Profit Aware Load Balancing for Distributed Cloud Data Centers

The advent of cloud systems has spurred the emergence of an impressive assortment of Internet services. Recent pressures on enhancing the profitability by curtailing surging dollar costs on energy have posed challenges to, as well as placed a new emphasis on, designing energy-efficient request dispatching and resource management algorithms. What further adds to the design challenge is the highly diverse nature of Internet service requests in terms of Quality-of-Service (QoS) constraints and business values. Nonetheless, most of the existing job scheduling and resource management solutions are for a single type of request and are profit oblivious. They are unable to reap the benefit of multi-service profit-aware algorithm designs. In this paper, we consider a cloud service provider operating geographically distributed data centers in a multi-electricity-market environment, and propose an energy-efficient, profit-and cost-aware request dispatching and resource allocation algorithm to maximize a service provider's net profit. We formulate the net profit maximization issue as a constrained optimization problem, using a unified task model capturing multiple cloud layers (e.g., SaaS, PaaS, IaaS.) The proposed approach maximizes a service provider's net profit by judiciously distributing service requests to data centers, powering on/off an appropriate number of servers, and allocating server resources to dispatched requests. We conduct extensive experiments to validate our proposed algorithm. Results show that our proposed approach can improve a service provider's net profit significantly.