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In a cloud environment, scheduling problem as an NP-complete problem can be solved using various metaheuristic algorithms. The metaheuristic algorithms are very popular for scheduling tasks because of their effectiveness. A bacterial foraging is a swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. This paper proposes a task scheduling algorithm based on bacterial foraging optimization to reduce the idle time of virtual machines whereas the load balancing and reducing of runtime have occurred. The Cloudsim toolkit has assessed the performance of the proposed method in comparison with some scheduling algorithms. According to the obtained results, the makespan and energy consumption were reduced by using the proposed algorithm.

In this paper, we consider the contention aware task scheduling problem on a grid topology of processors. By contention awareness, we mean that simultaneous communication on a link has to be serialized. To solve this problem, we propose several nature inspired metaheuristic algorithms: Simulated Annealing (SA), Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Cuckoo Search (CS), and Firefly Algorithm (FA). We perform benchmark evaluation of these algorithms for the Normalized Schedule Length (NSL) parameter. The benchmark task graphs that we consider are: random task graphs, peer set task graphs, systolic array task graphs, Gaussian elimination task graphs, divide and conquer task graphs, and fast Fourier transform task graphs.

Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfully applied to solve task scheduling on computational Grids. In this paper, Gravitational Search Algorithm (GSA), as one of the latest population-based metaheuristic algorithms, is used for task scheduling on computational Grids. The proposed method employs GSA to find the best solution with the minimum makespan and flowtime. We evaluate this approach with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) method. The results demonstrate that the benefit of the GSA is its speed of convergence and the capability to obtain feasible schedules.