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The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [ABSTRACT FROM AUTHOR]

With the increment in figuring advancements, Cloud computing has permitted the clients to get to assets from anyplace whenever and to pay for the assets based on pay-peruse. Cloud computing allocates the submitted tasks to virtual machine (VM) by dynamic allocation. With the bandwidth and network connectivity getting better, more number of users and organizations are moving to Cloud. All things considered, there are different difficulties that are looked by Cloud service provider. One of the most important challenges is of load balancing. With the number of users gradually increasing, load balancing has become more important. Numerous calculations have been proposed till now for load adjusting, where every one of it centres around the various boundaries. However, these techniques suffer from various issues like - stuck in native optima. None of the calculations has demonstrated to be totally proficient. To deal with the issues identified with existing meta-heuristic procedures, in this paper, a mutation based ant colony optimization load balancing algorithm is proposed. Here we focus on the mutation in Ant Colony Optimization (ACO) algorithm for efficiently balancing the load among various data centers to further develop the presentation boundaries like response time and while improving the overall fitness function. [ABSTRACT FROM AUTHOR]