Abstract
In this paper, we investigate the problem of energy cost minimization for geographically distributed data centers with the guaranteed quality of service (i.e., service delay) under time-varying system dynamics. In order to satisfy the user demands, these data centers (DCs) consume a large amount of energy. The increasing energy cost of the DCs is a contemporary problem for the online service providers. To reduce the energy cost of the DCs, recent research studies suggest the workload distribution techniques among geo-distributed data centers by exploiting the dynamic electricity prices and an increased use of the renewable energy. In this paper, we propose a green geographical load balancing (GreenGLB) online algorithm based on the greedy algorithm design technique for the interactive and indivisible workload distribution. An indivisible workload is a sequential task, which cannot be further divided and must be assigned to a single data center. The basic idea of our algorithm is to assign the incoming workload at each time considering the current offered prices of electricity, the renewable energy levels, and respecting the given set of constraints. The experimental results based on the real-world traces illustrate the effectiveness of GreenGLB over the existing workload distribution techniques and attain a significant reduction in the energy cost of the geo-distributed DCs.
Highlights
In the development of cloud computing services, there is a growing trend towards large-scale data centers (DCs)
User requests arrive at global load balancer which is assigned to a unique DC based on multiple factors such as current electricity price, renewable energy level, utilization level of servers, and delay
We investigated a contemporary problem of minimizing total energy cost considering dynamic electricity prices, on-site renewable energy, and the number of active server
Summary
In the development of cloud computing services, there is a growing trend towards large-scale data centers (DCs). Data centers use brown energy as the main source besides on-site renewable energy sources [11] Both academia and industry have proposed various optimization techniques to address the different aspects such as mitigating electricity cost of the data centers along with guaranteeing quality of service (i.e., service delay) to end users [12]. The homogeneity of servers with respect to energy efficiency and dynamic electricity prices over geographically distributed data centers are the key ideas to shift the user requests to data center which reduces the overall energy cost [9]. We consider multiple geo-distributed DCs. User requests arrive at global load balancer (global-LB) which is assigned to a unique DC based on multiple factors such as current electricity price, renewable energy level, utilization level of servers, and delay.
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