Decomposition-based multi-objective evolutionary algorithm for virtual machine and task joint scheduling of cloud computing in data space

Article Computer Science, Information Systems

An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty

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)

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Article Computer Science, Artificial Intelligence

A multiobjective state transition algorithm based on modified decomposition method

Xiaojun Zhou et al.

Summary: The MOSTA/D algorithm proposes a new modified Tchebycheff aggregation function based on the concept of matching degree, which adaptively selects candidate solutions better matched with weight vectors, and optimizes subproblems collaboratively to maintain population diversity. Experimental results demonstrate the competitiveness of the proposed algorithm in solving benchmark problems with complex Pareto fronts and engineering optimization problems compared to other state-of-the-art evolutionary algorithms.

APPLIED SOFT COMPUTING (2022)

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Article Computer Science, Artificial Intelligence

A classification-assisted environmental selection strategy for multiobjective optimization

Jinyuan Zhang et al.

Summary: This paper proposes a classification-assisted environmental selection (CAES) strategy to reduce the number of function evaluations in MOEAs. The solutions are divided into promising and unpromising classes, and a classifier is used to classify the offspring solutions. Only the promising offspring are evaluated, leading to a reduction in the number of function evaluations. Experimental results show that the proposed CAES strategy effectively reduces the number of function evaluations without severely degrading the search ability of the original MOEAs.

SWARM AND EVOLUTIONARY COMPUTATION (2022)

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Article Computer Science, Artificial Intelligence

Offline data -driven evolutionary optimization based on model selection

Huixiang Zhen et al.

Summary: In this paper, an offline data-driven evolutionary optimization framework based on model selection (MS-DDEO) is proposed. By constructing a model pool and designing model selection criteria, the method can effectively select suitable models for different test problems and achieve better optimization performance with lower computational cost in offline optimization.

SWARM AND EVOLUTIONARY COMPUTATION (2022)

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Article Computer Science, Information Systems

Task Scheduling in Cloud Computing Environment Using Advanced Phasmatodea Population Evolution Algorithms

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.

ELECTRONICS (2022)

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Article Computer Science, Information Systems

Multi-objective workflow scheduling based on genetic algorithm in cloud environment

Xuewen Xia et al.

Summary: In this paper, a multi-objective genetic algorithm (MOGA) is proposed and applied to optimize workflow scheduling problems under the cloud computing environment. An initialization scheduling sequence scheme is introduced to enhance search efficiency, and the longest common subsequence (LCS) is integrated into the genetic algorithm (GA) to achieve a balance between exploration and exploitation. Experimental results demonstrate that the proposed GALCS algorithm outperforms ordinary GA and other state-of-the-art algorithms in finding a better Pareto front.

INFORMATION SCIENCES (2022)

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Article Automation & Control Systems

An Effective Cooperative Co-Evolutionary Algorithm for Distributed Flowshop Group Scheduling Problems

Quan-Ke Pan et al.

Summary: This article addresses a novel scheduling problem in modern manufacturing systems and proposes a cooperative co-evolutionary algorithm to solve it. Experimental results show that the algorithm outperforms other metaheuristics in the literature.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

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Article Computer Science, Artificial Intelligence

Genetic-based optimization in fog computing: Current trends and research opportunities

Carlos Guerrero et al.

Summary: Fog computing, as a new computational paradigm, has emerged to reduce network usage and latency in the IoT. This paper provides a comprehensive review of recent research works on genetic-based fog resource optimization. The authors propose a taxonomy for optimizing fog infrastructures and classify 70 papers accordingly. They evaluate the papers based on their genetic optimization design and outline the benefits and limitations of each work. The study concludes that more research efforts are needed to address current challenges and improve the design and deployment of genetic algorithms in fog domains.

SWARM AND EVOLUTIONARY COMPUTATION (2022)

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Article Computer Science, Information Systems

TCDA: Truthful Combinatorial Double Auctions for Mobile Edge Computing in Industrial Internet of Things

Lianbo Ma et al.

Summary: This paper proposes a truthful combinatorial double auction mechanism for the mobile edge computing system, which guarantees truthfulness and budget-balance. The mechanism considers the locality characteristics of the MEC system and achieves optimal allocation and pricing using padding method and efficient pricing strategy.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

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Article Computer Science, Artificial Intelligence

Virtual machine placement strategy using cluster-based genetic algorithm

Binbin Zhang et al.

Summary: The study focuses on the issue of virtual machine live migration in self-driving systems, presenting a cluster-based genetic algorithm to address the problem. By clustering the population and reducing crossover operations, the algorithm efficiently outputs an approximation result for the bin packing problem. Experimental results demonstrate that the proposed approach outperforms traditional genetic algorithms in terms of both accuracy and efficiency.

NEUROCOMPUTING (2021)

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Article Computer Science, Artificial Intelligence

Novel distributed load balancing algorithms in cloud storage

Yogesh Gupta

Summary: Cloud storage, a type of distributed storage based on cloud computing technology, emerged to efficiently manage the rapidly expanding data in cyberspace. It acts as a repository for data storage, management, and user accessibility, aiming to balance server load, reduce response time, and leverage overall system performance.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Automation & Control Systems

Color-Coating Scheduling With a Multiobjective Evolutionary Algorithm Based on Decomposition and Dynamic Local Search

Zhiming Dong et al.

Summary: The production scheduling of color-coated steel coils is crucial for steel enterprises, and in this study, a multiobjective optimization model and a new evolutionary algorithm were proposed to address the conflicting objectives in scheduling, improving production efficiency and economic benefits.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2021)

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Review Computer Science, Artificial Intelligence

Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

Essam H. Houssein et al.

Summary: Cloud computing is a paradigm providing on-demand access to shared computing resources, with task scheduling being a critical component for service performance. Improper scheduling may lead to resource wastage or performance degradation, hence the inclusion of meta-heuristic algorithms for efficient task distribution. Research and trends in this field offer insights and guidance for future study and practice.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

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Article Computer Science, Theory & Methods

A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

Dabiah Alboaneen et al.

Summary: This article proposes a new metaheuristic method to optimize joint task scheduling and VM placement in cloud data centers, aiming to achieve better overall results in terms of minimizing execution cost, makespan, and degree imbalance while maximizing resource utilization of physical hosts. Simulation results show that optimizing joint task scheduling and VM placement leads to improved performance compared to traditional task scheduling algorithms.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2021)

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Article Computer Science, Artificial Intelligence

Two-objective robust job-shop scheduling with two problem-specific neighborhood structures

Xiaozhi Wang et al.

Summary: This paper discusses a two-objective robust job-shop scheduling problem with uncertain processing times described by discrete scenarios. Two hybrid algorithms are developed by combining NSGA-II and TS operators, with specialized neighborhood structures for discrete scenarios, which outperformed existing algorithms in computational results. The advantages of the developed hybrid algorithms for the proposed two-objective problem were verified through the comparison.

SWARM AND EVOLUTIONARY COMPUTATION (2021)

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Article Automation & Control Systems

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

Qing-Hua Zhu et al.

Summary: This work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize task completion time and total cost in a multi-cloud environment, ensuring security and reliability constraints are met.

IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2021)

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Article Computer Science, Information Systems

VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm

Kashav Ajmera et al.

Summary: A huge cloud data center faces the challenge of fulfilling customer's dynamic workload requirement while minimizing energy consumption. This paper proposes a Virtual Machine Scheduling method using Modified Clonal Selection Algorithm to achieve energy-efficient scheduling and handle dynamic workload effectively.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2021)

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Article Engineering, Industrial

Data analytics and optimization for smart industry

Lixin Tang et al.

Summary: Industrial intelligence is a core technology in the upgrading of traditional industries, using a fusion of data analytics and optimization to enhance production processes and management efficiency, solve key issues, and improve decision-making efficiency by enabling enterprises to predict and control unknown areas.

FRONTIERS OF ENGINEERING MANAGEMENT (2021)

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Review Computer Science, Information Systems

Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions

Mohammad Masdari et al.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2020)

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Memetic niching-based evolutionary algorithms for solving nonlinear equation system

Zuowen Liao et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

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A hybrid energy-Aware virtual machine placement algorithm for cloud environments

A. S. Abohamama et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

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Article Computer Science, Interdisciplinary Applications

Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing

Sahar Saeedi et al.

COMPUTERS & INDUSTRIAL ENGINEERING (2020)

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A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Xuan Chen et al.

IEEE SYSTEMS JOURNAL (2020)

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Multi-resource balance optimization for virtual machine placement in cloud data centers

Wenting Wei et al.

COMPUTERS & ELECTRICAL ENGINEERING (2020)

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Article Automation & Control Systems

Multiobjective Differential Evolution With Personal Archive and Biased Self-Adaptive Mutation Selection

Xianpeng Wang et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2020)

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Many-Objective Cloud Task Scheduling

Shaojin Geng et al.

IEEE ACCESS (2020)

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A Collaborative Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithms

Qi Kang et al.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2019)

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Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory

Najme Mansouri et al.

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An integrated algorithm for multi-agent fault-tolerant scheduling based on MOEA

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A NSGA-II Algorithm Hybridizing Local Simulated-Annealing Operators for a Bi-Criteria Robust Job-Shop Scheduling Problem Under Scenarios

Bing Wang et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2019)

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Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Shayem Saleh Alresheedi et al.

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Task scheduling and resource allocation in cloud computing using a heuristic approach

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Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes

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Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

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A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem

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An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem

Xinyu Li et al.

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS (2016)

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Greedy randomized adaptive search procedure with exterior path relinking for differential dispersion minimization

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Decomposition of a Multiobjective Optimization Problem into a Number of Simple Multiobjective Subproblems

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Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II

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Decomposition-based multi-objective evolutionary algorithm for virtual machine and task joint scheduling of cloud computing in data space

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