Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes increased complexities in achieving an energy efficient management of the Cloud jobs. The phenomenon of a few proportions of tasks characterising increased resource intensity within a given job usually lead the providers to over-provision all the encompassed tasks, resulting in majority of the tasks incurring an increased proportions of resource idleness. To this end, this paper proposes a novel analytics framework which integrates a resource estimation module to estimate the resource requirements of tasks a priori, a straggler classification module to classify tasks based on their resource intensity, and a resource optimisation module to optimise the level of resource provision depending on the task nature and various runtime factors. Performance evaluations conducted both theoretically and through practical experiments prove that the proposed methodology performs better than the compared statistical resource estimation methods and existing models of straggler mitigation, and further demonstrate the effectiveness of the proposed methodology in achieving energy conservation by postulating appropriate level of resource provisioning for task execution.

中文翻译：

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一种通过对抗任务异构性来优化数据中心能源意识资源供应的方法

云工作负载越来越异构，以至于单个云作业可能包含一个到多个任务，并且属于同一作业的任务在实际执行过程中可能表现不同。这种固有的任务异构性增加了实现云作业的节能管理的复杂性。在给定作业中，少数任务的特征是资源强度增加的现象通常会导致提供者过度供应所有包含的任务，从而导致大多数任务的资源闲置比例增加。为此，本文提出了一种新颖的分析框架，该框架集成了一个资源估计模块来先验地估计任务的资源需求，一个落后的分类模块根据任务的资源强度对任务进行分类，以及一个资源优化模块，用于根据任务性质和各种运行时间因素优化资源供应水平。通过理论和实际实验进行的性能评估证明，所提出的方法比比较统计资源估计方法和现有的落后者减缓模型表现更好，并通过假设适当的资源供应水平进一步证明了所提出的方法在实现节能方面的有效性用于任务执行。