Multi-search-routes-based methods for minimizing makespan of homogeneous and heterogeneous resources in Cloud computing

[1]  Hayong Shin,et al.  Learning per-machine linear dispatching rule for heterogeneous multi-machines control , 2021, Int. J. Prod. Res..

[2]  Shaowei Cai,et al.  Correlation-Aware Heuristic Search for Intelligent Virtual Machine Provisioning in Cloud Systems , 2021, AAAI.

[3]  Liang Gao,et al.  An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance , 2021, Expert Syst. Appl..

[4]  Wenxia Guo,et al.  Cloud Resource Scheduling With Deep Reinforcement Learning and Imitation Learning , 2021, IEEE Internet of Things Journal.

[5]  Zhang Miao,et al.  A discrete PSO-based static load balancing algorithm for distributed simulations in a cloud environment , 2021, Future Gener. Comput. Syst..

[6]  Mohamed Elhoseny,et al.  Hybridization of firefly and Improved Multi-Objective Particle Swarm Optimization algorithm for energy efficient load balancing in Cloud Computing environments , 2020, J. Parallel Distributed Comput..

[7]  Mainak Adhikari,et al.  Multi-objective scheduling strategy for scientific workflows in cloud environment: A Firefly-based approach , 2020, Appl. Soft Comput..

[8]  F. Richard Yu,et al.  Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT With Edge Computing: A Deep Reinforcement Learning Approach , 2020, IEEE Internet of Things Journal.

[9]  Jing Zeng,et al.  Q-learning based dynamic task scheduling for energy-efficient cloud computing , 2020, Future Gener. Comput. Syst..

[10]  Qingqi Pei,et al.  Cooperative Computation Offloading and Resource Allocation for Blockchain-Enabled Mobile-Edge Computing: A Deep Reinforcement Learning Approach , 2020, IEEE Internet of Things Journal.

[11]  Chunlin Li,et al.  Neighborhood search-based job scheduling for IoT big data real-time processing in distributed edge-cloud computing environment , 2020, The Journal of Supercomputing.

[12]  Alejandro Quintero,et al.  A Tabu search approach for a virtual networks splitting strategy across multiple cloud providers , 2020, Int. J. Metaheuristics.

[13]  Jennifer S. Raj,et al.  An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm , 2020, Soft Computing.

[14]  Tongquan Wei,et al.  A Survey of Profit Optimization Techniques for Cloud Providers , 2020, ACM Comput. Surv..

[15]  V. S. Shankar Sriram,et al.  IBGSS: An Improved Binary Gravitational Search Algorithm based search strategy for QoS and ranking prediction in cloud environments , 2020, Appl. Soft Comput..

[16]  Zhao Tong,et al.  A scheduling scheme in the cloud computing environment using deep Q-learning , 2020, Inf. Sci..

[17]  Yuanjun Laili,et al.  Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment , 2020, Robotics Comput. Integr. Manuf..

[18]  Fatma A. Omara,et al.  A deep learning based framework for optimizing cloud consumer QoS-based service composition , 2020, Computing.

[19]  Albert Y. Zomaya,et al.  Heterogeneous Job Allocation Scheduler for Hadoop MapReduce Using Dynamic Grouping Integrated Neighboring Search , 2020, IEEE Transactions on Cloud Computing.

[20]  Khalid Moussaid,et al.  FACO: a hybrid fuzzy ant colony optimization algorithm for virtual machine scheduling in high-performance cloud computing , 2019, Journal of Ambient Intelligence and Humanized Computing.

[21]  Zibin Zheng,et al.  Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments , 2019, IEEE Transactions on Parallel and Distributed Systems.

[22]  Daniel Grosu,et al.  Scheduling parallel identical machines to minimize makespan: A parallel approximation algorithm , 2019, J. Parallel Distributed Comput..

[23]  Mohit Kumar,et al.  A comprehensive survey for scheduling techniques in cloud computing , 2019, J. Netw. Comput. Appl..

[24]  Mainak Adhikari,et al.  A Survey on Scheduling Strategies for Workflows in Cloud Environment and Emerging Trends , 2019, ACM Comput. Surv..

[25]  A. M. Senthil Kumar,et al.  Multi-Objective Task Scheduling Using Hybrid Genetic-Ant Colony Optimization Algorithm in Cloud Environment , 2019, Wireless Personal Communications.

[26]  Chapram Sudhakar,et al.  Energy efficient VM scheduling and routing in multi-tenant cloud data center , 2019, Sustain. Comput. Informatics Syst..

[27]  Xuyun Zhang,et al.  A computation offloading method over big data for IoT-enabled cloud-edge computing , 2019, Future Gener. Comput. Syst..

[28]  Srikumar Venugopal,et al.  Autonomic decentralized elasticity based on a reinforcement learning controller for cloud applications , 2019, Future Gener. Comput. Syst..

[29]  K. Kousalya,et al.  Amelioration of task scheduling in cloud computing using crow search algorithm , 2019, Neural Computing and Applications.

[30]  Vijayan Sugumaran,et al.  Task scheduling techniques in cloud computing: A literature survey , 2019, Future Gener. Comput. Syst..

[31]  Zhetao Li,et al.  Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing , 2019, IEEE Transactions on Mobile Computing.

[32]  Bo Yang,et al.  A dynamic ant-colony genetic algorithm for cloud service composition optimization , 2019, The International Journal of Advanced Manufacturing Technology.

[33]  Kenli Li,et al.  Profit Maximization for Cloud Brokers in Cloud Computing , 2019, IEEE Transactions on Parallel and Distributed Systems.

[34]  Ioannis Konstantinou,et al.  DERP: A Deep Reinforcement Learning Cloud System for Elastic Resource Provisioning , 2018, 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).

[35]  Yonggang Wen,et al.  Energy-Efficient Task Execution for Application as a General Topology in Mobile Cloud Computing , 2018, IEEE Transactions on Cloud Computing.

[36]  Rajkumar Buyya,et al.  On minimizing total energy consumption in the scheduling of virtual machine reservations , 2018, J. Netw. Comput. Appl..

[37]  P. Ganeshkumar,et al.  Multi-objective Task Scheduling to Minimize Energy Consumption and Makespan of Cloud Computing Using NSGA-II , 2018, Journal of Network and Systems Management.

[38]  Jun Zhang,et al.  An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing , 2018, IEEE Transactions on Evolutionary Computation.

[39]  Federico Della Croce,et al.  Longest Processing Time rule for identical parallel machines revisited , 2018, ArXiv.

[40]  Fei Wang,et al.  An Iterative Budget Algorithm for Dynamic Virtual Machine Consolidation Under Cloud Computing Environment , 2018, IEEE Transactions on Services Computing.

[41]  Ioannis Konstantinou,et al.  Elastic management of cloud applications using adaptive reinforcement learning , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[42]  Jie Xu,et al.  Reliable Computing Service in Massive-Scale Systems through Rapid Low-Cost Failover , 2017, IEEE Transactions on Services Computing.

[43]  Tommaso Melodia,et al.  The Value of Cooperation: Minimizing User Costs in Multi-Broker Mobile Cloud Computing Networks , 2017, IEEE Transactions on Cloud Computing.

[44]  Guofeng Zhu,et al.  Energy-efficient and QoS-aware model based resource consolidation in cloud data centers , 2017, Cluster Computing.

[45]  Dusit Niyato,et al.  Joint Optimization of Resource Provisioning in Cloud Computing , 2017, IEEE Transactions on Services Computing.

[46]  Qinru Qiu,et al.  A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[47]  Xiaodong Liu,et al.  A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment , 2016, Secur. Commun. Networks.

[48]  Xiaomin Zhu,et al.  A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly , 2016 .

[49]  Jun Zhang,et al.  Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches , 2015, ACM Comput. Surv..

[50]  Yu Chen,et al.  Prepartition: A new paradigm for the load balance of virtual machine reservations in data centers , 2014, 2014 IEEE International Conference on Communications (ICC).

[51]  Qin Xiong,et al.  An online parallel scheduling method with application to energy-efficiency in cloud computing , 2013, The Journal of Supercomputing.

[52]  Cheng-Zhong Xu,et al.  URL: A unified reinforcement learning approach for autonomic cloud management , 2012, J. Parallel Distributed Comput..

[53]  Vijay V. Vazirani,et al.  Approximation Algorithms , 2001, Springer Berlin Heidelberg.

[54]  T Jayasree,et al.  Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers , 2021, Concurr. Comput. Pract. Exp..

[55]  Haluk Rahmi Topcuoglu,et al.  Neural network based multi-objective evolutionary algorithm for dynamic workflow scheduling in cloud computing , 2020, Future Gener. Comput. Syst..

[56]  R. K. Jena,et al.  Multi Objective Task Scheduling in Cloud Environment Using Nested PSO Framework , 2015 .