Negotiation game for joint IT and energy management in green datacenters

Abstract

As the power demand of datacenters is increasing sharply, a promising solution is to power datacenters locally by renewable energies. However, one of the main challenges when operating such green datacenters is to conciliate the intermittent power supply and the power demand. To deal with this problem, we view the green datacenter as two sub-systems, namely, Information Technology (IT) sub-system which consumes energy, and electrical sub-system which supplies energy. The objective is to find an efficient trade-off between the power demand and power supply, respecting the operational requirements of both sub-systems (i.e., the requirements on utility, or monetary gain, which includes monetary revenue and monetary cost). First, we analyze the problem by a black-box approach. In this approach, the models of the two sub-systems are unknown to each other, and the two sub-systems negotiate by exchanging their power preferences. However, we found that the black-box approach cannot guarantee stable solutions in term of execution time and generational distance (which is the distance between a solution and the Pareto front). Then we introduce a semi black-box approach, in which the two subsystems are modeled as the buyer and the supplier in a buyer-supplier negotiation game. We propose an algorithm that allows the buyer and supplier to negotiate, seeking for an efficient trade-off between the power demand and power supply. The analytical results show that the semi black-box algorithm converges to equilibrium, and these results are then confirmed by experimental results. We conduct the experiments by implementing a middleware of a datacenter powered by renewable energies. The experimental results show that the semi black-box algorithm improves significantly the stability, quality of service (QoS) and utility of the datacenter, compared to other algorithms. In term of stability, compared to the black-box algorithm, the semi black-box algorithm reduces the standard deviation of execution time and generational distance by 23 and 27 times, respectively. In term of QoS and utility, the semi black-box algorithm outperforms the algorithms that do not consider joint IT-energy management, as well as the algorithms that do not utilize a semi black-box design.

An Energy Saving Control Strategy Based on Multi-Agent Q-Learning Algorithm for Data Center

“…When an interactive job executes immediately, a batch job may be delayed in the queue until it must be executed to meet its deadline or sufficient renewable energy is available. To solve the task scheduling problems caused by an intermittent power supply, Minh-Thuyen et al [31] summarized the data center supplied by renewable energy as the information technology (IT) consumption subsystem and the electrical supply subsystem of renewable energy and abstracted them into the buyer and supplier in a negotiation game. By allowing the buyer and supplier to negotiate, a negotiation algorithm is introduced to win the game.…”

Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities

“…They propose a solution where each side optimizes its objectives, interacting through a negotiation loop to reach a joint agreement and present DATAZERO, a project based on the above idea. Thi et al [27] also focus on power management, especially green data centers. They propose a buyer-supplier negotiation game and algorithm for the problem of optimizing server operations and energy management for data centers entirely powered by renewable energy sources.…”

Combined IT and power supply infrastructure sizing for standalone green data centers

“…The overall approach leads to a robust sizing. In case the workload has more variability, a negotiation can be initiated at runtime between IT scheduling and power supply storage management to adapt the power demand (i.e., by changing the task scheduling of the current workload) and to make possible the supply of the data center demand [37].…”