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As the usage of mission-critical mobile applications increases in Industry 4.0, such as smart manufacturing and self-driving cars, the cloud computing paradigm and its supporting data centers have become more crucial. However, a common practice in the cloud data center computing industry tends to supply a surfeit of computing resources mainly for a robust quality-of-service (QoS). In this paper, we propose a simple real-time algorithm which combines a power-aware job assignment policy for a centralized job dispatcher and a power- and QoS-aware dynamic speed scaling policy for each physical machine (PM). The job assignment policy is called &#x201C;Join the Least Power Consuming (LPC) Server&#x201D; that routes an incoming cloud job to a server spending minimum power upon request. The server-side adaptive speed scaling policy expedites energy efficiency and satisfies response time-associated QoS condition. We call this policy &#x201C;Minimizing Earliness (ME)&#x201D; since it manages the server speed towards finishing jobs at their deadlines as precisely as possible, reducing the earliness of job completion. The design principle of LPC-ME combination supports both energy efficiency and service quality required in cloud data centers. Numerical experiments compare the proposed algorithm&#x2019;s power consumption and response time with those of existing popular policies and demonstrate better energy efficiency with negligible degradation of service quality.

Power- and QoS-Aware Job Assignment With Dynamic Speed Scaling for Cloud Data Center Computing