Dynamic estimation of CPU demand of web traffic
valuetools '06 Proceedings of the 1st international conference on Performance evaluation methodolgies and tools
A scalable application placement controller for enterprise data centers
Proceedings of the 16th international conference on World Wide Web
PowerNap: eliminating server idle power
Proceedings of the 14th international conference on Architectural support for programming languages and operating systems
Shares and utilities based power consolidation in virtualized server environments
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Dynamic optimization of power and performance for virtualized server clusters
Proceedings of the 2010 ACM Symposium on Applied Computing
Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures
ICDCS '10 Proceedings of the 2010 IEEE 30th International Conference on Distributed Computing Systems
Server workload analysis for power minimization using consolidation
USENIX'09 Proceedings of the 2009 conference on USENIX Annual technical conference
Power Consumption Prediction and Power-Aware Packing in Consolidated Environments
IEEE Transactions on Computers
A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers
IEEE Transactions on Services Computing
Dynamic resource allocation with management objectives: implementation for an OpenStack cloud
Proceedings of the 8th International Conference on Network and Service Management
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We address the problem of resource allocation in a large-scale cloud environment, which we formalize as that of dynamically optimizing a cloud configuration for green computing objectives under CPU and memory constraints. We propose a generic gossip protocol for resource allocation, which can be instantiated for specific objectives. We develop an instantiation of this generic protocol which aims at minimizing power consumption through server consolidation, while satisfying a changing load pattern. This protocol, called GRMP-Q, provides an efficient heuristic solution that performs well in most cases---in special cases it is optimal. Under overload, the protocol gives a fair allocation of CPU resources to clients. Simulation results suggest that key performance metrics do not change with increasing system size, making the resource allocation process scalable to well above 100,000 servers. Generally, the effectiveness of the protocol in achieving its objective increases with increasing memory capacity in the servers.