Research Area: Cloud Computing
In the age of big data, software-as-a-service (SaaS) clouds provide heterogeneous and multitenant utilization of underlying virtual environments in data centers. Real-time and parallel deployment of applications with data-intensive workloads of various sizes pose challenges in optimal resource scheduling, power utilization, task completion time, network latency, and so on, causing degradation in the quality of service and affecting the user experience. In this paper, we investigate the role of application profiles in addressing the tradeoff between performance and energy efficiency of small- to medium-scale data centers. A power-aware framework for efficient placement of application workloads in the data center is proposed. The framework considers various application workflow constraints, such as CPU, memory, network I/O, and power consumption requirements to develop realistic profiles of application workloads. A system model for the efficient workflow assignment in the data center using a novel scheduler algorithm is presented. The performance of the proposed scheduler is validated through simulation studies. We compare the proposed scheduler with two scheduling algorithms: robust time cost (RTC) and heterogeneous earliest finish time (HEFT). Results show that the proposed scheduler is 19% and 38% more energy efficient than RTC and HEFT, respectively for medium–large sized workloads.
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Author(s) Name: Basit Qureshi
Journal name: Future Generation Computer Systems
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Publisher name: ELSEVIER
DOI: 10.1016/j.future.2018.11.010
Volume Information: Volume 94, May 2019, Pages 453-467
