NDLI: Cloud Offloading on Customer-Provided Resources

Content Provider	IEEE Xplore Digital Library
Author	Kunfeng Lai Hong Tang Haiyang Wang Shengyong Ding Dan Wang
Copyright Year	2013
Description	Author affiliation: Hong Kong Polytech. Univ., Hong Kong, China (Kunfeng Lai; Dan Wang) \|\| China Telecom Co. Ltd., China (Hong Tang; Shengyong Ding) \|\| Simon Fraser Univ., Burnaby, BC, Canada (Haiyang Wang)
Abstract	Cloud offloading has recently attracted a substantial amount of attention from both industry and academia. This new generation of service, beyond conventional task scheduling and management, utilizes the abundant computation capacity from the public clouds and takes it as a part of external resource on the mobile devices. In this paper, we find that the cloud offloading can potentially increase the running latency of the offloaded tasks. Our measurement shows that the cloud offloading systems, such as Gaikai, can introduce up to 400ms communication latency between the customers and the remote offloading servers. This creates a severe bottleneck to offload the delay sensitive tasks. To mitigate such a challenge, we suggest Cloud Offloading on Customer-Provided Resources, which utilizes the local resources of the customers, such as their home PCs, to minimize the latency during the task offloading. We discuss the framework design based on our commercial system SpotCloud and propose a travel-aware protocol to further address the latency problems when the customers are traveling far from their home PCs. Our model analysis and trace-based simulation indicate that the task execution latency can be reduced by 60% when using the customer-provided offloading service. It is reasonable to believe that such a framework can serve as a useful complement to the conventional cloud offloading on pubic clouds.
Starting Page	4695
Ending Page	4700
File Size	1440307
Page Count	6
File Format	PDF
ISBN	9781467359382
ISSN	15253511
e-ISBN	9781467359399
e-ISBN	9781467359375
DOI	10.1109/WCNC.2013.6555335
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Analytical models Blogs
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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