Loading [MathJax]/extensions/TeX/upgreek.js
Performance Model of MapReduce Iterative Applications for Hybrid Cloud Bursting | IEEE Journals & Magazine | IEEE Xplore
Scheduled Maintenance: On Monday, 27 January, the IEEE Xplore Author Profile management portal will undergo scheduled maintenance from 9:00-11:00 AM ET (1400-1600 UTC). During this time, access to the portal will be unavailable. We apologize for any inconvenience.

Performance Model of MapReduce Iterative Applications for Hybrid Cloud Bursting


Abstract:

Hybrid cloud bursting (i.e., leasing temporary off-premise cloud resources to boost the overall capacity during peak utilization) can be a cost-effective way to deal with...Show More

Abstract:

Hybrid cloud bursting (i.e., leasing temporary off-premise cloud resources to boost the overall capacity during peak utilization) can be a cost-effective way to deal with the increasing complexity of big data analytics, especially for iterative applications. However, the low throughput, high latency network link between the on-premise and off-premise resources (“weak link”) makes maintaining scalability difficult. While several data locality techniques have been designed for big data bursting on hybrid clouds, their effectiveness is difficult to estimate in advance. Yet such estimations are critical, because they help users decide whether the extra pay-as-you-go cost incurred by using the off-premise resources justifies the runtime speed-up. To this end, the current paper presents a performance model and methodology to estimate the runtime of iterative MapReduce applications in a hybrid cloud-bursting scenario. The paper focuses on the overhead incurred by the weak link at fine granularity, for both the map and the reduce phases. This approach enables high estimation accuracy, as demonstrated by extensive experiments at scale using a mix of real-world iterative MapReduce applications from standard big data benchmarking suites that cover a broad spectrum of data patterns. Not only are the produced estimations accurate in absolute terms compared with experimental results, but they are also up to an order of magnitude more accurate than applying state-of-art estimation approaches originally designed for single-site MapReduce deployments.
Published in: IEEE Transactions on Parallel and Distributed Systems ( Volume: 29, Issue: 8, 01 August 2018)
Page(s): 1794 - 1807
Date of Publication: 06 February 2018

ISSN Information:

Funding Agency:


References

References is not available for this document.