An Improved Hybrid Swarm Intelligence for Scheduling IoT Application Tasks in the Cloud

Attiya, Ibrahim; Elaziz, Mohamed Abd; Abualigah, Laith; Nguyen, Tu N.; El‐Latif, Ahmed A. Abd

doi:10.1109/tii.2022.3148288

Cited by 90 publications

(34 citation statements)

References 29 publications

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“…In a recent scheduling solution [72], a hybrid of multi-verse optimizer (MVO) and GA is combined to construct the MVO-GA approach to reduce total execution time of independent tasks of CBS problems. Similarly, we found that many recent research works involving multiple effective scheduling algorithms for executing a single BoT application are suggested, using improved ACO in [73], a hybrid of MRFO and SSA in [74], and deep-reinforcement learning (DRL) scheduler [75] to optimize different QoS parameters.…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

et al. 2022

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Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.

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Section: Related Workmentioning

confidence: 99%

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

et al. 2022

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“…If the right meta-heuristics are chosen as components of hybrid method, strengths of one approach compensates weaknesses of the other, and vice-versa. Hybrid meta-heuristics are proven as efficient optimizers and they were validated against different problems [56,59,[77][78][79].…”

Section: Motivation and Preliminariesmentioning

confidence: 99%

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Bačanin

Stoean

Živković

et al. 2022

Sensors

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There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

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“…computation offloading methods in edge computing networks, considering UAV transmission and clean energy. Attiya et al [26] suggested another task scheduler for managing IoT application tasks using a CCE. Specifically, they suggested a new hybrid swarm intelligence method, based on a modified manta ray foraging optimization (MRFO) algorithm and the salp swarm algorithm (SSA), to process the scheduling of IoT tasks in cloud computing.…”

Section: System Structure and Problem Formulationmentioning

confidence: 99%

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

et al. 2022

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Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

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An Improved Hybrid Swarm Intelligence for Scheduling IoT Application Tasks in the Cloud

Cited by 90 publications

References 29 publications

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Distributed Edge Computing for Resource Allocation in Smart Cities Based on the IoT

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