An SLA-Driven Cache Optimization Approach for Multi-tenant Application on PaaS

As multi-tenant applications spring up in clouds, more and more people advocate using Service Level Agreement (SLA) in service delivery to fit tenants' non-functional needs e.g. Response time and budget limit. However, most of the present application optimizations based on SLA focuses on virtual machine-based (VM-based) computing service, while other services such as storage and cache are often neglected. In this paper, we propose an SLA-driven application optimization for cache service to help to meet tenants' needs better and improve cost-effectiveness, which can be taken as complementary to the existing work. The proposed approach, built on top of Platform-as-a-Service (PaaS), pays attention to evicted data. It considers both tenant SLA-evaluated status and data performance when weighting the evicted data with re-cache likelihoods, and then adjusts their re-cache priorities. At the beginning of every cycle it predicts tenant status and evicted data performance for the coming cycle by Holt-Winters double exponential smoothing. Our simulation experiments demonstrate the optimization effectiveness in improving cache cost-effectiveness and satisfying tenant SLAs.