计算机应用 ›› 2017, Vol. 37 ›› Issue (10): 2754-2759.DOI: 10.11772/j.issn.1001-9081.2017.10.2754

• 先进计算 • 上一篇    下一篇

虚拟云下满足多重约束的时限敏感任务调度算法

张奕1,2, 程小辉1,2, 陈柳华3   

  1. 1. 桂林理工大学 信息科学与工程学院, 广西 桂林 541004;
    2. 广西嵌入式技术与智能系统重点实验室(桂林理工大学), 广西 桂林 541004;
    3. 克莱姆森大学 电子与计算机工程系, 南卡罗来纳州 克莱姆森市 29631, 美国
  • 收稿日期:2017-04-17 修回日期:2017-07-10 出版日期:2017-10-10 发布日期:2017-10-16
  • 通讯作者: 张奕(1977-),女,江西九江人,副教授,博士,CCF会员,主要研究方向:面向服务的计算、实时计算,E-mail:zywait@glut.edu.cn
  • 作者简介:张奕(1977-),女,江西九江人,副教授,博士,CCF会员,主要研究方向:面向服务的计算、实时计算;程小辉(1961-),男,江西樟树人,教授,博士研究生,CCF会员,主要研究方向:物联网;陈柳华(1987-),男,广东湛江人,博士,主要研究方向:分布式计算.
  • 基金资助:
    国家自然科学基金资助项目(61662017);高等学校科学技术研究项目(2013YB113);广西重点实验室嵌入式技术和智能系统基金资助项目(桂林理工大学)。

Multi-constraints deadline-aware task scheduling heuristic in virtual clouds

ZHANG Yi1,2, CHENG Xiaohui1,2, CHEN Liuhua3   

  1. 1. School of Information Science and Engineering, Guilin University of Technology, Guilin Guangxi 541004, China;
    2. Guangxi Key Laboratory of Embedded Technology and Intelligent Systems (Guilin University of Technology), Guilin Guangxi 541004, China;
    3. School of Electrical and Computer Engineering, Clemson University, Clamson, South Carolina 29631, USA
  • Received:2017-04-17 Revised:2017-07-10 Online:2017-10-10 Published:2017-10-16
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61662017), the Scientific and Technological Research Program for Guangxi Educational Commission (2013YB113), the Guangxi Key Laboratory Fund of Embedded Technology and Intelligent Systems (Guilin University of Technology).

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。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,提出一种能源与时限可感知的非迁移调度(EDA-NMS)算法。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,以达到减少能源消耗的目的。多重扩展实验结果表明,EDA-NMS采用成本和能耗有效的虚拟机实例类型组合方案,与主动及响应式调度(PRS)算法相比,在减少静态能耗的同时,能更有效地满足用户关键任务的敏感时限并确保用户支付成本最低。

关键词: 虚拟云, 实时任务, 调度, 关键度, 能源可感知

Abstract: Many existing scheduling approaches in cloud data centers try to consolidate Virtual Machines (VMs) by VM live migration technique to minimize the number of Physical Machines (PMs) and hence minimize the energy consumption, however, it introduces high migration overhead; furthermore, the cost factor that leads to high payment cost for cloud users is usually not taken into account. Aiming at energy reduction for cloud providers and payment saving for cloud users, as well as guaranteeing the deadline of user tasks, a heuristic task scheduling algorithm called Energy and Deadline-Aware with Non-Migration Scheduling (EDA-NMS) was proposed. The execution of the tasks that have loose deadlines was postponed to avoid waking up new PMs and migration overhead, thus reducing the energy consumption. The results of extensive experiments show that compared with Proactive and Reactive Scheduling (PRS) algorithm, by selecting a smart VM combination scheme, EDA-NMS can reduce the static energy consumption and ensure the lowest payment with meeting the deadline requirement for key user tasks.

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

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