Container deployment strategy for edge networking

Show full item record



Permalink

http://hdl.handle.net/10138/340347

Citation

Wong , W , Zavodovski , A , Zhou , P & Kangasharju , J 2019 , Container deployment strategy for edge networking . in R Martins , H M C Paulino & L Veiga (eds) , MECC '19 : Proceedings of the 4th Workshop on Middleware for Edge Clouds & Cloudlets . ACM , pp. 1-6 , Workshop on Middleware for Edge Clouds & Cloudlets , Davis , California , United States , 09/12/2019 . https://doi.org/10.1145/3366614.3368101

Title: Container deployment strategy for edge networking
Author: Wong, W.; Zavodovski, A.; Zhou, P.; Kangasharju, J.
Other contributor: Martins, Rolando
Paulino, Hervé Miguel Cordeiro
Veiga, Luís
Contributor organization: Department of Computer Science
Doctoral Programme in Computer Science
Kasvipatologia, Valkonen Jari (-2009)
Helsinki Institute for Information Technology
Collaborative Networking research group / Jussi Kangasharju
Publisher: ACM
Date: 2019
Language: eng
Number of pages: 6
Belongs to series: MECC '19
ISBN: 978-1-4503-7032-5
DOI: https://doi.org/10.1145/3366614.3368101
URI: http://hdl.handle.net/10138/340347
Abstract: Edge computing paradigm has been proposed to support latency-sensitive applications such as Augmented Reality (AR)/ Virtual Reality(VR) and online gaming, by placing computing resources close to where they are most demanded, at the edge of the network. Many solutions have proposed to deploy virtual resources as close as possible to the consumers using virtual machines and containers. However, the most popular container orchestration tools, e.g., Docker Swarm and Kubernetes, do not take into account the locality aspect during deployment, resulting in poor location choices at the edge of the network. In this paper, we propose an edge deployment strategy to tackle the lack of locality awareness of the container orchestrator. In this strategy, the orchestrator collects information about latency and the real-time resource consumption from the current container deployments, providing a bird’s-eye view of the most demanded locations and the best places for deployment to cover the largest number of clients. We evaluated the proposed model using 16 AWS regions across the globe and compared to the standard deployment strategies. The experimental results show our edge strategy reduces the average latency between serving container to the clients by up to 4 times compared to the standard deployment algorithms. © 2019 Association for Computing Machinery.
Description: Conference code: 156753 Cited By :2 Export Date: 1 February 2021 References: AlertManager, , https://prometheus.io/docs/alerting/alertmanager/, Accessed: 2019-01-30; Docker Swarm Mode Overview, , https://docs.docker.com/engine/swarm/, Accessed: 2019-01-30; Google cAdvisor, , https://github.com/google/cadvisor, Accessed: 2019-01-30; Prometheus - Monitoring System & Time Series Database, , https://prometheus.io, Accessed: 2019-01-30; The Kubernetes Scheduler, , https://kubernetes.io/docs/reference/command-line-tools-reference/kube-scheduler/, Accessed: 2019-01-30; (2018) Ericsson Mobility Report, , https://www.ericsson.com/assets/local/mobility-report/documents/2018/ericsson-mobilityreport-june-2018.pdf, Technical Report; Balan, R., Flinn, J., Satyanarayanan, M., Sinnamohideen, S., Yang, H.-I., The case for cyber foraging (2002) Proceedings of the 10th Workshop on ACM SIGOPS European Workshop (EW 10), pp. 87-92. , https://doi.org/10.1145/1133373.1133390, ACM, New York, NY, USA; Gordon, M.S., Anoushe Jamshidi, D., Mahlke, S., Mao, Z.M., Chen, X., CoMET: Code offload by migrating execution transparently (2012) Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation (OSDI’12), pp. 93-106. , http://dl.acm.org/citation.cfm?id=2387880.2387890, USENIX Association, Berkeley, CA, USA; Habak, K., Ammar, M., Harras, K.A., Zegura, E., Femto clouds: Leveraging mobile devices to provide cloud service at the edge (2015) 2015 IEEE 8th International Conference on Cloud Computing, pp. 9-16. , https://doi.org/10.1109/CLOUD.2015.12; Hindman, B., Konwinski, A., Zaharia, M., Ghodsi, A., Joseph, A.D., Katz, R., Shenker, S., Stoica, I., Mesos: A Platform for Fine-grained Resource Sharing in the Data Center (2011) Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation (NSDI’11), pp. 295-308. , http://dl.acm.org/citation.cfm?id=1972457.1972488, USENIX Association, Berkeley, CA, USA; Pahl, C., Lee, B., Containers and clusters for edge cloud architectures – A technology review (2015) 2015 3rd International Conference on Future Internet of Things and Cloud, pp. 379-386. , https://doi.org/10.1109/FiCloud.2015.35; Roughan, M., Simplifying the synthesis of internet traffic matrices (2005) SIGCOMM Comput. Commun. Rev., 35 (5), pp. 93-96. , https://doi.org/10.1145/1096536.1096551, Oct. 2005; Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N., The case for VM-based cloudlets in mobile computing (2009) IEEE Pervasive Computing, 8 (4), pp. 14-23. , https://doi.org/10.1109/MPRV.2009.82, Oct 2009; Saurez, E., Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., Incremental Deployment and Migration of Geo-distributed Situation Awareness Applications in the Fog (2016) Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS’16), pp. 258-269. , https://doi.org/10.1145/2933267.2933317, ACM, New York, NY, USA; Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., Edge computing: Vision and challenges (2016) IEEE Internet of Things Journal, 3 (5), pp. 637-646. , https://doi.org/10.1109/JIOT.2016.2579198, Oct 2016; Wu, C.-P., Suresh, M.A., Silva, D.D., Container lifecycle management for edge nodes: Poster (2017) Proceedings of the Second ACM/IEEE Symposium on Edge Computing (SEC’17), p. 2; Yi, S., Hao, Z., Qin, Z., Li, Q., Fog computing: Platform and applications (2015) 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pp. 73-78
Subject: Containers
Deployment
Edge computing
Scheduling
Augmented reality
Middleware
Virtual reality
Computing paradigm
Computing resource
Deployment algorithms
Deployment strategy
Locality awareness
Resource consumption
Sensitive application
113 Computer and information sciences
Peer reviewed: Yes
Rights: unspecified
Usage restriction: openAccess
Self-archived version: acceptedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
bricklayer.pdf 1.618Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record