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We have presented the design, implementation, and evaluation of a resource management system for cloud computing services

Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment

IEEE Trans. Parallel Distrib. Syst., no. 6 (2018): 1107-1117

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摘要

Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on app...更多

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简介
  • THE elasticity and the lack of upfront capital investment offered by cloud computing is appealing to many businesses.
  • The authors study a different problem: how can a cloud service provider best multiplex its virtual resources onto the physical hardware?
  • This is important because much of the touted gains in the cloud model come from such multiplexing.
  • The cloud model is expected to make such practice unnecessary by offering automatic scale up and down in response to load variation.
  • Besides reducing the hardware cost, it saves on electricity which contributes to a significant portion of the operational expenses in large data centers
重点内容
  • THE elasticity and the lack of upfront capital investment offered by cloud computing is appealing to many businesses
  • We present the design and implementation of an automated resource management system that achieves a good balance between the two goals
  • The only downside of having more cold spots in the system is that it may increase the number of actively used PMs. This is investigated in Fig. 6b which shows that the average numbers of actively used PMs remain essentially the same with or without load prediction
  • We start with a small scale experiment consisting of three physical machines and five virtual machines so that we can present the results for all servers in Fig. 7
  • We have presented the design, implementation, and evaluation of a resource management system for cloud computing services
  • Our system multiplexes virtual to physical resources adaptively based on the changing demand
方法
  • The authors' experiments are conducted using a group of 30 Dell PowerEdge blade servers with Intel E5620 CPU and 24 GB of RAM.
  • An Apache server runs on each VM.
  • The authors use httperf to invoke CPU intensive PHP scripts on the Apache server.
  • This allows them to subject the VMs to different degrees of CPU load by adjusting the client request rates.
结果
  • The only downside of having more cold spots in the system is that it may increase the number of APMs. The only downside of having more cold spots in the system is that it may increase the number of APMs
  • This is investigated in Fig. 6b which shows that the average numbers of APMs remain essentially the same with or without load prediction
结论
  • The authors have presented the design, implementation, and evaluation of a resource management system for cloud computing services.
  • The authors' system multiplexes virtual to physical resources adaptively based on the changing demand.
  • The authors use the skewness metric to combine VMs with different resource characteristics appropriately so that the capacities of servers are well utilized.
  • The authors' algorithm achieves both overload avoidance and green computing for systems with multiresource constraints
表格
  • Table1: Load Prediction Algorithms
  • Table2: Parameters in Our Simulation
Download tables as Excel
相关工作
  • 7.1 Resource Allocation at the Application Level

    Automatic scaling of Web applications was previously studied in [14] and [15] for data center environments. In MUSE [14], each server has replicas of all web applications running in the system. The dispatch algorithm in a frontend L7-switch makes sure requests are reasonably served while minimizing the number of underutilized servers. Work [15] uses network flow algorithms to allocate the load of an application among its running instances. For connection oriented Internet services like Windows Live Messenger, work [10] presents an integrated approach for load dispatching and server provisioning. All works above do not use virtual machines and require the applications be structured in a multitier architecture with load balancing provided through an front-end dispatcher. In contrast, our work targets Amazon EC2-style environment where it places no restriction on what and how applications are constructed inside the VMs. A VM is treated like a blackbox. Resource management is done only at the granularity of whole VMs.
基金
  • This work was supported by the National Natural Science Foundation of China (Grant No 61170056), the National High Technology Research and Development Program (“863” Program) of China (Grant No 2013AA013203), National Basic Research Program of China (Grant No 2009CB320505) and Digital Resource Security Protection Service Based on Trusted Identity Federation and Cloud Computation SubProject of 2011 Information Security Special Project sponsored by National Development and Reform Commission
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