A cost aware topology formation scheme for latency sensitive applications in edge infrastructure-as-a-service paradigm. (March 2022)

• Record Type: Journal Article
• Authors: Gauttam, Himanshu; Pattanaik, K.K.; Bhadauria, Saumya; Saxena, Divya; Sapna,
• Is Part Of: Journal of network and computer applications. Volume 199(2022)
• Publication Date: 2022-03
• Subjects: IoT -- Edge computing -- EIaaS paradigm -- Topology formation -- Edge resource utilization; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2021.103303
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: The network topology formation in an Edge Infrastructure-as-a-Service (EIaaS) paradigm must consider the placement of Edge Computational Nodes (ECNs) so as to minimize the delay. Existing ECN placement schemes consider redundant node density, non-optimal location selection, and distance-based association, which affect the ultra-low latency requirement(s) of applications. Further, per ECN to IoT nodes association is key to efficient utilization of ECNs and delay minimization between IoT node(s) and ECN. This work proposes a Cost-aware Edge Computational Node Placement (coECNP) scheme for optimal topology formation in EIaaS paradigm with the objective of IoT nodes delay minimization. It formulates ECN placement problem as a constrained optimization problem. Each iteration in the location discovery module of coECNP identifies optimal placement location by utilizing IoT node's density on an updated set of IoT nodes and hop-distance among previous iterations' ECN locations and current candidate locations. As a result, it maximizes the number of IoT nodes that access ECN with minimum hop-distance, leading to end-to-end delay minimization. The assignment module of coECNP takes care of previously assigned nodes in each iteration before associating new IoT nodes to the nearest ECN to attain balanced mapping. Thus, it alleviates total delay from IoT node to respective ECN and enhances edge resource utilization to cater the application(s) near real-time execution requirement(s). The performance comparison indicates that coECNP achieves promising results by reducing IoT nodes delay by 23%–64%, 20%–66%, and 35%–73% on periodic, event-based, and query-based data traffic scenarios, respectively, under various network settings, compared to the benchmark solutions.