ICTF: Imperialist Competitive Algorithm-based Task Scheduling in Fog Computing

Fog computing address numerous cloud computing challenges such as high latency, low capacity and network failure.  The cloud computing infrastructure includes a large number of IoT devices with the ability to process in the cloud environment.  In fog computing, processing and storage provide on IoT devices locally instead of remote servers, therefore, fog computing is the best choice to enable IoT in order to provide efficient, faster and secure services for many users on the edge of the network.  Fog computing have a variety of challenges. One of these important challenges is resource management and task scheduling such that solving this problem has a great impact on system efficiency and service quality. In this paper, we present a task scheduling approach based on the imperialist competition algorithm namely, Imperialist Competitive Algorithm-based Task Scheduling in Fog Computing (ICTF). In the proposed method, we consider the search space as a directional graph. Assume that each task that contains a set of tasks is a graph with a root node and an end leaf node whose middle nodes are the task set. Each path in this graph that starts at the root node and ends at the leaf is a solution represented by a string. This solution is modeled as a country. Therefore, in the proposed method, the concept of country includes the tasks of a job along with the fog nodes that are assigned to these tasks. The initial population consists of a random number of these solutions. ICTF presents a cost function consisting of three important criteria for assessing the initial population of countries and determining the imperialists and colonies countries include energy, execution time and execution cost. The assimilation operation is performed on two different members of countries, namely the imperialists and colonies country, and two new types of members are created called children. The countries participating in this process are among the best countries and are selected using the cost function. In this process, the best offspring produced are passed on to the next generation, and this operation continues until the final population of the countries is obtained. The assimilation operator has different models and in this article we use the two-point assimilation operator. The name of the revolution operator used in this algorithm is the inverse of the task. This operator randomly selects two tasks belonging to a fog node and moves them together. The above operation is repeated until the population converges and reaches the final answer. We show that our proposed approach is more efficient in terms of makespan, resource utilization, energy consumption and remaining energy compared to the similar approach.