Improving resource efficiency of container-instance clusters on clouds
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Cloud computing providers such as Amazon and Google have recently begun offering container-instances, which provide an efficient route to application deployment within a lightweight, isolated and well-defined execution environment.Cloud providers currently offer Container Service Platforms (CSPs), which orchestrate containerised applications.Existing CSP frameworks do not offer any form of intelligent resource scheduling: applications are usually scheduled individually, rather than taking a holistic view of all registered applications and available resources in the cloud. This can result in increased execution times for applications, resource wastage through underutilised container-instances, and a reduction in the number of applications that can be deployed, given the available resources.The research presented in this paper aims to extend existing systems by adding a cloud-based Container Management Service (CMS) framework that offers increased deployment density, scalability and resource efficiency. CMS provides additional functionalities for orchestrating containerised applications by joint optimisation of sets of containerised applications, and resource pool in multiple (geographical distributed) cloud regions.We evaluated CMS on a cloud-based CSP i.e., Amazon EC2 Container Management Service (ECS) and conducted extensive experiments using sets of CPU and Memory intensive containerised applications against the direct deployment strategy of Amazon ECS. The results show that CMS achieves up to 25% higher cluster utilisation, and up to 70% reduction in execution times.
Awada , U & Barker , A D 2017 , Improving resource efficiency of container-instance clusters on clouds . in 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017) . IEEE , The 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing , Madrid , Spain , 14-17 May .conference
17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017)
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