Online Electricity Cost Saving Algorithms for Co-Location Data Centers

Abstract

This work studies the online electricity cost minimization problem at a co-location data center. A co-location data center serves multiple tenants who rent the physical infrastructure within the data center to run their respective cloud computing services. Consequently, the co-location operator has no direct control over power consumption of its tenants, and an efficient mechanism is desired for eliciting desirable consumption patterns from the co-location tenants. Electricity billing faced by a data center is nowadays based on both the total volume consumed and the peak consumption rate. This leads to an interesting new combinatorial optimization structure on the electricity cost optimization problem, which also exhibits an online nature due to the definition of peak consumption. We model and solve the problem through two approaches: the pricing approach and the auction approach. For the former, we design an offline 2-approximation algorithm as well as an online algorithm with a small competitive ratio in most practical settings. For the latter, we design an efficient (2+ c )-competitive online algorithm, where c is a system dependent parameter close to 1.49, and then convert it into an efficient mechanism that executes in an online fashion, runs in polynomial time, and guarantees truthful bidding and (2+2 c )-competitive in social cost.

An Emergency Demand Response Mechanism for Cloud Computing

“…For the power consumption of a server, parameter ks is set within [20,60] for CPU and within [0.2, 2] or RAM [13,7]. We assume that the data center is powered by BC Hydro with an electricity charge of ¢4.86 per kWh [18,6]. The value of ht is generated by adding randomness to it.…”