Title :
Remediating Overload in Over-Subscribed Computing Environments
Author :
Wang, Long ; Hosn, Rafah A. ; Tang, Chunqiang
Author_Institution :
Thomas J. Watson Res. Center, IBM Corp., Hawthorne, NY, USA
Abstract :
Resource over subscription brings the risk of resource overload. This paper proposes a mechanism to remediate overload without assuming there is always resource available for migration. A work value notion is introduced to compare importance of VMs, and the overload remediation problem is formulated as a variant of Removable Online Multi-Knapsack Problem. An algorithm is proposed to solve this optimization problem. The mechanism is implemented in a large commercial Cloud environment. Experiments and model-based studies demonstrate the effectiveness of the proposed mechanism in remediating overload and its performance in maximizing work values provided by computing environments (27% higher work values than the baseline algorithm in our study).
Keywords :
cloud computing; knapsack problems; optimisation; performance evaluation; resource allocation; virtual machines; baseline algorithm; commercial cloud environment; model-based studies; optimization problem; overload remediation problem; oversubscribed computing environments; removable online multiknapsack problem; resource migration; resource overloading risk; resource oversubscription; work value maximization; work value notion; Complexity theory; Computational modeling; Memory management; Monitoring; Servers; Throughput; Virtual machine monitors; VM placement; optimization; over-commitment; over-subscription; overload; quiesce;
Conference_Titel :
Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4673-2892-0
DOI :
10.1109/CLOUD.2012.53