Singh, Amritpal and Aujla, Gagangeet Singh and Bali, Rasmeet Singh (2021) 'Container-based load balancing for energy efficiency in software-defined edge computing environment.', Sustainable Computing: Informatics and Systems, 30 . p. 100463.
The workload generated by the Internet of Things (IoT)-based infrastructure is often handled by the cloud data centers (DCs). However, in recent time, an exponential increase in the deployment of the IoT-based infrastructure has escalated the workload on the DCs. So, these DCs are not fully capable to meet the strict demand of IoT devices in regard to the lower latency as well as high data rate while provisioning IoT workloads. Therefore, to reinforce the latency-sensitive workloads, an intersection layer known as edge computing has successfully balanced the entire service provisioning landscape. In this IoT-edge-cloud ecosystem, large number of interactions and data transmissions among different layer can increase the load on underlying network infrastructure. So, software-defined edge computing has emerged as a viable solution to resolve these latency-sensitive workload issues. Additionally, energy consumption has been witnessed as a major challenge in resource-constrained edge systems. The existing solutions are not fully compatible in Software-defined Edge ecosystem for handling IoT workloads with an optimal trade-off between energy-efficiency and latency. Hence, this article proposes a lightweight and energy-efficient container-as-a-service (CaaS) approach based on the software-define edge computing to provision the workloads generated from the latency-sensitive IoT applications. A Stackelberg game is formulated for a two-period resource allocation between end-user/IoT devices and Edge devices considering the service level agreement. Furthermore, an energy-efficient ensemble for container allocation, consolidation and migration is also designed for load balancing in software-defined edge computing environment. The proposed approach is validated through a simulated environment with respect to CPU serve time, network serve time, overall delay, lastly energy consumption. The results obtained show the superiority of the proposed in comparison to the existing variants.
|Full text:||(AM) Accepted Manuscript|
Available under License - Creative Commons Attribution Non-commercial No Derivatives 4.0.
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|Publisher Web site:||https://doi.org/10.1016/j.suscom.2020.100463|
|Publisher statement:||© 2020 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/|
|Date accepted:||07 October 2020|
|Date deposited:||08 November 2021|
|Date of first online publication:||25 October 2020|
|Date first made open access:||08 November 2021|
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