Resource efficiency in container-instance clusters
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Cloud computing providers 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 support the flexible orchestration of 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 underutilized container-instances, and a reduction in the number of applications that can be deployed, given the available resources. This paper presents a cloud-based Container Management Service(CMS) framework, which offers increased deployment density, scalability and resource efficiency for containerised applications. CMS extends the state-of-the-art by providing additional functionalities for orchestrating containerised applications by joint optimisation of sets of containerised applications and resource pool on the cloud. We evaluate 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 2.5 times faster execution times.
Awada , U & Barker , A D 2017 , Resource efficiency in container-instance clusters . in Second International Conference on Internet of Things, Data and Cloud Computing (ICC 2017) . ACM , 2nd International Conference on Internet of Things, Data and Cloud Computing (ICC 2017) , Cambridge , United Kingdom , 22-23 March . DOI: 10.1145/3018896.3056798conference
Second International Conference on Internet of Things, Data and Cloud Computing (ICC 2017)
© 2017, the Author(s). This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at dl.acm.org / https://doi.org/10.1145/3018896.3056798
This research is supported by the Amazon Web Services (AWS) Education Research Grant.
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