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Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both virtual machines (VMs) and hosted applications need to be configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and applications further complicates the problem of cloud configuration.(More)
Website capacity determination is crucial to measurement-based access control, because it determines when to turn away excessive client requests to guarantee consistent service quality under overloaded conditions. Conventional capacity measurement approaches based on high-level performance metrics like response time and throughput may result in either(More)
In a web system, configuration is crucial to the performance and service availability. It is a challenge, not only because of the dynamics of Internet traffic, but also the dynamic virtual machine environment the system tends to be run on. In this paper, we propose a reinforcement learning approach for autonomic configuration and recon-figuration of(More)
Although cloud computing has gained sufficient popularity recently, there are still some key impediments to enterprise adoption. Cloud management is one of the top challenges. The ability of on-the-fly partitioning hardware resources into virtual machine(VM) instances facilitates elastic computing environment to users. But the extra layer of resource(More)
An increasing number of new multicore systems use the Non-Uniform Memory Access architecture due to its scalable memory performance. However, the complex interplay among data locality, contention on shared on-chip memory resources, and cross-node data sharing overhead, makes the delivery of an optimal and predictable program performance difficult.(More)
Understanding server capacity is crucial for system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure the server capacity in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website,(More)
The deployment of MapReduce in datacenters and clouds present several challenges in achieving good job performance. Compared to in-house dedicated clusters, datacenters and clouds often exhibit significant hardware and performance heterogeneity due to continuous server replacement and multi-tenant interferences. As most Mapreduce implementations assume(More)
Virtual machine (VM) technology enables multiple VMs to share resources on the same host. Resources allocated to the VMs should be re-configured dynamically in response to the change of application demands or resource supply. Because VM execution involves privileged domain and VM monitor, this causes uncertainties in VMs' resource to performance mapping and(More)
MapReduce emerges as an important distributed programming paradigm for large-scale applications. Running MapReduce applications in clouds presents an attractive usage model for enterprises. In a virtual MapReduce cluster, the interference between virtual machines (VMs) causes performance degradation of map and reduce tasks and renders existing data(More)
Cloud elasticity allows dynamic resource provisioning in concert with actual application demands. Feedback control approaches have been applied with success to resource allocation in physical servers. However, cloud dynamics make the design of an accurate and stable resource controller more challenging, especially when response time is considered as the(More)