Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions
Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions
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管理云计算中虚拟机的性能开销：调查、最新技术和未来方向
DOI：
10.1109/jproc.2013.2287711
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发表时间：
2014
影响因子：
20.6
通讯作者：
Vasilakos Athanasios V.
中科院分区：
文献类型：
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作者：
Xu Fei;Liu Fangming;Jin Hai;Vasilakos Athanasios V.
基础设施即服务（IaaS）云计算为客户（租户）提供了一种可扩展且经济的方式来按需配置虚拟机（vm），同时只按时间向他们收取租用的计算资源的费用。然而，由于VM在数据中心共享计算资源上的竞争，这种新的计算范式不可避免地给租户带来了明显的VM性能开销（即不可预测的性能），这已经成为IaaS云的主要问题之一。因此，最近越来越多的工作致力于保证租户的虚拟机性能。在本调查中，我们回顾了管理虚拟机性能开销的最新研究，并在IaaS云的不同场景下总结了它们，从单服务器虚拟化、单个大型数据中心到多个地理分布式数据中心。具体来说，我们通过举例说明代表性场景来揭示VM性能开销的原因，讨论性能建模方法，特别关注其准确性和成本，并通过确定其有效性和实现复杂性来比较开销缓解技术。通过对每个现有解决方案的优缺点的深入了解，我们进一步提出了与IaaS云中VM性能开销的建模方法和缓解技术相关的未来研究挑战。
Infrastructure-as-a-Service (IaaS) cloud computing offers customers (tenants) a scalable and economical way to provision virtual machines (VMs) on demand while charging them only for the leased computing resources by time. However, due to the VM contention on shared computing resources in datacenters, this new computing paradigm inevitably brings noticeable performance overhead (i.e., unpredictable performance) of VMs to tenants, which has become one of the primary issues of the IaaS cloud. Consequently, increasing efforts have recently been devoted to guaranteeing VM performance for tenants. In this survey, we review the state-of-the-art research on managing the performance overhead of VMs, and summarize them under diverse scenarios of the IaaS cloud, ranging from the single-server virtualization, a single mega datacenter, to multiple geodistributed datacenters. Specifically, we unveil the causes of VM performance overhead by illustrating representative scenarios, discuss the performance modeling methods with a particular focus on their accuracy and cost, and compare the overhead mitigation techniques by identifying their effectiveness and implementation complexity. With the obtained insights into the pros and cons of each existing solution, we further bring forth future research challenges pertinent to the modeling methods and mitigation techniques of VM performance overhead in the IaaS cloud.
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DOI：
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发表时间：
2013-06
期刊：
--
影响因子：
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作者：
D. Novakovic;N. Vasic;Stanko Novakovic;Dejan Kostic;R. Bianchini
通讯作者：
D. Novakovic;N. Vasic;Stanko Novakovic;Dejan Kostic;R. Bianchini
DOI：
10.1145/2503210.2503274
发表时间：
2013-11
期刊：
2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
影响因子：
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作者：
Kaveh Razavi;T. Kielmann
通讯作者：
Kaveh Razavi;T. Kielmann
影响因子：
12.8
作者：
Henrique Rodrigues;J. R. Santos;Yoshio Turner;Paolo Soares;D. Guedes
通讯作者：
Henrique Rodrigues;J. R. Santos;Yoshio Turner;Paolo Soares;D. Guedes
DOI：
--
发表时间：
2012-06
期刊：
--
影响因子：
--
作者：
Jeongseob Ahn;Changdae Kim;Jaeung Han;Young-ri Choi;Jaehyuk Huh
通讯作者：
Jeongseob Ahn;Changdae Kim;Jaeung Han;Young-ri Choi;Jaehyuk Huh
DOI：
10.1145/2413176.2413210
发表时间：
2012-12
影响因子：
6.4
作者：
Joshua Reich;Oren Laadan;E. Brosh;A. Sherman;V. Misra;Jason Nieh;D. Rubenstein
通讯作者：
Joshua Reich;Oren Laadan;E. Brosh;A. Sherman;V. Misra;Jason Nieh;D. Rubenstein