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Article
Chemistry, Analytical
Sudheer Mangalampalli et al.
Summary: Task scheduling in the cloud computing paradigm is challenging due to the dynamic and heterogeneous workloads. Inappropriate task assignment leads to quality degradation and violation of SLA metrics, decreasing trust in the cloud provider. To address this, we propose an efficient task scheduling algorithm that considers task and virtual machine priorities, accurately scheduling tasks to appropriate VMs.
Article
Mathematics, Applied
Yu Lu et al.
Summary: In this paper, a linearized L 1-Galerkin finite element method is proposed to solve the nonlinear coupled time-fractional prey-predator problem. The time-space error splitting technique is utilized in the convergence analysis to derive the unconditionally optimal L-2-norm error estimate of the numerical scheme. Additionally, the unconditional superclose and superconvergence results under the bilinear finite element are deduced in detail. Numerical examples are presented to demonstrate the accuracy of the proposed FEMs and the effectiveness of the fast algorithm.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Abiodun Akinwale et al.
Journal of Computing and Information Technology
(2023)
Article
Computer Science, Information Systems
Mustafa Ibrahim Khaleel
Summary: This article proposes a dual-phase metaheuristic algorithm called CSSA-DE to minimize energy consumption in the job scheduling of Internet of Things critical services. The algorithm clusters computing nodes and selects the node with the highest Performance-to-Power Ratio as the mega cluster head. Then, it integrates the sparrow search algorithm (SSA) with the differential evolution (DE) algorithm to efficiently find appropriate task-VM combinations and reduce resource fragmentation.
INTERNET OF THINGS
(2023)
Article
Computer Science, Information Systems
Sundas Iftikhar et al.
Summary: Cloud computing is a cost-effective and scalable solution for modern technology. Cloud providers face increased costs due to the shift of resource needs to cloud-based systems. By reducing energy consumption through intelligent task scheduling algorithms, cloud providers can improve cost reduction.
INTERNET OF THINGS
(2023)
Review
Computer Science, Theory & Methods
Raj Mohan Singh et al.
Summary: This study provides a comprehensive taxonomic review and analysis of recent metaheuristic scheduling techniques in cloud and fog environments. It includes evaluation criteria, scheduling objectives, a taxonomy of scheduling algorithms, and rigorous evaluation of existing literature. The study also focuses on the performance of hybrid algorithms.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Software Engineering
Manikandan Nanjappan et al.
Summary: Cloud computing faces risks in load balancing, but these can be overcome through the use of modified canopy fuzzy c-means algorithm and particle swarm-based optimization algorithm for resource allocation and task scheduling.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Review
Computer Science, Information Systems
Aryan Rahimikhanghah et al.
Summary: Cloud computing is an emerging technology, but the delay in responding to requests has led to the emergence of fog computing as a supplementary technology. Fog computing reduces traffic and latency by bringing cloud services closer to users, improving resource scheduling efficiency and influencing user experience. Existing studies primarily focus on performance, energy efficiency, and resource utilization.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2022)
Article
Automation & Control Systems
Ibrahim Attiya et al.
Summary: This article proposes a new task scheduling method, called MRFOSSA, for optimizing the scheduling of IoT application tasks in cloud computing. This method uses a hybrid swarm intelligence approach, utilizing a modified Manta ray foraging optimization algorithm and the salp swarm algorithm, to improve local search ability and outperform other metaheuristic techniques.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Information Systems
Zhixia Zhang et al.
Summary: This paper explores task scheduling in cloud computing and presents an interval many-objective optimization model and evolutionary algorithm, which consider uncertain factors while improving scheduling efficiency and performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Nebojsa Bacanin et al.
Summary: The paper proposes an enhanced firefly algorithm for tackling workflow scheduling challenges in a cloud-edge environment. The improved algorithm shows significant enhancements in convergence speed and results' quality compared to the original firefly algorithm and other state-of-the-art metaheuristics.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Analytical
Said Nabi et al.
Summary: This paper presents an adaptive task scheduling approach based on Particle Swarm Optimization (PSO), which improves task execution time, throughput, and average resource utilization ratio (ARUR). It introduces an adaptive inertia weight strategy called Linearly Descending and Adaptive Inertia Weight (LDAIW) to achieve a better balance between local and global search. The proposed approach is compared with renowned PSO-based inertia weight strategies and other well-known meta-heuristic scheduling approaches, and the results show significant improvements in makespan, throughput, and ARUR.
Article
Computer Science, Information Systems
An-Ning Zhang et al.
Summary: In this paper, a task scheduling technique based on the APPE algorithm is proposed for intelligent resource allocation in a heterogeneous cloud environment. The algorithm improves the time taken for finding solutions by optimizing the convergent evolution of the nearest optimal solutions and adds a restart strategy to prevent local optimization. The evaluation function considers the makespan, resource cost, and load balancing degree to find the best solutions. Experimental results show that the APPE algorithm outperforms similar algorithms and achieves faster convergence and greater resource usage.
Article
Computer Science, Hardware & Architecture
Yun Wang et al.
Summary: Effective workflow scheduling is crucial for improving execution performance in cloud computing. This article introduces a new approach, VMALS, that combines ant colony optimization and list scheduling. Using variable neighborhood search, VMALS achieves better results compared to other algorithms.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Mathematics, Interdisciplinary Applications
Naveed Ahmad Khan et al.
Summary: This paper analyzes the mathematical model of a prey-predator system with immigrant prey and proposes a novel soft computing technique called LeNN-WOA-NM algorithm to solve it. The algorithm combines the function approximating ability of LeNNs, the global search ability of WOA, and the local search mechanism of Nelder-Mead algorithm. The effectiveness of the proposed algorithm is established through statistical data obtained from the study of variations on the growth rate, force of interaction, and catching rate. The efficiency of solutions obtained by LeNN-WOA-NM is validated through performance measures including absolute errors, MAD, TIC, and ENSE.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2022)
Article
Mathematics
Mohamed Abdel-Basset et al.
Summary: Task scheduling is a significant challenge in cloud computing, and metaheuristic algorithms have been used to overcome this challenge. This study introduces a new task scheduler called HDE, which improves the differential evolution algorithm to enhance scheduling performance. Comparisons with other algorithms show that HDE is the most efficient scheduling algorithm.
Article
Engineering, Multidisciplinary
S. Velliangiri et al.
Summary: Cloud computing is a highly scalable on-demand Internet-based computing service used by various working and non-working classes globally. Task scheduling, a critical application for end-users and cloud service providers, faces challenges in finding optimal resources. The Hybrid Electro Search with a genetic algorithm (HESGA) proposed in this paper combines the advantages of genetic and electro search algorithms, outperforming existing scheduling algorithms.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Computer Science, Hardware & Architecture
Mohammad Masdari et al.
JOURNAL OF SUPERCOMPUTING
(2020)
Article
Multidisciplinary Sciences
Alneel Mohammed Zain et al.
SN APPLIED SCIENCES
(2020)
Article
Computer Science, Artificial Intelligence
Mohamed Abd Elaziz et al.
KNOWLEDGE-BASED SYSTEMS
(2019)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Ebtesam Aloboud et al.
10TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2019) / THE 2ND INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40 2019) / AFFILIATED WORKSHOPS
(2019)
Article
Computer Science, Artificial Intelligence
Surafel Luleseged Tilahun
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2019)
Article
Computer Science, Artificial Intelligence
Haoran Zhang et al.
APPLIED SOFT COMPUTING
(2018)
Article
Mathematics, Interdisciplinary Applications
Yaning Li et al.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2018)
Article
Computer Science, Software Engineering
Demyana Izzat Esa et al.
INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING
(2016)
Article
Computer Science, Artificial Intelligence
Surafel Luleseged Tilahun et al.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2015)
Article
Computer Science, Software Engineering
Won Kim
JOURNAL OF OBJECT TECHNOLOGY
(2009)
