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虚拟云环境下基于模型预测且满足多重约束的时限敏感任务调度研究

张奕1,程小辉1,陈柳华2   

  1. 1. 桂林理工大学信息科学与工程学院
    2. 克莱姆森大学
  • 收稿日期:2017-04-17 修回日期:2017-06-21 发布日期:2017-06-21
  • 通讯作者: 张奕

Multi-constraints task scheduling heuristic based on model predictive control in real-time cloud environments

  • Received:2017-04-17 Revised:2017-06-21 Online:2017-06-21
  • Contact: Yi ZHANG

摘要: 目前以虚拟云服务平台作为强大计算平台的虚拟云环境下,许多现存调度方法致力于合并虚拟机以减少物理机数目,从而达到减少能源消耗的目的,但会引入高额虚拟机迁移成本。此外,现存方法也没有考虑导致用户高额支付成本的成本因子影响。本文以减少云服务提供者能源消耗和云服务终端用户支付成本为目标,同时保障用户任务的时限要求。本文提出的能源与时限可感知的非迁移调度算法(Energy and Deadline Aware with Non-Migration Scheduling , EDA-NMS),利用任务时限的松弛度,延迟宽松时限任务的执行从而无需唤醒新的物理机,更无需引入虚拟机动态迁移成本,达到减少能源消耗的目的。EDA-NMS采用智能虚拟机实例类型组合方案,满足用户关键任务的敏感时限并保障用户支付成本最低。多重扩展实验结果表明EDA-NMS算法与现存其它算法比较,能更有效地减少能源消耗和成本,且保证了任务时限要求。

关键词: 虚拟云环境, 实时任务, 调度, 关键度, 能源可感知

Abstract: Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just suf?cient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy ef?ciency without introducing VM migration overhead and without compromising deadline guarantees.

Key words: virtualized cloud, real-time task, scheduling, criticality, energy-aware

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