Automated control of multiple virtualized resources

By Pradeep Padala, Kai-yuan Hou, Kang G. Shin, Xiaoyun Zhu, Mustafa Uysal, Zhikui Wang, S. Singhal, A. Merchant. European Conference on Computer Systems, 2009

578 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/7f14d6750effc95ab67ba22822329e6ae3e2d773

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms (2017, background): “For example, many papers explore (sometimes offline) workload profiling and aggressive online resource reallocation, via dynamic monitoring, scheduling, and/or live VM migration [2, 7, 20, 21, 24, 27].”
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “[88] propose a MIMO adaptive controller that uses a second-order ARMA to model the non-linear and time-varying relationship between the resource allocation and its normalized performance.”
Resource Management in Clouds: Survey and Research Challenges (2015, methodology): “[168] who use an ARMA filter in modeling the relationship between an application’s resource allocations and its normalized performance, under the assumption that such a linear model provides a reasonable local approximation of the actual non-linear, workload-dependent relationship in a virtualized e”
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].”
Cloud computing: state-of-the-art and research challenges (2010, background): “For server resources that are shared among VMs, such as bandwidth, memory cache and disk I/O, maximally consolidating a server may result in resource congestion when a VM changes its footprint on the server [38].”
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].”
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.”
Performance Isolation and Fairness for Multi-Tenant Cloud Storage (2012, unknown): “Autocontrol [23] and Mesos [15] allocate per-node CPU and memory to schedule batch jobs and VMs, using utility-driven optimization and dominant resource fairness, respectively.”