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In this paper an algorithm has been proposed to balance the loads in a distributed computing system based on game theory which models the load balancing problem as a non-cooperative game among the users. The proposed load balancing game, which is infinite and with perfect information, aims to establish fairness both in system and user level. The optimal or near-optimal solution of the game is approximated by a genetic algorithm and an introduced hybrid population-based simulated annealing algorithm, using the concept of Nash equilibrium. Since all users responses are shown to converge to their near-optimal solution, distribution of users' jobs is "fair". Simulations demonstrate near-optimality of the proposed algorithms in terms of makespan and fairness for the proposed load balancing scheme.

Fog computing provides a distributed computing paradigm that executes interactive and distributed applications, such as the Internet of Things (IoT) applications. Large-scale scientific applications, often in the form of workflow ensembles, have a distributed and interactive nature that demands a dispersed execution environment like fog computing. However, handling a large-scale application in heterogeneous environment of fog computing requires harmonizing heterologous resources over the continuum from the IoT to the cloud. This paper investigates offloading and task allocation problems for orchestrating the resources in a fog computing environment where the IoT application is considered in the form of workflow ensembles. We called Offload-Location a mechanism which has been designed to find offloading coalition structure alongside a matching algorithm for allocating the offloaded tasks to fog/cloud resources. The introduced solution attempts to minimize the execution time and minimize the price paid to servers for executing the tasks provided that Quality of Service (QoS) requirements of the ensemble's deadline and budget are retaining. These objectives lead to maximizing the number of completed workflows of

This paper presents a toolkit for simulating resource management of scientific workflows in distributed environments with graph topol-ogy called WIDESim. WIDESim can work with all three different structures of scientific workflows: single, multiple workflows, and workflow ensembles. Also, unlike most existing network simulators, there is no constraint on the topology of the distributed environment. We have analyzed the performance of WIDESim in comparison to standard simulators and workflow management tools. The comparison indicates that WIDESim’s performance is close to existing standard simulators besides its improvements.