当前位置: X-MOL 学术Computing › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Constraint aware profit maximization scheduling of tasks in heterogeneous datacenters
Computing ( IF 3.3 ) Pub Date : 2020-08-10 , DOI: 10.1007/s00607-020-00838-1
Chinmaya Kumar Swain , Bhawana Gupta , Aryabartta Sahu

The extensive use of cloud services in different domains triggers the efficient use of cloud resources to achieve maximum profit. The heterogeneous nature of data centers and the heterogeneous resource requirement of user applications create a scope of improvement in task scheduling. The resource requirements in terms of task constraints must be fulfilled for the tasks to be admitted to the system. Once a task admitted to the system, it may violate service level agreement and incurs penalty due to the disproportionate resource allocation at run time. The latency-sensitive and short-lived workloads need effective scheduling to gain more profit. In this work, we propose Heuristic of Ordering and Mapping for Constraint Aware Profit Maximization (HOM-CAPM) problem for efficient scheduling of tasks with constraints and deadlines to gain maximum profit. The HOM-CAPM approach considers estimation of task execution time in a heterogeneous environment, efficient task ordering, and profit-based task allocation to maximize the overall profit of the cloud system. To gain maximum profit the proposed heuristic considers two cases, (a) not allowing the tasks for execution if it expected to miss its deadline and (b) allowing the task which earns substantial profit even though it is expected to miss its deadline. The results of the extensive simulation using Google trace data as input show that our proposed HOM-CAPM approach generates more profit than other state-of-the-art approaches.

中文翻译:

异构数据中心任务的约束感知利润最大化调度

云服务在不同领域的广泛使用,触发了云资源的高效利用,实现利润最大化。数据中心的异构特性和用户应用程序的异构资源需求为任务调度创造了改进空间。必须满足任务约束方面的资源需求,才能允许任务进入系统。一旦任务被系统接纳,它可能会违反服务级别协议并因运行时不成比例的资源分配而招致惩罚。对延迟敏感且寿命短的工作负载需要有效的调度才能获得更多利润。在这项工作中,我们提出了约束感知利润最大化(HOM-CAPM)问题的启发式排序和映射,用于有效调度具有约束和截止日期的任务以获得最大利润。HOM-CAPM 方法考虑异构环境中任务执行时间的估计、高效的任务排序和基于利润的任务分配,以最大化云系统的整体利润。为了获得最大利润,提议的启发式考虑两种情况,(a) 如果任务预计会错过其最后期限,则不允许执行;(b) 允许即使预计会错过其最后期限也能赚取可观利润的任务。使用 Google 跟踪数据作为输入的广泛模拟的结果表明,我们提出的 HOM-CAPM 方法比其他最先进的方法产生更多的利润。为了获得最大利润,提议的启发式考虑两种情况,(a) 如果任务预计会错过其最后期限,则不允许执行;(b) 允许即使预计会错过其最后期限也能赚取可观利润的任务。使用 Google 跟踪数据作为输入的广泛模拟的结果表明,我们提出的 HOM-CAPM 方法比其他最先进的方法产生更多的利润。为了获得最大利润,提议的启发式考虑两种情况,(a) 如果任务预计会错过其最后期限,则不允许执行;(b) 允许即使预计会错过其最后期限也能赚取可观利润的任务。使用 Google 跟踪数据作为输入的广泛模拟的结果表明,我们提出的 HOM-CAPM 方法比其他最先进的方法产生更多的利润。
更新日期:2020-08-10
down
wechat
bug