Hierarchical Load Balancing and Clustering Technique for Home Edge Computing

Abstract

The edge computing system attracts much more attention and is expected to satisfy ultra-low response time required by emerging IoT applications. Nevertheless, as there were problems on latency such as the emerging traffic requiring very sensitive delay, a new Edge Computing system architecture, namely Home Edge Computing (HEC) supporting these real-time applications has been proposed. HEC is a threelayer architecture made up of HEC servers, which are very close to users, Multi-access Edge Computing (MEC) servers and the central cloud. This paper proposes a solution to solve the problems of latency on HEC servers caused by their limited resources. The increase in the traffic rate creates a long queue on these servers, i.e., a raise in the processing time (delay) for requests. By leveraging, based on clustering and load balancing techniques, we propose a new technique called HEC-Clustering Balance. It allows us to distribute the requests hierarchically on the HEC clusters and another focus of the architecture to avoid congestion on a HEC server to reduce the latency. The results show that HEC-Clustering Balance is more efficient than baseline clustering and load balancing techniques. Thus, compared to the HEC architecture, we reduce the processing time on the HEC servers to 19% and 73% respectively on two experimental scenarios.

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

“…These schemes, i.e., a nearest offloading algorithm and a predictive offloading algorithm, optimize the offloading decisions for each vehicle to complete its computation task, i.e., executing locally, offloading to Multi-access Edge Computing (MEC) server connected to roadside units, and offloading to remote cloud server. To reduce the processing time for IoT requests on local servers, Babou et al [41] present a hierarchical cluster-based load-balancing system. The authors propose a three-layer architecture made up of edge servers, MEC servers, and central cloud.…”

“…Despite the solid contributions in the aforementioned studies on IoT service placement, the proposed approach in this paper is distinguished as highly decentralized (Novelty 1) and is designed for scalable IoT networks. Furthermore, to the best of our knowledge [42], most of the existing resource management schemes [11], [13], [28]- [36], [40], [41] only study one objective (e.g., load-balancing, minimizing monetary cost) in the context of IoT service provisioning. In contrast, the present research studies two opposing objectives (Novelty 2) that can be extended to account for any criteria regarding the preferences of users or service providers such as energy-saving.…”

Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

“…In light of the forthcoming MEC/RAN integration, current literature includes a noteworthy amount of MEC clustering techniques that incorporate different optimization criteria, such as minimizing end-to-end delay of MEC services [26], reducing traffic congestion within the MEC cluster [27], enhancing MEC service coverage [28], or offloading core network traffic to the edge MEC nodes [29]. Given the vast literature in the area, in this work, we choose not to focus on how to form a MEC cluster.…”

“…In the sequel, we term the proposed strategy as the Bound and Bound 0/1 Multiple Knapsack Problem (BB-ZOMKP) strategy and present it in the pseudocode form of Algorithm IV-B. The proposed BB-ZOMKP strategy consist of four main parts: Initialize (steps 4-8), BaselinePlacement (steps 9-24), ContentPlacement (steps [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] and Backtrack (steps 41-56).…”

Χρονοδρομολόγηση Εργασιών Σε Περιβάλλοντα Υπολογιστικών Νεφών

“…Η ονοµασία τους πϱοκύπτει από το γεγονός ότι η µεγαλύτεϱη ανάγκη για υπολογιστική ισχύ ϐρίσκεται συνήϑως στους τελικούς κόµβους, ή αλλιώς στις ακµές του δικτύου. Η χρονοδροµολόγηση εργασιών σε τέτοια συστήµατα µποϱεί να γίνει αρκετά πολύπλοκη καθώς πϱέπει να υποστηριχθούν πολυάριθµοι ετερογενείς κόµβοι, δυναµική µετάθεση εργασιών σε συστήµατα Υπολογιστικού Νέφους όταν υπάρχει ανάγκη για περισσότερη υπολογιστική ισχύ καθώς και δυνατότητες προσαρµογής στην συνεχώς εναλλασσόµενη δοµή του δικτύου [138][139][140].…”