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

Abstract

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

Retracted: Minimal channel cost‐based energy‐efficient resource allocation algorithm for task offloading under FoG computing environment

“…Therefore, as the FNs rise, the network EE of all algorithms decreases. However, the proposed MiCCRA/oEeFRA algorithm decreases the network EE from 5.58 × 10 10 bit/J at F = 4 to 3.019 × 10 10 bit/J at F = 12 as compared with the OTRA algorithm 24 decrease of 3.18 × 10 9 bit/J at F = 4 to 0.919 × 10 9 bit/J at F = 12, results in improved network EE as compared with various algorithms and having decreasing EE at F = 16 of remaining existing algorithms are 0.85 × 10 5 bit/J, 0.574 × 10 5 bit/J, 6.74 × 10 6 bit/J, 3.68 × 10 8 bit/J and 4.28 × 10 7 bit/J for WORA, 25 EETOS, 17 EE‐APA, 34 WGK 33 and GKOA, 35 respectively.…”

Joint delay and energy aware dragonfly optimization‐based uplink resource allocation scheme for LTE‐A networks in a cross‐layer environment

“…The distribu- tion of the arrival rate of users is uniform with the values as in Table 2. Outcomes of the proposed scheme is compared with HORA and CRBA algorithm [20]; whale optimization algorithm (WOA)-based resource allocation scheme [25]; RSMA technique with Han-Kobayashi scheme for RA in multicell mm-wave environment [29]; amended barnacles mating optimizer-based RA algorithm(ABMO) [24] and energy-aware mode selection for D2D resource allocation using Hungarian algorithm [19]. Unit cell that are uniformly distributed MTCDs and H2H UEs are considered.…”

“…The amended barnacles mating optimizer (ABMO)- [24] and modified whale optimizer-based [25] RA algorithms, aims to improve the performance of 5G networks by enhancing the number of connected users and reducing transmit power consumption. This algorithm increases its accuracy and effectiveness by optimizing the best locations for base stations, considering the principles of green communications.…”

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

“…In the edge-fog-cloud domain, several approaches have tried to use or improve the standard WOA for resource allocation and job scheduling. Sing et al [20] apply the WOA to develop a fog computing resource allocation algorithm for IoT applications. The approach initially uses a task classification and buffering technique based on dynamic fuzzy c-means clustering to classify tasks and employ the least slack time scheduling for parallel virtual queues.…”