Adaptive Data Center Management Algorithm Based on the Cooperative Game Approach

Abstract

Recently, data centers (DCs) have become an indispensable part of modern computing infrastructures. However, DCs often consume a significant amount of energy and lead to the workload unbalance with increasing service requests. Keeping focus on this point, in this paper, we propose a novel energy-aware DC management scheme. To design an efficient DC control algorithm, the main challenge is uncertainties such as uncertain energy price and unpredictable users' demands. In response to these uncertainties, we adopt the idea of cooperative game theory, and introduce a new two-phase bargaining model to get the mutual advantage. To decide the energy price, we formulate the Stackelberg bargaining game while adapting the current system situation. To balance the workloads among DCs, the migration bargaining game is developed. These two game models are tightly coupled to achieve greater and reciprocal advantages during dynamic DC operations. The main novelty of our proposed two-phase bargaining approach is to handle comprehensively contradictory requirements for the DC management. Finally, extensive experiment results validate the efficiency of our proposed algorithm by comparing with the existing state-of-the-art DC management protocols in terms of average payoff of all DCs, system throughput and fairness among DCs.

Power Allocation and Energy Cost Minimization in Cloud Data Centers Microgrids: A Two-Stage Optimization Approach

“…However, some studies suggest that operating cloud DCs in MGs can significantly enhance energy efficiency since MGs are characterized as networks of distributed energy resources that bolster resiliency. Kim [42] considers DCs energy price uncertainties and unpredictable users' demands and formulates a novel two-phase bargaining model. At the first phase, he used Stackelberg game to decide of the dynamic energy price.…”