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Smart city article details
| Title | Efficient Load-Balancing And Container Deployment For Enhancing Latency In An Edge Computing-Based Iot Network Using Kubernetes For Orchestration |
|---|---|
| ID_Doc | 22345 |
| Authors | Mdemaya G.B.J.; Ndadji M.M.Z.; Sindjoung M.L.F.; Velempini M. |
| Year | 2024 |
| Published | International Journal of Advanced Computer Science and Applications, 15, 10 |
| DOI | http://dx.doi.org/10.14569/IJACSA.2024.01510122 |
| Abstract | Edge Computing (EC) provides computational and storage resources close to data-generating devices, and reduces end-to-end latency for communications between end-devices and the remote servers. In smart cities (SC) for example, thousands of applications are running on edge servers, and it becomes crucial to manage resource allocation and load balancing to improve data transmission throughput and reduce latency. Kubernetes (k8s) is a widely used container orchestration platform that is commonly employed for the efficient management of containerized applications in SC. However, it does not integrate well with certain EC requirements such as network-related metrics and the heterogeneity of EC clusters. Furthermore, requests are equally distributed across all replicas of an application, which may increase the time taken for processing, since in the EC environment, nodes are geographically dispersed. Several existing studies have investigated this problem, unfortunately, the proposed solutions consume a lot of node's resources in the cluster. To the best of our knowledge, none of studies considered the cluster heterogeneity when deploying applications that have different resource requirements. To address this issue, this paper proposes a new technique to deploy applications on edge servers by extending Kubernetes scheduler, and an approach to manage requests among the different nodes. The simulation results show that our solution generates better results than some of the stateof-the-art works in terms of latency. © (2024), (Science and Information Organization). All rights reserved. |
| Author Keywords | edge computing; Internet of Things; Kubernetes; Latency; load-balancing |
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