PROJECT TITLE :

BURSE: A Bursty and Self-Similar Workload Generator for Cloud Computing

ABSTRACT:

As two of the most vital characteristics of workloads, burstiness and self-similarity are gaining a lot of and more attention. Workload generation, which is a key technique for performance analysis and simulations, has also attracted an increasing interest in cloud community in recent years. Though a large number of ways for synthetically generating bursty or self-similar workloads are proposed in the literature, none of them will accommodate workload generation with each of the 2 characteristics. During this paper, a configurable and intelligible synthetic generator (BURSE) is proposed for bursty and self-similar workloads in cloud computing based mostly on a superposition of two-state Markov Modulated Poisson Processes (MMPP2s). The proposed generator can turn out workloads with both specified intension of burstiness and self-similarity. Detailed experimental analysis demonstrates the accuracy, robustness and sensible applicability of BURSE.


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