a minimum cost resource allocation approach for cloud data centers
DESCRIPTION
A Minimum Cost Resource Allocation Approach for Cloud Data Centers. 指導教授：王國禎 學生 ：連懷恩 國立交通大學資訊工程系 行動計算與寬頻網路實驗室. Outline. Introduction Related work Phase 1: A branch and bound algorithm Phase 2: A server/VM chain algorithm Conclusion. Introduction. - PowerPoint PPT PresentationTRANSCRIPT
Copyright © 2012, [email protected]
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A Minimum Cost Resource Allocation Approach for Cloud Data Centers
指導教授：王國禎 學生：連懷恩
國立交通大學資訊工程系行動計算與寬頻網路實驗室
Copyright © 2012, [email protected]
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Outline
• Introduction• Related work• Phase 1: A branch and bound algorithm• Phase 2: A server/VM chain algorithm• Conclusion
Copyright © 2012, [email protected]
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Introduction
• Existing approaches only consider come aspects of resource allocation problem(e.g., either for VM or for server level) in cloud data center.
• A complete resource allocation approach should include the following features: per application resource allocation, resizing on both VM and server level, transition cost, VM placement, and optimization over time domain.
• Our goal is a minimum cost (optimal) resource allocation approach in cloud data center.
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Related Work
ApproachNumber of
active serversNumber of VMs
for each appTransition cost VM placement
Optimization over time domain
Chunqiang Tang et al.
X O X O X
Norman Bobroff et al.
O X X O O
Minghong Lin et al.
O X O X O
Our proposed approach
O O O O O
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A Branch and Bound Algorithm
• We use a branch and bound algorithm to deal with the resizing problem of VMs and servers.
• B&B can lead to an optimal solution but is usually in high complexity. In our modified version, we can make it more efficiently by introducing the break-even time condition and off-line migration.
• The algorithm itself is finished, but we still need to prove a necessary condition to bound its complexity.
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A Server/VM Chain Algorithm
• A chain representation of server/VM in space-time diagram.
• We face some troubles in proving its optimality.
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Conclusion
• The most complete resource allocation approach in cloud data center so far.
• It is theoretically optimal if we succeed, however the actual results still heavily rely on the quality of prediction.
• Some important necessary condition and optimality still needs to be proven.
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Hw2 - Hadoop
1. Setup the Hadoop environment on the virtual machines inherited from Hw1.
2. Modify the code from Hw1 to a multinodes Hadoop version
3. Performance comparison between single node, multinodes.