Author Description

With the increasing popularity of cloud computing service, IT companies are building and expanding their datacenters nationwide or worldwide. With hundreds of thousands of servers, a commercial datacenter consumes many mega-watts of power annually, and imposes significant electricity costs to its operator. In this dissertation, we focus on reducing energy cost for Internet-scale datacenters (IDCs) to promote more efficient datacenter operation. To reduce IDC energy consumption, load-aware capacity provisioning schemes have been considered where capacity can be reduced (by turning off servers or scheduling a low CPU frequency) when its load is lower. To reduce IDC energy cost, electricity price-aware load shifting is considered, where IDCs with a lower price/cost serve more load. Our work is related to the load-aware capacity provisioning and electricity price-aware load shifting. However, different from most existing work, we consider practical traffic and load models. We first consider traffic dynamics at each IDC. We consider both large time scale and small time scale traffic variation. We further explicitly differentiate the load demand. We consider both delay sensitive jobs which have strict quality of service requirements, and delay tolerant jobs. We propose joint capacity allocation and load shifting schemes for distributed IDCs with dynamic and differentiated load demand. Our work is summarized as follows. In datacenters, traffic demand varies both in large and small time scales. A datacenter with dynamic traffic often needs toover-provision active servers to meet the peak load demand, which incurs significant energy cost. In the dissertation, our first goal is to achieve an optimal tradeoff between energy efficiency and service performance over a set of distributed IDCs with dynamic demand. In particular, we consider the overload probability as the QoS metric, where overload is defined as service demand exceeding the capacity of an IDC. We require the overlo