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Deng Z, Yu X. Resource Mapping Allocation Scheme in 6G Satellite Twin Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:5816. [PMID: 35957373 PMCID: PMC9371124 DOI: 10.3390/s22155816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 06/15/2023]
Abstract
The sixth generation (6G) satellite twin network is an important solution to achieve seamless global coverage of 6G. The deterministic geometric topology and the randomness of the communication behaviors of 6G networks limit the realism and transparency of cross-platform and cross-object communication, twin, and computing co-simulation networks. Meanwhile, the parallel-based serverless architecture has a high redundancy of computational resource allocation. Therefore, for the first time, we present a new hypergraph hierarchical nested kriging model, which provides theoretical analysis and modeling of integrated relationships for communication, twin, and computing. We explore the hierarchical unified characterization method which joins heterogeneous topologies. A basis function matrix for local flexible connectivity of the global network is designed for the connection of huge heterogeneous systems to decouple the resource mapping among heterogeneous networks. To improve the efficiency of resource allocation in communication, twin, and computing integrated network, a multi-constraint multi-objective genetic algorithm (MMGA) based on the common requirements of operations, storage, interaction, and multi-layer optimal solution conflict is proposed for the first time. The effectiveness of the algorithm and architecture is verified through simulation and testing.
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Abstract
With the rapid development of new-generation information technologies such as big data, cloud computing, Internet of Things, and mobile internet in traditional manufacturing, the development of intelligent manufacturing (IM) is accelerating. Digital twin is an important method to achieve the goal of IM, and provides an effective means for the integrated development of design and manufacturing (R & M). In view of the problems of long installation and debugging cycles, and process parameters requiring multiple trial and error in the research and development (R & D) process of laser melting deposition (LMD) equipment, this paper focuses on building an LMD equipment model based on digital twin technology. It involves performing virtual assembly, motion setting, collision inspection, and PLC debugging, thereby providing an innovative method and insights for improving the R & D efficiency of the IM of LMD equipment.
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Raipurkar AR, Chandak MB. Optimized execution method for queries with materialized views: Design and implementation. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-202821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
A query application for On-Line Analytical Processing (OLAP) examines various kinds of data stored in a Data Warehouse (DW). There have been no systematic studies that look at the impact of query optimizations on performance and energy consumption in relational and NoSQL databases. Indeed, due to a lack of precise power calculation techniques in various databases and queries, the energy activity of several basic database operations is mostly unknown, as are the queries themselves, which are very complicated, extensive, and exploratory. As a result of the rapidly growing size of the DW system, query response times are regularly increasing. To improve decision-making performance, the response time of such queries should be as short as possible. To resolve these issues, multiple materialized views from individual database tables have been collected, and queries have been handled. Similarly, due to overall maintenance and storage expenses, as well as the selection of an optimal view set to increase the data storage facility’s efficacy, materializing all conceivable views is not viable. Thus, to overcome these issues, this paper proposed the method of energy-aware query optimization and processing, on materialized views using enhanced simulated annealing (EAQO-ESA). This work was carried out in four stages. First, a Simulated Annealing (SA) based meta-heuristic approach was used to pre-process the query and optimize the scheduling performance. Second, the optimal sets of views were materialized, resulting in enhanced query response efficiency. Third, the authors assessed the performance of the query execution time and computational complexity with and without optimization. Finally, based on processing time, efficiency, and computing cost, the system’s performance was validated and compared to the traditional technique.
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Affiliation(s)
| | - Manoj B. Chandak
- ShriRamdeobaba College of Engineering and Management, Gittikhadan, Nagpur
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An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud. ALGORITHMS 2021. [DOI: 10.3390/a14070200] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numerical optimisation problems. This algorithm imitates the symbiotic associations (mutualism, commensalism, and parasitism stages) displayed by organisms in an ecosystem. Despite the improvements made with the discrete symbiotic organism search (DSOS) algorithm, it still becomes trapped in local optima due to the large size of the values of the makespan and response time. The local search space of the DSOS is diversified by substituting the best value with any candidate in the population at the mutualism phase of the DSOS algorithm, which makes it worthy for use in task scheduling problems in the cloud. Thus, the eDSOS strategy converges faster when the search space is larger or more prominent due to diversification. The CloudSim simulator was used to conduct the experiment, and the simulation results show that the proposed eDSOS was able to produce a solution with a good quality when compared with that of the DSOS. Lastly, we analysed the proposed strategy by using a two-sample t-test, which revealed that the performance of eDSOS was of significance compared to the benchmark strategy (DSOS), particularly for large search spaces. The percentage improvements were 26.23% for the makespan and 63.34% for the response time.
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Resource provisioning in scalable cloud using bio-inspired artificial neural network model. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106876] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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6
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Aygun B, Gunel Kilic B, Arici N, Cosar A, Tuncsiper B. Application of binary PSO for public cloud resources allocation system of video on demand (VoD) services. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106870] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Evolutionary many-objective assembly of cloud services via angle and adversarial direction driven search. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.10.054] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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8
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Yang Y, Yang B, Wang S, Jin T, Li S. An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.106003] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Agarwal M, Srivastava GMS. A PSO Algorithm Based Task Scheduling in Cloud Computing. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2019. [DOI: 10.4018/ijamc.2019100101] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.
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Anusooya G, Vijayakumar V, Narayanan VN. Reducing the carbon emission by early prediction of peak time load in a data center. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019. [DOI: 10.3233/jifs-169990] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- G. Anusooya
- School of Computing Science and Engineering, Vellore Institute Technology University, Chennai, Tamil Nadu, India
| | - V. Vijayakumar
- School of Computing Science and Engineering, Vellore Institute Technology University, Chennai, Tamil Nadu, India
| | - V. Neela Narayanan
- School of Computing Science and Engineering, Vellore Institute Technology University, Chennai, Tamil Nadu, India
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Zhou J, Yao X, Lin Y, Chan FT, Li Y. An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.05.009] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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13
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Energy efficient and QoS-aware routing protocol for wireless sensor network-based smart grid applications in the context of industry 4.0. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.07.045] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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14
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Energy-efficient application assignment in profile-based data center management through a Repairing Genetic Algorithm. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.03.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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15
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Xingbo J, Ju W, Dali W, Chunsheng F. The research on meta-job scheduling heuristics in heterogeneous environments. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jin Xingbo
- College of Computer Science and Technology, Jilin University, Changchun, China
- The People’s Liberation Army, Huludao, China
| | - Wang Ju
- Department of Environmental Science, Jilin University, Changchun, China
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, China
| | - Wang Dali
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Fang Chunsheng
- Department of Environmental Science, Jilin University, Changchun, China
- Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, China
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An Efficient and Energy-Aware Cloud Consolidation Algorithm for Multimedia Big Data Applications. Symmetry (Basel) 2017. [DOI: 10.3390/sym9090184] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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17
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Zhou J, Yao X. Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.03.017] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Singh P, Dutta M, Aggarwal N. A review of task scheduling based on meta-heuristics approach in cloud computing. Knowl Inf Syst 2017. [DOI: 10.1007/s10115-017-1044-2] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Reducing energy usage in drive storage clusters through intelligent allocation of incoming commands. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2016.10.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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20
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Khanmirza E, Esmaeilzadeh A, Markazi AHD. Predictive control of a building hybrid heating system for energy cost reduction. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.05.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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22
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Yao G, Ding Y, Jin Y, Hao K. Endocrine-based coevolutionary multi-swarm for multi-objective workflow scheduling in a cloud system. Soft comput 2016. [DOI: 10.1007/s00500-016-2063-8] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Kaur N, Singh S. A Budget-constrained Time and Reliability Optimization BAT Algorithm for Scheduling Workflow Applications in Clouds. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.procs.2016.09.032] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Xu J, Lin WC, Wu J, Cheng SR, Wang ZL, Wu* CC. Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration. INT J COMPUT INT SYS 2016. [DOI: 10.1080/18756891.2016.1256572] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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25
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Kalra M, Singh S. A review of metaheuristic scheduling techniques in cloud computing. EGYPTIAN INFORMATICS JOURNAL 2015. [DOI: 10.1016/j.eij.2015.07.001] [Citation(s) in RCA: 125] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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26
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Dokeroglu T, Bayir MA, Cosar A. Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.01.026] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Guzek M, Bouvry P, Talbi EG. A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article]. IEEE COMPUT INTELL M 2015. [DOI: 10.1109/mci.2015.2405351] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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28
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Jafari Navimipour N, Sharifi Milani F. Task Scheduling in the Cloud Computing Based on the Cuckoo Search Algorithm. ACTA ACUST UNITED AC 2015. [DOI: 10.7763/ijmo.2015.v5.434] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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