A Whale Optimization Algorithm Based Resource Allocation Scheme for Cloud-Fog Based IoT Applications

Fog computing has been prioritized over cloud in terms of latency-sensitive Internet Things (IoT) based services. We consider a limited resource-based fog system where real-time tasks with heterogeneous resource configurations are required to allocate within the execution deadline. Two modules designed handle continuous streaming tasks. The first module is task classification and buffering (TCB), which classifies heterogeneity using dynamic fuzzy c-means clustering buffers into parallel virtual queues according enhanced least laxity time. second offloading optimal allocation (TOORA), decides offload either or also optimally assigns resources nodes whale optimization algorithm, provides high throughput. simulation results our proposed called optimized (WORA), compared other models, such as shortest job (SJF), multi-objective monotone increasing sorting-based (MOMIS) Fuzzy Logic Real-time Task Scheduling (FLRTS) algorithm. When 100 700 executed 15 nodes, show that WORA algorithm saves 10.3% average cost MOMIS 21.9% FLRTS. comparing energy consumption, consumes 18.5% less than 30.8% performed 6.4% better 12.9% FLRTS makespan 2.6% 4.3% successful completion