SummaryEnergy efficiency is a contemporary and challenging issue in geographically distributed data centers. These data centers consume significantly high energy and cast a negative impact on the energy resources and environment. To minimize the energy cost and the environmental impacts, Internet service providers use different approaches such as geographical load balancing (GLB). GLB refers to the placement of data centers in diverse geolocations to exploit variations in electricity prices with the objective to minimize the total energy cost. GLB helps to minimize the overall energy cost, achieve quality of service, and maximize resource utilization in geo‐distributed data centers by employing optimal workload distribution and resource utilization in the real time. In this paper, we summarize various optimization‐based workload distribution strategies and optimization techniques proposed in recent research works based on commonly used optimization factors such as workload type, load balancer, availability of renewable energy, energy storage, and data center server specification in geographically distributed data centers. The survey presents a systemized and a novel taxonomy of workload distribution in data centers. Moreover, we also debate various challenges and open research issues along with their possible solutions.
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.INDEX TERMS Energy efficiency in cloud computing, geographical load balancing, geographically distributed data centers, green computing, dynamic electricity prices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.