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

Chhabra, Amit; Sahana, Sudip Kumar; Sani, Nor Samsiah; Mohammadzadeh, Ali; Omar, Hasmila Amirah

doi:10.3390/en15134571

Cited by 16 publications

(5 citation statements)

References 83 publications

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“…For simulation results evaluation, the suggested strategy is compared to an existing technique that was implemented utilizing optimization algorithms such as whale optimization, earthworm, cuckoo search, and so on. We have compared our proposed approach with five existing techniques including h-DEWOA (hybrid-differential-evolution-enabled whaleoptimization algorithm) [38], cuckoo-search-differential algorithm (CSDEO) [39], Cuckoo-search-particle-swarmoptimization algorithm (CSPSO) [40], blacklist matrix-basedmulti-objective algorithm (BLEMO) [41], and EEOA (Electricearthworm-optimization algorithm) [10]. All comparisons are performed over 30 iterations across 10 workloads from the HPC2N and CEA-CURIE datasets.…”

Section: B Simulation Resultsmentioning

confidence: 99%

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Gaurav Goel

2023

IJRITCC

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Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation. This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%.

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Section: B Simulation Resultsmentioning

confidence: 99%

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Gaurav Goel

2023

IJRITCC

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“…The WOA-BAT hybrid algorithm emerges as a robust solution for optimizing smart grid operations, leveraging the complementary features of both the WOA and BAT. Amit et al [28] focus on solving task scheduling problems in cloud environments through a hybrid metaheuristic approach. The authors highlight the limitations in the standard WOA related to solution diversity, convergence speed, and exploration-exploitation tradeoff and propose h-DEWOA, integrating enhancements like chaotic maps for improved exploration, opposition-based learning for solution diversity, and differential evolution for enhanced exploration.…”

Section: Related Workmentioning

confidence: 99%

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

Arcas,

Cioara,

Anghel

2024

Biomimetics

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As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services’ computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

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“…It followed a taxonomy focusing on virtualization, consolidation, and energy-awareness dimensions, reviewing each technique qualitatively based on key goals, methods, advantages, and limitations. Chhabra et al (2022) introduced a novel approach termed h-DEWOA, which integrates chaotic maps, opposition-based learning (OBL), and differential evolution (DE) with the standard WOA aim to enhance exploration, convergence speed, and the balance between exploration and exploitation. Additionally, an efficient allocation heuristic improved resource assignment.…”

Section: Heuristic Approaches For Task Schedulingmentioning

confidence: 99%

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

Alsadie

2024

PeerJ Computer Science

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Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.

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

Cited by 16 publications

References 83 publications

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

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

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

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