A Multi-Tenant Memory Management Mechanism for Cloud Data Storage

With the popularization of cloud computing, software as a service (SaaS) has become an important form of cloud computing. Memory resource owned by each data node in the cloud is a key resource to improve data access performance of multi-tenant applications. Therefore, memory resource share and provisioning have received a lot of attention from SaaS providers. For the service providers, how to reasonably allocate memory resource in each data node in order to obtain higher profits while guaranteeing tenants’ service level agreement (SLA) has become a challenge. Addressing the challenge, we propose a framework of multi-tenant memory management (MTMM) for cloud data storage and corresponding memory allocation method. The method takes the maximum profits service provider can obtain as a target. Combined with tenants’ SLA profit models, the global memory allocation problem is analyzed and modeled as an objective optimal problem. Corresponding the profits service provider can get under different memory allocation strategies are predicted through it. Considering the characteristics of multi-tenant memory allocation, we solve the problem by optimized genetic algorithm in order to improve the performance of the method. Compared with the traditional LRU method and multi-tenant memory allocation method employed in single node, the mechanism proposed in this paper can effectively manage memory and provide higher profits for service providers.