FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework

Abstract

Recent advances in the areas of Internet of Things (IoT), Big Data, and Machine Learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as smart devices prevail more in our daily life. Ensuring Quality of Service (QoS) for delay-sensitive applications is a must, and fog computing is seen as one of the primary enablers for satisfying such tight QoS requirements, as it puts compute, storage, and networking resources closer to the user.In this paper, we first introduce FOGPLAN, a framework for QoS-aware Dynamic Fog Service Provisioning (QDFSP). QDFSP concerns the dynamic deployment of application services on fog nodes, or the release of application services that have previously been deployed on fog nodes, in order to meet low latency and QoS requirements of applications while minimizing cost. FOGPLAN framework is practical and operates with no assumptions and minimal information about IoT nodes. Next, we present a possible formulation (as an optimization problem) and two efficient greedy algorithms for addressing the QDFSP at one instance of time. Finally, the FOGPLAN framework is evaluated using a simulation based on real-world traffic traces.

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

“…In reality, most user requests are one of a finite number of services, and the potential computation saving by not redeploying matters to the service provider. Similarly, [10] has similar considerations. Again, an optimization problem considering both service placement, and task selection is crafted and then approximated through a novel algorithm.…”

Service Placement in Fog Computing Using Constraint Programming

“…This scenario considers monolithic applications (e.g. data) [6,10]. In [10], the authors investigate the problem of IoT data placement in a Fog infrastructure.…”

“…In [6], Yousefpour et al study the dynamic Fog service provisioning problem. The study aims to dynamically provisioning IoT applications on the fog nodes, to comply with the QoS terms (delay threshold, penalties, etc.)…”

“…investigating this challenge for a couple of years [6][7][8][9][10][11], we claim that proposed approaches are not generic enough to deal with all applications' specifics that Fog infrastructures would have to host. Current algorithms rely mainly or Integer Linear Programming (ILP) or heuristic approaches that are not easily expandable (i.e.…”

Load Aware Provisioning of IoT Services on Fog Computing Platform

“…This work is later extended in [14], where a genetic algorithm is proposed to solve the optimization problem. In [15], the provisioning problem is also modelled as an ILP. However, the objective function consists in minimizing the cost.…”