SCoPe: A Decision System for Large Scale Container Provisioning Management
Abstract: Operating system (OS) containers provide a process level virtualization in a multi-tenant Cloud environment. Such containers are becoming increasingly popular in developer community as they facilitate fast development and delivery of enterprise class Cloud services. Furthermore, these containers share a common OS and hence, they have a low resource foot-print leading to reduced provisioning time. In this paper, we investigate such promise of containers while provisioning large scale 3-tier applications. First, through benchmarking, we observe that at very large scale, several application scaling factors (e.g., number of containers of an application provisioned in parallel, application load) and system state parameters (e.g., number of applications and containers running on a system) introduce variability in application provisioning time because of resource bottleneck in general, and specifically due to the OS process overhead. To address such variability, we propose a provisioning decision management system SCoPe that provides an application partitioning and provisioning strategy where we determine the maximum number of containers of every application that can be provisioned in parallel across physical machines, while meeting the service level agreement (SLA) on provisioning time and Cloud provider specific objectives (e.g., maximize consolidation of applications, minimize operating cost). This joint partitioning and provisioning problem is NP-hard and we propose a greedy heuristic solution. Using real data set and through extensive experiments, we demonstrate the performance of SCoPe for large scale container based application provisioning. Compared to other well-known heuristics, SCoPe can reduce the partitioning by 5x or more, while meeting SLAs.
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