Adaptive control of virtualized resources in utility computing environments

By Pradeep Padala, Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, S. Singhal, A. Merchant, K. Salem. European Conference on Computer Systems, 2007

677 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/2f293c3d6b07e740c239acf913fbf6cb41e32db4

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Citations in the corpus

Q-clouds: managing performance interference effects for QoS-aware clouds (2010, methodology): “The disassociation between virtual and physical resources allows the management layer to transparently adapt resource allocations [21, 25, 26].”
CloudScale: elastic resource scaling for multi-tenant cloud systems (2011, methodology): “Previous work [44, 29, 31, 36, 32] has extensively studied applying control theory to achieve adaptive fine-grained resource allocations based on SLO conformance feedback.”
Live virtual machine migration with adaptive, memory compression (2009, background): “Some virtual machine migration strategies [2][3] are proposed to balance VM loads among physical nodes.”
Automated control of multiple virtualized resources (2009, methodology): “Examples of its application include web server performance guarantees [1], dynamic adjustment of the cache size for multiple request classes [19], CPU and memory utilization control in web servers [9], adjustment of resource demands of virtual machines based on resource availability [28], and dynami”
Live Video Analytics at Scale with Approximation and Delay-Tolerance (2017, background): “Autonomic computing [24, 26,29,66,70,77] allocate resources to VMs and web applications to maximize their quality of service.”
Cloud auto-scaling with deadline and budget constraints (2010, background): “There have been a number works on dynamic resource provisioning in virtualized computing environment [9][10][12][4].”
Adaptive resource provisioning for read intensive multi-tier applications in the cloud (2011, background): “In [4] and [5], a two-level control loop is proposed to make resource allocation decisions within a single physical machine.”
Applying reinforcement learning towards automating resource allocation and application scalability in the cloud (2013, background): “The current approach favoured for allocating resources to applications on the basis of fluctuating numbers of requests and application performance is to define threshold based policies [6, 7].”
Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting (2011, background): “[5] provide a control-theoretic solution where each tier of the application is executed on each virtual machine.”
Energy aware consolidation for cloud computing (2008, background): “A framework for adaptive control of virtualized resources in utility computing environments is presented in [5].”
PRESS: PRedictive Elastic ReSource Scaling for cloud systems (2010, methodology): “Previous work has applied control theory [24], [25], [26] or reinforcement learning [27] to adaptively adjust resource allocations based on SLO conformance feedback.”
Cyber–Physical Systems: A Perspective at the Centennial (2012, background): “on resource reservation for real-time applications, in [160], [161], [162] on a control theoretical approach to performance and throughput management of computing systems, in [163] on real-time scheduling algorithms for power management in embedded real-time systems, and in [7] for more on real-time”
Efficient resource provisioning in compute clouds via VM multiplexing (2010, background): “Other work focus on short-term, dynamic provisioning techniques [20, 18, 15].”