SLA-aware Stochastic Load Balancing in Dynamic Cloud Environment

As the number of enterprises dispatching their workload to the cloud has increased significantly over the last decade, service level agreements (SLAs) becoming a key element to consider for maintaining the quality of service (QoS). In order to facilitate the perseverance of service quality at a satisfactory level, clouds perform load balancing through migration of virtual machines (VMs) from overloaded physical machines (PMs). However, there are several challenges in achieving effective and efficient load balancing. First, VMs in clouds use different resources to serve a variety of applications, which results in varying levels of resource overutilization in different PMs. Second, due to the application’s time-varying heterogeneous nature of resource requirements, the PM’s resource consumption vary over time, making the profiling of resources difficult. Migration decisions in previous load balancing techniques are mostly based on deterministic resource demand estimation, which treats each resource equally and leads to inefficient migrations, causing severe SLA violations in terms of performance degradation. To address this problem, we propose a SLA-aware stochastic load balancing scheme using VM migrations, namely SLA-LB. It provides probabilistic guarantee against resource overloading, while satisfying the SLA. As opposed to previous methods, SLA-LB dynamically assigns different weights to different resources based on PM’s overload probability and effectively addresses the multidimensional resource requirement with stochastic characterization. Experimental results using PlanetLab and Google Cluster trace show that SLA-LB outperforms previous load balancing methods, i.e., RIAL, Sandpiper and CloudScale in terms of performance degradation by an average margin of 10.8%, 23.53% and 33%, respectively.