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Workload-aware live storage migratio...
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Zheng, Jie.
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Workload-aware live storage migration for clouds.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Workload-aware live storage migration for clouds./
作者:
Zheng, Jie.
面頁冊數:
55 p.
附註:
Source: Masters Abstracts International, Volume: 49-01, page: 0504.
Contained By:
Masters Abstracts International49-01.
標題:
Computer Science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1485979
ISBN:
9781124213033
Workload-aware live storage migration for clouds.
Zheng, Jie.
Workload-aware live storage migration for clouds.
- 55 p.
Source: Masters Abstracts International, Volume: 49-01, page: 0504.
Thesis (M.S.)--Rice University, 2010.
The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business. This thesis presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads.
ISBN: 9781124213033Subjects--Topical Terms:
626642
Computer Science.
Workload-aware live storage migration for clouds.
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The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business. This thesis presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads.
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