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PROJECT TITLE :

Correlation Modeling and Resource Optimization for Cloud Service with Fault Recovery - 2017

ABSTRACT:

Energy-efficient Cloud Computing has recently attracted much attention, where not only performance however conjointly energy consumption are necessary metrics to be thought-about for designing rational resource scheduling ways. Most of existing approaches for achieving energy efficient computing focus on connecting these 2 metrics and balancing the tradeoff between them, that but is insufficient as a result of another vital issue reliability isn't thought of. After all, each virtual machine (VM) failures and server failures inevitably interrupt execution of a cloud service, and eventually result in spending more time and consuming more energy on finishing the cloud service. Thus, reliability significantly affects service performance and energy consumption, and therefore they ought to not be handled separately. Connecting these correlated metrics is essential for creating more precise evaluation and any for developing rational cloud resource scheduling strategies. In this paper, we present a correlated modeling approach applying Semi-Markov models, the Laplace-Stieltjes transform (LST), a Bayesian approach to investigate reliability-performance (R-P) and reliability-energy (R-E) correlations for cloud services using a retrying fault recovery mechanism. A recursive technique is also proposed for modeling the correlations for cloud services using a check-pointing fault recovery mechanism. The proposed correlation models will be used to calculate the expected service time and energy consumption for completing a cloud service. Moreover, the models can contribute to analyzing the expected performance-energy tradeoff. We formulate the expected performance-energy optimization drawback by describing performance and energy consumption metrics as functions of assigned CPU frequencies. Finally, we have a tendency to use a derivation approach to determine Pareto optimal solutions for the formulated optimization downside. Illustrative examples are provided.


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