Abstract
The principle of local activity originated from electronic
circuits, but can easily translate into other non-electrical
homogeneous/heterogeneous media. Cloud resource is an example
of a locally-active device, which is the origin of complexity
in cloud scheduling system. However, most of the researchers
implicitly assume the cloud resource to be locally passive when
constructing new scheduling strategies. As a result, their research
solutions perform poorly in the complex cloud environment. In
this paper, we first study several complexity factors caused by
the locally-active cloud resource. And then we extended the
”Local Activity Principle” concept with a quantitative measurement
based on Entropy Theory. Furthermore, we classify the
scheduling system into ”Order” or ”Chaos” state with simulating
complexity in the cloud. Finally, we propose a new approach to
controlling the chaos based on resource’s Local Activity Ranking
for QoS-aware cloud scheduling and implement such idea in
Spark. Experiments demonstrate that our approach outperforms
the native Spark Fair Scheduler with server cost reduced by 23%,
average response time improved by 15% - 20% and standard
deviation of response time minimized by 30% - 45%.
Keywords—Local Activity Principle, Entropy Theory,
circuits, but can easily translate into other non-electrical
homogeneous/heterogeneous media. Cloud resource is an example
of a locally-active device, which is the origin of complexity
in cloud scheduling system. However, most of the researchers
implicitly assume the cloud resource to be locally passive when
constructing new scheduling strategies. As a result, their research
solutions perform poorly in the complex cloud environment. In
this paper, we first study several complexity factors caused by
the locally-active cloud resource. And then we extended the
”Local Activity Principle” concept with a quantitative measurement
based on Entropy Theory. Furthermore, we classify the
scheduling system into ”Order” or ”Chaos” state with simulating
complexity in the cloud. Finally, we propose a new approach to
controlling the chaos based on resource’s Local Activity Ranking
for QoS-aware cloud scheduling and implement such idea in
Spark. Experiments demonstrate that our approach outperforms
the native Spark Fair Scheduler with server cost reduced by 23%,
average response time improved by 15% - 20% and standard
deviation of response time minimized by 30% - 45%.
Keywords—Local Activity Principle, Entropy Theory,
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Services Computing (SCC) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 585-592 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-5090-2628-9 |
ISBN (Print) | 978-1-5090-2628-9 |
DOIs | |
Publication status | Published - 1 Sept 2016 |
Event | 2016 IEEE International Conference on Services Computing - San Francisco, California , United Kingdom Duration: 27 Jun 2016 → 2 Jul 2016 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7557343 |
Conference
Conference | 2016 IEEE International Conference on Services Computing |
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Abbreviated title | SCC |
Country/Territory | United Kingdom |
City | San Francisco, California |
Period | 27/06/16 → 2/07/16 |
Internet address |
Keywords
- Local Activity Principle
- Entropy Theory
- Cloud Scheduling
- Quality of Service
- complex systems
- Order and