Metaheuristics-Based Planning and Optimization for SLA-Aware Resource Management in PaaS Clouds

The Platform as a Service (PaaS) model of Cloud Computing has emerged as an enabling yet disruptive paradigm for accelerated development of applications on the Cloud. PaaS hides administration complexities of the underlying infrastructure such as the physical or virtual machines. This abstraction is achieved through advanced automation and OS-level multi-tenant containers. However, the on-demand procurement, unpredictable workloads and auto-scaling result in rapid increase and decrease of containers. This causes undesired utilization of Cloud resources and energy wastage that can be avoided with real time planning. Hence, the main challenge of a PaaS Cloud provider is to regularly plan and optimize the placement of containers on Cloud machines. However, the service-driven constraints regarding containers and spatial constraints regarding machines make SLA-aware resource allocation non-trivial. This relatively novel "Service Consolidation" problem is a variant of multi-dimensional bin-packing and hence NP-hard. In this work, we concretely frame this problem by leveraging the definition of Machine Reassignment model proposed by Google for the ROADEF/EURO challenge and characterize it for Open Shift PaaS. We apply Metaheuristic search to discover best (re) allocation solutions on Clouds of varying scales. We compare four state of the art algorithms as problem properties change in datasets and evaluate their performance against a variety of metrics including objective function score, machines used, utilization, resource contention, SLA violations, migrations and energy consumption. Finally, we present a policy-led ranking of solutions to obscure the complexity of individual metrics and decide for the most preferred solution. Hence, we provide valuable insights for SLA-aware resource management in PaaS Clouds.