[论文笔记] Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing (FGCS, 2012)

Timespan: 2.5 – 2.14
Anton Beloglazov, Jemal H. Abawajy, Rajkumar Buyya: Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Comp. Syst. 28(5): 755-768 (2012) (gs:35)

    作者Anton Beloglazov是墨尔本大学的博士生，师从Rajkumar Buyya，同时还在IBM实习，研究兴趣有：分布式系统、虚拟化、数据中心节能等。博士论文题目是：Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers，关注通过动态合并数据中心的虚拟机来提高物理资源的利用率，在满足QoS约束前提下降低能耗。目前，他还在参与OpenStack Neat（基于OpenStack的VM动态合并框架）。

    以下是他发表的论文：

Anton Beloglazov and Rajkumar Buyya, "Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers Under Quality of Service Constraints", IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE CS Press, USA, 2012

Anton Beloglazov and Rajkumar Buyya, "OpenStack Neat: A Framework for Dynamic Consolidation of Virtual Machines in OpenStack Clouds - A Blueprint", Technical Report CLOUDS-TR-2012-4, Cloud Computing and Distributed Systems Laboratory, The University of Melbourne, August 14, 2012

Anton Beloglazov, Sareh Fotuhi Piraghaj, Mohammed Alrokayan, and Rajkumar Buyya, "Deploying OpenStack on CentOS Using the KVM Hypervisor and GlusterFS Distributed File System", Technical Report CLOUDS-TR-2012-3, Cloud Computing and Distributed Systems Laboratory, The University of Melbourne, August 14, 2012

Anton Beloglazov and Rajkumar Buyya, "Optimal Online Determin istic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers", Concurrency and Computation: Practice and Experience (CCPE), Volume 24, Issue 13, Pages: 1397-1420, John Wiley & Sons, Ltd, New York, USA, 2012

Anton Beloglazov, Jemal Abawajy, and Rajkumar Buyya, "Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing", The International Journal of Grid Computing and eScience, Future Generation Computer Systems (FGCS), Volume 28, Issue 5, Pages: 755-768, Elsevier Science, Amsterdam, The Netherlands, May 2012

Kyong Hoon Kim, Anton Beloglazov and Rajkumar Buyya, "Power-Aware Provisioning of Virtual Machines for Real-time Cloud Services", Concurrency and Computation: Practice and Experience (CCPE), Volume 23, Number 13, Pages: 1492-1505, John Wiley & Sons, Ltd, New York, USA, 2011
Anton Beloglazov and Rajkumar Buyya, "Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers", Proceedings of the IBM Collaborative Academia Research Exchange Workshop (I-CARE 2010), Bangalore, India, October 22, 2010

Anton Beloglazov and Rajkumar Buyya, "Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers", Proceedings of the 8th International Workshop on Middleware for Grids, Clouds and e-Science (MGC 2010), Bangalore, India: ACM, November 29 — December 3, 2010

Anton Beloglazov, Rajkumar Buyya, Young Choon Lee, and Albert Zomaya, "A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems", Advances in Computers, Marvin V. Zelkowitz (editor), Volume 82, Pages: 47-111, ISSN: 0065-2458, Elsevier, 2011
Rajkumar Buyya, Anton Beloglazov, and Jemal Abawajy, "Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges", Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 2010 — Keynote Paper

Anton Beloglazov and Rajkumar Buyya, "Energy Efficient Allocation of Virtual Machines in Cloud Data Centers", In Proceedings of the 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2010), Melbourne, Australia, May 17-20, 2010

Anton Beloglazov and Rajkumar Buyya, "Energy Efficient Resource Management in Virtualized Cloud Data Centers", IEEE TCSC Doctoral Symposium, In Proceedings of the 10th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid 2010), Melbourne, Australia, May 17-20, 2010

Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose, and Rajkumar Buyya, "CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms", Software: Practice and Experience (SPE), Volume 41, Number 1, Pages: 23-50, ISSN: 0038-0644, John Wiley & Sons, Ltd, New York, USA, January, 2011. DOI: 10.1002/spe.995

Anton Beloglazov and Rajkumar Buyya, "Power and Performance Efficient Resource Management in Cloud Computing", in Proceedings of the IEEE Science and Engineering Graduate Research Expo 2009, Melbourne, Australia, 2009, pp. 38-40

以下是论文笔记：

1. 本文首先提出了一个节能云计算(energy-efficient Cloud computing)的架构框架(architectural framework)和原则。
下图系统架构图(S3.1)

如上图所示，分为四层，其中Green Service Allocator一层角色众多，承担了对服务用户请求进行分析、SLA协商、服务定价、VM调度、VM管理等功能。

2. (S3.2)中则提出了Power model，建立了能耗与CPU利用率的管理：

其中Pmax是服务器完全利用时最高的能耗，k是空闲服务器效率的能源比例（通常为70%)，u则是CPU的利用率。
(能耗跟内存、磁盘、网络使用情况也有关系，但主要与CPU有关，所以上面的公式里只体现了CPU利用率，这个论断见S3.2)

3. 在此基础上，(S4)提出了资源供给与分配的算法，以改进云计算环境的节能。该算法是启发式的(heuristics)，可以在确保满足客户QoS的前提下，使得数据中心的节能效果得到改进。
(S4.1) “VM placement”：是关于针对创建新的VM请求，分配到哪台PM使得能耗增加最小的问题。此问题被建模为“bin packing problem with variable bin sizes and prices”，使用了修改版的“Best Fit Decreasing (BFD)”算法。
(S4.2)“VM selection”: 是关于如何优化当前的VM分配，以优化能耗的问题。主要分成两个步骤：首先选择迁移对象（一组VMs）；然后使用MBFD算法，确定选出的迁移对象将被放置到哪些PMs上。

4. 针对“何时选择哪些迁移对象的问题”，(S4.2)中提出了三种选择策略（基本思想差不多）：

• the minimization of migrations(MM) policy
• the highest potential growth(HPG) policy
• the random choice(RC) policy

以MM策略为例，下图是该策略的规则：

大意如下：
    如果某一台PM的CPU利用率过高超出了上限，则找出一组个数最少的VM，这组VM就是迁移对象；
    如果某一台PM的CPU利用率过低达不到下限，则该PM上所有VM都是迁移对象。

5. (S5)进行了实验验证
(S5.1)中介绍了性能度量指标：

• total energy consumption
• SLA violation percentage
• number of VM migrations initiated by the VM manager
• average SLA violation

实验是在模拟平台(CloudSim toolkit)上进行的。具体的实验过程和结果详见原文。

6. 作者在(S6)提出了相关的open challenges

• optimization of VM placement according to the utilization of multiple system resources
• optimization of virtual network topologies
• optimization of thermal states and cooling system operation
• efficient consolidation of VMs for managing heterogeneous workloads
• a holistic approach to energy-aware resource management