منظم با توجه به معماری های مختلف ، سطوح تجزیه و تحلیل و معیارهای ظرفیتی قابل انجام است. سه مثال نماینده ارائه شده است برای نشان دادن چگونگی تجزیه و تحلیل تجارت EE SE می تواند به بینشهای مهم و رهنمودهای طراحی مفید برای شبکه های سلولی شناختی آینده منجر شود.

Dernière mise à jour : 2020-07-09
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Référence: Anonyme

Anglais

c/The term “green” (also environmental, environmentally friendly, eco friendly and nature friendly) refers to products, services, laws, guidelines and policies considered to inflict minimal or no harm on the environment (Kawitkar, 2013). In addition, it involves more than environmental issues and relates to all aspects of sustainability and corporate social responsibility (International Tourism Partnership, 2013). Green products have gained popularity in the market (Raska and Shaw, 2012) but

Perse

ج / اصطلاح "سبز" (همچنین سازگار با محیط زیست ، سازگار با محیط زیست ، سازگار با محیط زیست و دوستدار طبیعت) به محصولات ، خدمات ، قوانین ، دستورالعمل ها و سیاست هایی گفته می شود که حداقل یا هیچ خسارت به محیط زیست وارد می کند (کاویتکار ، 2013). بعلاوه ، این موضوع بیش از مسائل زیست محیطی را در بر می گیرد و به همه جنبه های پایداری و مسئولیت اجتماعی شرکت ها مربوط می شود (همکاری بین المللی گردشگری ، 2013). محصولات سبز محبوبیت زیادی در بازار به دست آورده اند (راسکا و شاو ، 2012) اما

Dernière mise à jour : 2020-01-15
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Référence: Anonyme
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Anglais

Management of a patient’s blood glucose or metabolism in nuclear medicine studies has become an integral aspect of daily work primarily due to the increasing use of F-18 flurodeoxyglucose (FDG) positron emission tomography (PET). Newer tracers such as F-18 Fluciclovine and C-11 Choline, are in theory subject to metabolic shifts and changes based on patients’ insulin levels, and also require attention to achieving optimum patient preparation. Metabolic derangements can also affect other studies, such as gastric emptying (GE), the results of which are dependent upon the patient’s blood glucose level during the time of imaging. The growing variety of diabetic medications has increased the complexity of the instructions which need to be given to patients. Current guidelines for patient preparation were developed in the past and have only slowly evolved with the introduction of newer oral medications. In addition to older insulin formulations newer formulations with different profiles of onset, duration, and consistency of action are being used. The wide spectrum of newer drugs now in use for treating diabetes has not been accompanied by any updated consensus on how to manage these drugs for imaging studies which require blood glucose level management. In this article we review these newer diabetes medications primarily to raise awareness of the changing landscape. Our focus will be on suggestions to optimize patient preparation and management for these studies. For each scenario, our suggestions will be given as summary proposals for best patient management. Our hope is that this discussion will stimulate multicenter studies to provide data to support new practice guidelines for metabolically dependent nuclear medicine procedures. Semin Nucl Med 00:1-11 © 2019 Elsevier Inc. All rights reserved.

Perse

به روز رسانی در مورد گلوکز و مدیریت متابولیک سرم مطالعات پزشکی هسته ای بالینی: وضعیت فعلی و دستورالعمل های آینده

Dernière mise à jour : 2019-10-21
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Référence: Anonyme

Anglais

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

Perse

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

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Your Article Library 7 Effects of Mining and Processing of Mineral Resources on Environment Article shared by : ADVERTISEMENTS: Some of the major environmental effects of mining and processing of mineral resources are as follows: 1. Pollution 2. Destruction of Land 3. Subsidence 4. Noise 5. Energy 6. Impact on the Biological Environment 7. Long-term Supplies of Mineral Resources. Mining and processing of mineral resources normally have a considerable im­pact on land, water, air, and biologic resources.Social impacts result from the increased demand for housing and other services in mining areas. 1. Pollution: Mining operations often pollute the atmosphere, surface waters and ground water. Rainwater seeping through spoil heaps may become heavily contami­nated, acidic or turbid, with potentially devastating effects on nearby streams and rivers. ADVERTISEMENTS: Trace elements (cadmium, cobalt, copper and others) when leached from mining wastes and concentrated in water, soil or plants, may be toxic or may cause diseases in people and other animals who consume contaminated water or plants, or who use the soil. Specially constructed ponds to collect run­off can help but cannot eliminate all problems. Huge volumes of dust generated by explosions, transportation and processing may lead to the death of surrounding vegetation. Chemicals used in the extrac­tion processes, such as drilling muds, are often highly polluting substances. 2. Destruction of Land: Mining activity can cause a considerable loss of land because of chemical con­tamination, destruction of productive layers of soil, and often permanent scar­ring of the land surface. Large mining operations disturb the land by directly removing material in some areas and by dumping waste in others. There can be a considerable loss of wildlife habitat. 3. Subsidence: ADVERTISEMENTS: The presence of old, deep mines may cause the ground surface to subside in a vertical or horizontal direction. This may severely damage buildings, roads and farmland, as well as alter the surface drainage patterns. 4. Noise: Blasting and transport cause noise disturbance to local residents and to wild­life. 5. Energy: Extraction and transportation requires huge amounts of energy which adds to impacts such as acid rain and global warming. 6. Impact on the Biological Environment: Physical changes in the land, soil, water and air associated with mining directly and indirectly affect the biological environment. Direct impacts include death of plants or animals caused by mining activity or contact with toxic soil or water from mines. Indirect impacts include changes in nutrient cycling, total biomass, species diversity, and ecosystem stability due to alterations in groundwater or surface water availability or quality. 7. Long-term Supplies of Mineral Resources: ADVERTISEMENTS: The economies of industrialized countries require the extraction and process­ing of large amounts of minerals to make products. As other economies indus­trialize, their mineral demands increase rapidly. The mineral demands of coun­tries in Asia, such as Malaysia, Thailand and South Korea have grown phenom­enally in the last twenty years. Since mineral resources are a non-renewable resource, it is important for all countries to take a low-waste sustainable earth approach to dealing with them. Developed countries need to change from a high-waste throw away approach and developing countries need to insure that they do not adopt such an ap­proach. Low-waste approach requires emphasis on recycling, reusing and waste reduction and less emphasis on dumping, burying and burning. Recycling and reuse benefit the environment because they: 1. Extend the supply of minerals by reducing the amount of materials that must be extracted ADVERTISEMENTS: 2. Require less energy than extraction 3. Cause less pollution and land disruption 4. Reduce waste disposal costs and prolong the life of landfills by reducing the volume of solid waste. Reducing unnecessary waste of non-renewable resources can extend supplies even more dramatically than recycling and reuse because it reduces the need to extract more resources, thereby reducing the impact of extraction and process­ing on the environment. Sponsored LinksYou May Like 7 Arthritis Causing Foods You Probably Eat Every Day Health & Human Research Drink This at Night to Remove Belly Fat Food Prevent Which Foods Should Never Be Refrigerated? 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کتابخانه مقاله شما 7 تأثیر معادن و پردازش منابع معدنی در محیط زیست ماده به اشتراک گذاشته شده توسط: تبلیغات: برخی از اثرات عمده زیست محیطی معدن و پردازش منابع معدنی به شرح زیر است: 1. آلودگی 2. تخریب زمین 3. سقوط 4. نویز 5. انرژی 6. تاثیر محیط زیستی 7. منابع زراعی درازمدت. معدن و پردازش منابع معدنی معمولا تاثیر قابل توجهی در زمین، آب، هوا و زیست محیطی دارد

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Following the well established guidelines to design systematic mapping studies [9], the following paragraphs present the design of our study.

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به دنبال دستورالعمل های خوبی برای طراحی مطالعات نقشه برداری منظم [9]، پاراگراف های زیر طرح مطالعه ما را ارائه می دهد.

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The results and performance of the robot through this framework have been discussed and future guidelines have been provided to make this software more feature rich and learning oriented.

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نتایج و عملکرد ربات از طریق این چارچوب مورد بحث قرار گرفته است و دستورالعمل های آینده ارائه شده است تا این نرم افزار دارای ویژگی های غنی و یادگیری گرا باشد.

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A novel, precise, rapid and sensitive reverse phase high performance liquid chromatographic method has been developed for the validated estimation of Tadalafil in bulk and tablet dosage form. The separation was achieved on Agilent Eclipse XDB C18 column (150 mm×4.6 mm, 5 µ) using a mobile phase that consists of the buffer (potassium dihydrogen orthophosphate) and acetonitrile in the ration of 50:50 V/V, pH 6 was adjusted with orthophosphoric acid. The flow rate was maintained at 1.2 ml/min and the detection wavelength was 285 nm. The method was validated for linearity, specificity, sensitivity as per ICH guidelines. The retention time was found to be 3.181 for Tadalafil. The calibration curve was linear over the concentration range of 10–150 µg/ml. The % RSD was satisfactory which showed the method found to be reliable. The high percentage recovery confi

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QUERY LENGTH LIMIT EXCEDEED. MAX ALLOWED QUERY : 500 CHARS

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"According to the 2012 guidelines of the American Academy of Otolaryngology & Head and Neck Surgery (AAO-HNS), tonsillectomy is indicated as follows:"Clinicians may recommend tonsillectomy for recurrent throat infection with a frequency of at least 7 episodes in the past year or at least 5 episodes per year for 2 years or at least 3 episodes per year for 3 years with documentation in the medical record for each episode of sore throat and one or more of the following: temperature >38.3oC, cervical adenopathy, tonsillar exudates, or positive test for Group A Beta- hemolytic strep.

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مطابق با دستورالعمل‌های آکادمی آمریکایی گوش و حلق و بینی و جراحی سر و گردن (AAO-HNS) در سال ۲۰۱۲، تانسیلکتومی در موارد زیر اندیکاسیون دارد: عفونت مکرر لوزه با تعداد دفعات ۷ بار در سال گذشته، حداقل ۵ بار در هر سال به مدت ۲ سال و یا حداقل ۳ بار در هر سال به مدت ۳ سال، که به صورت مستندات در پرونده‌های پزشکی بیمار موجود باشد و یا یک یا بیشتراز موارد زیر باشد: دمای بیشتر از ۳۸٫۳ درجه، آدنوپاتی گردن، ترشحات لوزه و یا مثبت شدن آزمون برای گروه A استرپتوکوک بتا همولیتیک.

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* Basic NetFlow information on the Cisco Site* RFC3334 - Policy-Based Accounting* RFC3954 - NetFlow Version 9* RFC3917 - Requirements for IP Flow Information Export (IPFIX)* RFC3955 - Candidate Protocols for IP Flow Information Export (IPFIX)* RFC5101 - Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information (IPFIX)* RFC5102 - Information Model for IP Flow Information Export* RFC5103 - Bidirectional Flow Export Using IP Flow Information Export* RFC5153 - IPFIX Implementation Guidelines* RFC5470 - Architecture for IP Flow Information Export* RFC5471 - Guidelines for IP Flow Information Export (IPFIX) Testing* RFC5472 - IP Flow Information Export (IPFIX) Applicability* RFC5473 - Reducing Redundancy in IP Flow Information Export (IPFIX) and Packet Sampling (PSAMP) Reports* Using Netflow to store re-aggregated inbound and outbound flows* AppFlow specifications and standards track discussion

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* Basic NetFlow information on the Cisco Site* RFC3334 - Policy-Based Accounting* RFC3954 - NetFlow Version 9* RFC3917 - Requirements for IP Flow Information Export (IPFIX)* RFC3955 - Candidate Protocols for IP Flow Information Export (IPFIX)* RFC5101 - Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of IP Traffic Flow Information (IPFIX)* RFC5102 - Information Model for IP Flow Information Export* RFC5103 - Bidirectional Flow Export Using IP Flow Information Export* RFC5153 - IPFIX Implementation Guidelines* RFC5470 - Architecture for IP Flow Information Export* RFC5471 - Guidelines for IP Flow Information Export (IPFIX) Testing* RFC5472 - IP Flow Information Export (IPFIX) Applicability* RFC5473 - Reducing Redundancy in IP Flow Information Export (IPFIX) and Packet Sampling (PSAMP) Reports* List of software related to flow accounting* Using Netflow to store re-aggregated inbound and outbound flows* AppFlow specifications and standards track discussion

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* UML state machine== References ==== External links ==* Introduction to UML 2 State Machine Diagrams by Scott W. Ambler* UML 2 State Machine Diagram Guidelines by Scott W. Ambler*Intelliwizard - UML StateWizard - A ClassWizard-like round-trip UML dynamic modeling/development framework and tool running in popular IDEs under open-source license.

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* UML state machine* Introduction to UML 2 State Machine Diagrams by Scott W. Ambler* UML 2 State Machine Diagram Guidelines by Scott W. Ambler*Intelliwizard - UML StateWizard - A ClassWizard-like round-trip UML dynamic modeling/development framework and tool running in popular IDEs under open-source license.

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* W3C Guidelines on Naming and Addressing: URIs, URLs, ...* W3C explanation of UTF-8 in URIs* W3C HTML form content types

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* رهنمودهای W3C در مورد نام‌گذاری و نشانی‌دهی: URIs، URLs و ...* توضیحات W3C در مورد UTF-8 در URIها* گونه‌های محتوای فرم‌های اچ‌تی‌ام‌ال در W3C

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*Rules for Nomenclature of Genes, Genetic Markers, Alleles, and Mutations in Mouse and Rat*HGNC Guidelines for Human Gene Nomenclature*SNP effect predictor with galaxy integration*Human Gene Mutation Database*GWAS Central*Open SNP — a portal for sharing own SNP test results*The HapMap Project

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* Online tool that predicts on the effects of SNPs on protein function* Rules for Nomenclature of Genes, Genetic Markers, Alleles, and Mutations in Mouse and Rat* HGNC Guidelines for Human Gene Nomenclature* SNP effect predictor with galaxy integration* Human Gene Mutation Database* GWAS Central

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