Improving Server Re-Consolidation for Datacenters via Resource Exchange and Load Adjustment

Kaiyue Duan, Yusen Li, Gang Wang, Xiaoguang Liu

Abstract: Server re-consolidation refers to online migrating applications among servers for minimizing the number of servers used. To avoid interruptions to online services, resources (i.e., transient resources) are occupied at both original and destination servers during the migrations. Transient resources reduce the remaining capacity of servers, making server re-consolidation challenging. To tackle this problem, we leverage the previously proposed concept of resource exchange, which refers to a set of resource configurations such that an application running under any one of them has similar performance, thus these configurations are exchangeable and such a set is called exchangeable configuration set (ECS). Moreover, we adjust the load pressure of an application to change its ECS when there is no suitable configuration, and such a strategy is called load adjustment. In this paper, we propose a new server re-consolidation problem that maintains applications' QoS target and availability at the same time. We leverage a machine-learning algorithm to estimate the performance of applications, based on which the ECSs are constructed efficiently. We design a simulated annealing-based (SA) algorithm to optimally solve the scheduling plan. We evaluate the proposed approach using data from real-world latency-critical applications. The results show that the proposed approach outperforms two state-of-the-art baselines in terms of reducing the number of used servers by 10% on average and up to 29%. To our knowledge, this is the first time that resource exchange and load adjustment strategies have been applied to the server re-consolidation problem.