Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Abstract

By increasing the number of IoT-devices, cloudcomputing faces challenges for some computation and timesensitive applications. Edge-computing has emerged to enable IoT-devices offload their computation tasks. Offloading tasks is a complex and challenging issue. We propose a comprehensive model including user, edge and cloud layers for scheduling continuous offering of services. Furthermore, we modeled the tasks of service as recurrent (repetitive) with a given frequency. The serviceplacement problem is formulated as a Mixed-Integer Linear Programming problem that aims to minimize the total delay of all services. We solve the problem with CPLEX, and proposed four fast heuristics to find near-optimal solutions. We compared the results of our proposed heuristics with the result obtained with CPLEX, in terms of problem-solving speed and accuracy, as well as resource utilization of all nodes. The results show that two of our proposed heuristics produce near-optimal solutions in a fraction of the time taken by CPLEX.Index Terms-edge computing, service placement, IoT devices, cloud computing• We propose a comprehensive model that includes all layers (user, edge, and cloud). It models three main node parameters (CPU, RAM, and network capacities), as well as the link

A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective

“…Therefore, a realistic network computing paradigm comprises cloud computing and edge computing as collaborative computation platforms. Consequently, many studies [23,41,42] consider three-tier (three-layered) network architecture composed of the cloud, the edge, and the Things layers to take advantage of the cloud and edge resources. For instance, Fizza et al [43] proposed two scheduling algorithms for autonomous vehicles that execute tasks based on latency tolerance.…”

“…In addition, a meta-heuristic task scheduling algorithm is proposed by [44] to schedule the tasks on either local, edge, or cloud processors by considering the computation power, storage capacity, and bandwidth capability. Consequently, Figure 2 illustrates the three-tier network architecture consisting of the things, edge, and cloud layer adapted from [42].…”