Swarm Intelligence Optimization for Distributed Cloud Workloads
DOI:
https://doi.org/10.15662/IJEETR.2025.0704018Keywords:
Swarm Intelligence, Cloud Computing, Distributed Workloads, Workload Scheduling, Load Balancing, Resource Optimization, Particle Swarm OptimizationAbstract
Today with Cloud Computing, however, workload management is more complicated as they are all heterogeneous (data centre, edge nodes and virtual resources). To minimize the execution time, energy consumption, service delay and resource imbalance, an efficient workload allocation is needed. Later, a research paper titled “Swarm Intelligence Optimization for Distributed Cloud Workloads.” claims that the framework based on the swarm intelligence for workload scheduling in distributed cloud system is missing. There are three components to it: Intelligent Task Profiling, Resource Monitoring and the Swarm based decision making and adaptive workload migration. The ants behavior, group decision making of birds and bee group to achieve near optimal resource task mapping, inspired optimization algorithm like Particle Swarm Optimization algorithm, Ant Colony Optimization algorithm and Artificial Bee Colony algorithm are the motivations of the proposed model. The features of the workloads (e.g., size of the tasks, priority, execution time and resource requirement) are the first ones collected through the framework. It then scans the cloud and finds out the available resources in the cloud based on the processing, memory, bandwidth, energy and load. To make the allocation decision, a swarm intelligence optimizer and a feedback module are used to ensure that the different scheduling policies are used with the various workload conditions. Analysis of the research results shows that it is possible to increase the scalability, fault tolerance, balancing and quality of service of the distributed system of clouds with the help of swarm optimization. Considering the highly unpredictable and dynamic nature of the cloud environment, the proposed framework is more flexible as compared to the static and heuristic scheduling.
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