Flow Control for Higher Resiliency in Wide Area Distributed Cloud Computing

Yabusaki, Hitoshi; Agetsuma, Masakuni; Nakagoe, Hiroshi; Murayama, Koichi; Osaki, H.; Hiramatsu, Masami; Kato, Takatoshi

doi:10.1109/compsacw.2013.110

Cited by 1 publication

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References 7 publications

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“…We previously have proposed a concept of wide area distributed cloud computing system that reduces response time using geographically distributed datacenters [9]; we have prototyped automated application deployment, unified management of distributed data, and a flow control mechanism.…”

Section: Introductionmentioning

confidence: 99%

Wide area tentative scaling (WATS) for quick response in distributed cloud computing

Yabusaki

Nakagoe

Murayama

et al. 2014

2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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As cloud computing is increasingly adopted by enterprises, a stringent service level agreement such as response time is required by the cloud in order to run business applications. This can be problematic because activity from geographically distributed terminals owing to globalization often increases the average response time. Federating various clouds enables to utilize datacenters in various geographical regions regardless of their servicers. The response time can be reduced by replicating the applications and related data at datacenters near the terminals by considering the factors of delay (e.g., data synchronization, distribution of multi-tier applications, and influence of other applications). However, it is unrealistic to accurately analyze all of the factors without any errors. We propose wide area tentative scaling (WATS) to improve the response time in a phased manner by repetitively replicate a part of the application and related data at other datacenters and selecting a better organization. The WATS approach is to repetitively change the organization to reduce the response time even if the analysis is incorrect, unlike mathematical-formulaebased approaches that require precise analysis. The drawback of this approach is that it consumes more computing resources due to repeating the replication. We therefore, applied Bayesian inference to search for a better organization with fewer trials. Evaluation results showed that WATS successfully reduced the response time in a phased manner.

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Section: Introductionmentioning

confidence: 99%