Task management in IoT-Fog-Cloud environment employing static scheduling Techniques

In a distributed computing system, there are limited resources, which needs to be utilized effectively. Then for improving QoS Fog computing paradigm is an effective way, with suitable allocations. Thus, different resource scheduling and optimization algorithms exist. However, still, there is a scope to improve bandwidth, latency, energy consumption, and total communication cost in the Fog environment. In this work investigation is done to show significance of task management in such resource constrained environment. Various heuristics and meta-heuristic algorithms are evaluated using simulations, to show the task placement and their impacts by using 5 different Montage datasets from work flow sim tool kit for Fog-Computing environment. Then QoS parameters like cost, makespan, and energy consumptions are computed for various state-of-the-art techniques like Min-max, PSO, GA, ACO, and BLA. This shows the behaviour of these techniques with such different tasks and allocation environment configurations. Evaluated result parameters are collected and presented in the result section. This work shows the effectiveness of heuristics and meta-heuristics techniques to manage the tasks and their allocations in the Fog environment.