A Hierarchical Resource Allocation and Consolidation Framework in a Multi-Core Server Cluster Using a Markov Decision Process Model
Abstract
This paper investigates a service level agreements (SLAs)-based resource allocation problem in a server cluster. The objective is to maximise the total profit, which is the total revenue minus the operational cost of the server cluster. The total revenue depends on the average request response time, whereas the operating cost depends on the total energy consumption of the server cluster. A joint optimisation framework is proposed, comprised of request dispatching, dynamic voltage and frequency scaling (DVFS) for individual cores of the servers, as well as server- and core-level consolidations. Each DVFS-enabled core in the server cluster is modelled by using a continuous-time Markov decision process (CTMDP). A near-optimal solution comprised of a central manager and distributed local agents is presented. Each local agent employs linear programming-based CTMDP solving method to solve the DVFS problem for the corresponding core. On the other hand, the central manager solves the request dispatch problem and finds the optimal number of ON cores and servers, thereby achieving a desirable tradeoff between service response time and power consumption. To reduce the computational overhead, a two-tier hierarchical solution is utilized. Experimental results demonstrate the outstanding performance of the proposed algorithm over the baseline algorithms.
