Author_Institution :
Dept. of Comput. Eng., Kyung Hee Univ., Seoul, South Korea
Abstract :
Demand response programs have been considered critical for power grid reliability and efficiency. Especially, the demand response of datacenters has recently received encouraging efforts due to huge demands and flexible power control knobs of datacenters. However, most current efforts focus on owner-operated datacenters, omitting another critical segment of datacenter business: multitenant colocation. In colocation datacenters, while there exist multiple tenants who manage their own servers, the colocation operator only provides facilities such as cooling, reliable power, and network connectivity. Therefore, colocation has a unique feature that challenges any attempts to design a demand response program: uncoordinated power management among tenants. To tackle this challenge, two incentive mechanisms are proposed to coordinate tenant power consumption for demand response under two different scenarios. First, in the case of economic demand response where the operator can adjust an elastic energy reduction target, we show that there is an interaction between the operator and tenant strategies, where each side maximizes its own benefit. Hence, we apply a two-stage Stackelberg game to analyze this scenario and derive this game´s equilibria. However, computing these equilibria can be intractable with exhaustive search; therefore, we propose an algorithm to find the Stackelberg equilibria with linear complexity. Second, in the case of emergency demand response where a fixed energy reduction target must be fulfilled, we devise two incentive schemes with the distributed algorithms that can achieve the same optimal social cost. While the first algorithm is based on the dual-decomposition method that is suitable for nonstrategic tenants, the second one is designed for strategic tenants to achieve a unique Nash equilibrium of a bidding game. Finally, trace-based simulations are also provided to illustrate the efficacy of our proposed incentive schemes.
Keywords :
computational complexity; computer centres; cooling; distributed algorithms; game theory; power aware computing; power consumption; power grids; power system management; power system reliability; Nash equilibrium; Stackelberg equilibria; bidding game; colocation datacenters; colocation operator; cooling; datacenter business; demand response programs; distributed algorithms; dual-decomposition method; economic demand responses; elastic energy reduction target; emergency demand response; fixed energy reduction target; game equilibria; incentive mechanisms; incentive schemes; linear complexity; multitenant colocation; network connectivity; nonstrategic tenants; optimal social cost; owner-operated datacenters; power control knobs; power grid efficiency; power grid reliability; reliable power; tenant power consumption; trace-based simulations; two-stage Stackelberg game; uncoordinated power management; Cooling; Economics; Game theory; Green communications; Load management; Power demand; Servers; Colocation datacenters; demand response; distributed algorithms; incentive mechanisms;