Energy-Efficient Stable and Balanced Task Scheduling in Data Centers

It is well known that load balancing in data centres can lead to unnecessary energy usage if all servers are kept active. Using dynamic server provisioning, the number of servers that serve requests can be reduced by turning off idle servers and thereby saving energy. However, such a scheme, usually increases the risk of instability of server queues. In this work, we analyze the trade-off between energy usage and stability of servers in a data center when we balance the load by dispatching arriving jobs. We propose algorithms to solve a stability and energy objective stochastic optimization problem with a high degree of flexibility to handle the trade-off between these two objectives. We consider variable size jobs to apply load balancing on selected active servers and find that the optimal solution is an NP-hard problem. We therefore develop two computationally efficient greedy and randomized approximation schemes to achieve the trade-off between these objectives. We investigate the performance of our proposed algorithms in minimizing the risk of queue length growth as well as the number of active servers needed to serve jobs, and compare it with several metrics in heterogeneous load scenarios.