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Articles de revues sur le sujet « Inter-datacenter network »

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Auteur : Grafiati
Publié le 14 mars 2023

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1

Li, Wenxin, Deke Guo, Keqiu Li, Heng Qi et Jianhui Zhang. « iDaaS : Inter-Datacenter Network as a Service ». IEEE Transactions on Parallel and Distributed Systems 29, no 7 (1 juillet 2018) : 1515–29. http://dx.doi.org/10.1109/tpds.2015.2505731.

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Hu, Si Quan, Peng Yuan Zhou et Jun Feng Wang. « The Inter-Datacenter Connection in SDN and Traditional Hybrid Network ». Advanced Materials Research 915-916 (avril 2014) : 1418–23. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1418.

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With the extensive application and continuous growth of big data, new challenges are imposed into network design. Without enough consideration of big data in traditional design, bottleneck problems will occur frequently in such IT infrastructure. SDN (Software Defined Network) is one of the solutions to conquer such problems. However, most current SDN applications are limited to pure SDN structure or internal data centers. Differently, we focus on the traffic transmission between data centers in this paper. We construct the WAN links between data centers through VPN based on SDN architecture and traditional network. Both theoretical analysis and experiment results show that SDN had advantage over traditional network. Our work also suggested SDN architecture and traditional network could coexist in the real condition.
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Huang, Haibin, Bingli Guo, Xin Li, Shan Yin, Yu Zhou et Shanguo Huang. « Crosstalk-aware virtual network embedding over inter-datacenter optical networks with few-mode fibers ». Optical Fiber Technology 39 (décembre 2017) : 70–77. http://dx.doi.org/10.1016/j.yofte.2017.09.019.

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Guo, Jiannan, et Zuqing Zhu. « When Deep Learning Meets Inter-Datacenter Optical Network Management : Advantages and Vulnerabilities ». Journal of Lightwave Technology 36, no 20 (15 octobre 2018) : 4761–73. http://dx.doi.org/10.1109/jlt.2018.2864676.

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Xuan Hejun, 宣贺君, 魏士伟 Wei Shiwei, 郭华平 Guo Huaping, 郭颂 Guo Song et 冯岩 Feng Yan. « Modeling and Optimization Algorithm for Virtual Network Function Deployment in Inter-Datacenter Elastic Optical Networks ». Laser & ; Optoelectronics Progress 56, no 21 (2019) : 210601. http://dx.doi.org/10.3788/lop56.210601.

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Liu, Yong, Qian Meng, Zhonghua Shen et Fulong Yan. « CASM : A Cost-Aware Switch Migration Strategy for Elastic Optical Inter-Datacenter Networks ». Photonics 9, no 5 (6 mai 2022) : 315. http://dx.doi.org/10.3390/photonics9050315.

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In inter-datacenter elastic optical networks, multi-controller deployment is adopted to improve the stability and scalability of the control plane. As the network scale increases, the traditional multi-controller deployment scheme ignores the dynamic characteristics of traffic, resulting in unbalanced load among multiple controllers. In response to this problem, the existing switch migration mechanism is proposed to achieve balanced distribution of control loads. However, most of the existing research work does not consider the additional cost of switch migration, and the load balancing performance of the controller is not significantly improved after switch migration. In this paper, we propose a cost-aware switch migration (CASM) strategy for controller load balancing. The proposed CASM strategy first measures the controller load through multiple performance indicators that affect the controller load, and then judges whether the controller is overloaded or underloaded based on the controller’s response time to the request message, thereby improving the load balancing performance of the controller. Additionally, when selecting the switch to be migrated, the CASM selects the optimal switch for migration based on minimizing the migration cost, thereby reducing the cost of switch migration. The performance evaluation shows that CASM significantly improves load balancing performance of controllers and reduces the migration cost compared to existing solutions.
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Potharaju, Rahul, et Navendu Jain. « An empirical analysis of intra- and inter-datacenter network failures for geo-distributed services ». ACM SIGMETRICS Performance Evaluation Review 41, no 1 (14 juin 2013) : 335–36. http://dx.doi.org/10.1145/2494232.2465749.

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Moon, Sang-Rok, Seung-Woo Lee, In-Ki Hwang, Hun-Sik Kang, Hae Young Rha et Joon Ki Lee. « Realization of real-time DSP for C-band PAM-4 transmission in inter-datacenter network ». Optics Express 28, no 2 (8 janvier 2020) : 1269. http://dx.doi.org/10.1364/oe.382194.

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Li, Yajie, Yongli Zhao, Bo Li, Xiaosong Yu, Hui Yang, Xinbo Wang et Jie Zhang. « Joint balancing of IT and spectrum resources for selecting virtualized network function in inter-datacenter elastic optical networks ». Optics Express 27, no 11 (13 mai 2019) : 15116. http://dx.doi.org/10.1364/oe.27.015116.

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Laoutaris, Nikolaos, Michael Sirivianos, Xiaoyuan Yang et Pablo Rodriguez. « Inter-datacenter bulk transfers with netstitcher ». ACM SIGCOMM Computer Communication Review 41, no 4 (22 octobre 2011) : 74–85. http://dx.doi.org/10.1145/2043164.2018446.

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Tziritas, Nikos, Samee U. Khan, Thanasis Loukopoulos, Spyros Lalis, Cheng-Zhong Xu, Keqin Li et Albert Y. Zomaya. « Online Inter-Datacenter Service Migrations ». IEEE Transactions on Cloud Computing 8, no 4 (1 octobre 2020) : 1054–68. http://dx.doi.org/10.1109/tcc.2017.2680439.

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Liu, Yuanhao, Fen Zhou, Cao Chen, Zuqing Zhu, Tao Shang et Juan-Manuel Torres-Moreno. « Disaster Protection in Inter-DataCenter Networks Leveraging Cooperative Storage ». IEEE Transactions on Network and Service Management 18, no 3 (septembre 2021) : 2598–611. http://dx.doi.org/10.1109/tnsm.2021.3089049.

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Landi, G., M. Capitani, A. Kretsis, K. Kontodimas, P. Kokkinos, D. Gallico, M. Biancani, K. Christodoulopoulos et E. Varvarigos. « Inter-Domain Optimization and Orchestration for Optical Datacenter Networks ». Journal of Optical Communications and Networking 10, no 7 (28 juin 2018) : B140. http://dx.doi.org/10.1364/jocn.10.00b140.

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Lin, Rongping, Moshe Zukerman, Gangxiang Shen et Wen-De Zhong. « Design of Light-Tree Based Optical Inter-Datacenter Networks ». Journal of Optical Communications and Networking 5, no 12 (27 novembre 2013) : 1443. http://dx.doi.org/10.1364/jocn.5.001443.

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Zhang, Liang, et Zuqing Zhu. « Spectrum-efficient anycast in elastic optical inter-datacenter networks ». Optical Switching and Networking 14 (août 2014) : 250–59. http://dx.doi.org/10.1016/j.osn.2014.05.018.

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Rauen, Zachary I., Burak Kantarci et Hussein T. Mouftah. « Resiliency versus energy sustainability in optical inter-datacenter networks ». Optical Switching and Networking 23 (janvier 2017) : 144–55. http://dx.doi.org/10.1016/j.osn.2016.06.003.

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Luo, Long, Hongfang Yu, Klaus-Tycho Foerster, Max Noormohammadpour et Stefan Schmid. « Inter-Datacenter Bulk Transfers : Trends and Challenges ». IEEE Network 34, no 5 (septembre 2020) : 240–46. http://dx.doi.org/10.1109/mnet.011.1900632.

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18

Muhammad, Ajmal, Nina Skorin-Kapov et Marija Furdek. « Manycast, Anycast, and Replica Placement in Optical Inter-Datacenter Networks ». Journal of Optical Communications and Networking 9, no 12 (29 novembre 2017) : 1161. http://dx.doi.org/10.1364/jocn.9.001161.

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19

Das, Shrayan, Kirtan Gopal Panda, Debarati Sen et Wasim Arif. « Risk‐aware last‐minute data backup in inter‐datacenter networks ». IET Networks 8, no 5 (septembre 2019) : 307–20. http://dx.doi.org/10.1049/iet-net.2018.5107.

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20

Lu, Ping, Qing Ling et Zuqing Zhu. « Maximizing Utility of Time-Constrained Emergency Backup in Inter-Datacenter Networks ». IEEE Communications Letters 20, no 5 (mai 2016) : 890–93. http://dx.doi.org/10.1109/lcomm.2016.2547427.

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21

Chen, Xiaoliang, Fan Ji, Shilin Zhu, Qinkun Bao et Zuqing Zhu. « Availability-aware service provisioning in SD-EON-based inter-datacenter networks ». Photonic Network Communications 31, no 3 (7 août 2015) : 543–49. http://dx.doi.org/10.1007/s11107-015-0542-y.

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Noormohammadpour, Mohammad, Srikanth Kandula, Cauligi S. Raghavendra et Sriram Rao. « Efficient inter-datacenter bulk transfers with mixed completion time objectives ». Computer Networks 164 (décembre 2019) : 106903. http://dx.doi.org/10.1016/j.comnet.2019.106903.

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23

Xiong, Yu, Yuanyuan Li, Bin Zhou, Ruyan Wang et George N. Rouskas. « SDN Enabled Restoration With Triggered Precomputation in Elastic Optical Inter-Datacenter Networks ». Journal of Optical Communications and Networking 10, no 1 (27 décembre 2017) : 24. http://dx.doi.org/10.1364/jocn.10.000024.

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24

Su, Sen, Yiwen Wang, Sujuan Jiang, Kai Shuang et Peng Xu. « Efficient algorithms for scheduling multiple bulk data transfers in inter-datacenter networks ». International Journal of Communication Systems 27, no 12 (8 juillet 2013) : 4144–65. http://dx.doi.org/10.1002/dac.2603.

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25

Li, Wenxin, Xiaobo Zhou, Keqiu Li, Heng Qi et Deke Guo. « TrafficShaper : Shaping Inter-Datacenter Traffic to Reduce the Transmission Cost ». IEEE/ACM Transactions on Networking 26, no 3 (juin 2018) : 1193–206. http://dx.doi.org/10.1109/tnet.2018.2817206.

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Yang, Zhenjie, Yong Cui, Xin Wang, Yadong Liu, Minming Li, Shihan Xiao et Chuming Li. « Cost-Efficient Scheduling of Bulk Transfers in Inter-Datacenter WANs ». IEEE/ACM Transactions on Networking 27, no 5 (octobre 2019) : 1973–86. http://dx.doi.org/10.1109/tnet.2019.2934896.

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27

Lin, Xiao, Weiqiang Sun, Xiaoyu Wang, Shengnan Yue, Malathi Veeraraghavan et Weisheng Hu. « Time-Space Decoupled SnF Scheduling of Bulk Transfers Across Inter-Datacenter Optical Networks ». IEEE Access 8 (2020) : 24829–46. http://dx.doi.org/10.1109/access.2020.2970542.

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28

Luo, Xiao, Chen Shi, Xue Chen, Liqian Wang et Tao Yang. « Global Optimization of All-Optical Hybrid-Casting in Inter-Datacenter Elastic Optical Networks ». IEEE Access 6 (2018) : 36530–43. http://dx.doi.org/10.1109/access.2018.2852067.

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Zhang, Liang, Tao Han et Nirwan Ansari. « Energy-Aware Virtual Machine Management in Inter-Datacenter Networks Over Elastic Optical Infrastructure ». IEEE Transactions on Green Communications and Networking 2, no 1 (mars 2018) : 305–15. http://dx.doi.org/10.1109/tgcn.2017.2771724.

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Yu, Ao, Hui Yang, Ting Xu, Baoguo Yu, Qiuyan Yao, Yajie Li, Tao Peng, Huifeng Guo, Jun Li et Jie Zhang. « Long-Term Traffic Scheduling Based on Stacked Bidirectional Recurrent Neural Networks in Inter-Datacenter Optical Networks ». IEEE Access 7 (2019) : 182296–308. http://dx.doi.org/10.1109/access.2019.2959303.

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Zhang, Yuchao, Xiaohui Nie, Junchen Jiang, Wendong Wang, Ke Xu, Youjian Zhao, Martin J. Reed, Kai Chen, Haiyang Wang et Guang Yao. « BDS+ : An Inter-Datacenter Data Replication System With Dynamic Bandwidth Separation ». IEEE/ACM Transactions on Networking 29, no 2 (avril 2021) : 918–34. http://dx.doi.org/10.1109/tnet.2021.3054924.

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32

Liu, Yong, Huaxi Gu, Fulong Yan et Nicola Calabretta. « Highly-Efficient Switch Migration for Controller Load Balancing in Elastic Optical Inter-Datacenter Networks ». IEEE Journal on Selected Areas in Communications 39, no 9 (septembre 2021) : 2748–61. http://dx.doi.org/10.1109/jsac.2021.3064664.

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33

Khatiri, Atefeh, Ghasem Mirjalily et Zhi-Quan Luo. « Balanced resource allocation for VNF service chain provisioning in inter-datacenter elastic optical networks ». Computer Networks 203 (février 2022) : 108717. http://dx.doi.org/10.1016/j.comnet.2021.108717.

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Tan, Yanxia, Rentao Gu, Yuefeng Ji, Dajiang Wang et Hongbiao Li. « Adaptability Analysis for IP Switching and Optical Switching in Geographically Distributed Inter-Datacenter Networks ». IEEE Access 6 (2018) : 56851–61. http://dx.doi.org/10.1109/access.2018.2873621.

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35

Cheng, Tracy Yingying, et Xiaohua Jia. « Delay-Sensitive Multicast in Inter-Datacenter WAN Using Compressive Latency Monitoring ». IEEE Transactions on Cloud Computing 8, no 1 (1 janvier 2020) : 86–96. http://dx.doi.org/10.1109/tcc.2017.2769080.

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Sotiriadis, Stelios, Nik Bessis et Nick Antonopoulos. « Exploring Inter-Cloud Load Balancing by Utilizing Historical Service Submission Records ». International Journal of Distributed Systems and Technologies 3, no 3 (juillet 2012) : 72–81. http://dx.doi.org/10.4018/jdst.2012070106.

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Cloud computing offers significant advantages to Internet users by deploying hosted services via bespoke service-provisioning environments. In advance, the emergence of Inter-Cloud increases the competences and opportunities of clients for a wider resource provision selection. This extends current capabilities by decoupling users from cloud providers while at the same time cloud providers offer an augmented service delivery mean. In practice, cloud users make use of their brokering component for selecting the best available resource, in terms of computational power and software licensing of a datacenter based on service level agreements for service execution. However, from the cloud perspective, the overall choice for balancing the different workloads within the Inter-Cloud is a complex decision. This article explores the performance of an Inter-Cloud to measure the utilization levels among their sub-clouds for various job submissions. With this in mind, the solution is modeled for achieving load balancing based on historical records from past service execution experiences. The record files are composed in the form of log files that keep related information about the size of the Inter-Cloud, basic specifications, and job submission parameters. Finally, the solution is integrated in a simulated setting for exploring the performance of the approach for various heavy workload submissions.
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Lin, Xiao, Shengnan Yue, Xiaoyu Wang, Weiqiang Sun, Malathi Veeraraghavan et Weisheng Hu. « Performance study of an SnF scheduling method for bulk data transfers over inter-datacenter WANs ». Optical Switching and Networking 37 (mai 2020) : 100558. http://dx.doi.org/10.1016/j.osn.2020.100558.

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Chen, Xiaoliang, Zuqing Zhu, Roberto Proietti et S. J. Ben Yoo. « On Incentive-Driven VNF Service Chaining in Inter-Datacenter Elastic Optical Networks : A Hierarchical Game-Theoretic Mechanism ». IEEE Transactions on Network and Service Management 16, no 1 (mars 2019) : 1–12. http://dx.doi.org/10.1109/tnsm.2018.2866400.

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Fang, Wenjian, Menglu Zeng, Xiahe Liu, Wei Lu et Zuqing Zhu. « Joint Spectrum and IT Resource Allocation for Efficient VNF Service Chaining in Inter-Datacenter Elastic Optical Networks ». IEEE Communications Letters 20, no 8 (août 2016) : 1539–42. http://dx.doi.org/10.1109/lcomm.2016.2580151.

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Li, Yang, Xiao Luo, Liqian Wang, Tao Yang, Xue Chen et Zhiguo Zhang. « Routing, modulation level and spectrum assignment for deadline-driven manycast requests in survivable inter-datacenter elastic optical networks ». Optical Fiber Technology 55 (mars 2020) : 102150. http://dx.doi.org/10.1016/j.yofte.2020.102150.

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Wei, Chengying, Lan Wu, Huanlin Liu, Cuilian Xiong et Yong Chen. « A spectrum-efficient algorithm based on traffic splitting and merging transmission for anycast in inter-datacenter elastic optical networks ». Photonic Network Communications 35, no 2 (2 novembre 2017) : 165–76. http://dx.doi.org/10.1007/s11107-017-0742-8.

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Chen, Xiaoliang, Zuqing Zhu, Jiannan Guo, Sheng Kang, Roberto Proietti, Alberto Castro et S. J. B. Yoo. « Leveraging Mixed-Strategy Gaming to Realize Incentive-Driven VNF Service Chain Provisioning in Broker-Based Elastic Optical Inter-Datacenter Networks ». Journal of Optical Communications and Networking 10, no 2 (31 janvier 2018) : A232. http://dx.doi.org/10.1364/jocn.10.00a232.

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Zhu, Min, Qi Chen, Jiahua Gu et Pingping Gu. « Deep Reinforcement Learning for Provisioning Virtualized Network Function in Inter-Datacenter Elastic Optical Networks ». IEEE Transactions on Network and Service Management, 2022, 1. http://dx.doi.org/10.1109/tnsm.2022.3172344.

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Yu, Hongfang, Zhenrong Chen, Gang Sun, Xiaojiang Du et Mohsen Guizani. « Profit Maximization of Online Service Function Chain Orchestration in an Inter-Datacenter Elastic Optical Network ». IEEE Transactions on Network and Service Management, 2020, 1. http://dx.doi.org/10.1109/tnsm.2020.3032969.

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Saxena, Deepika, et Ashutosh Kumar Singh. « Communication Cost Aware Resource Efficient Load Balancing (CARE-LB) Framework for Cloud Datacenter ». Recent Advances in Computer Science and Communications 13 (18 août 2020). http://dx.doi.org/10.2174/2666255813999200818173107.

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Background: Load balancing of communication-intensive applications, allowing efficient resource utilization and minimization of power consumption is a challenging multi-objective virtual machine (VM) placement problem. The communication among inter-dependent VMs, raises network traffic, hampers cloud client's experience and degrades overall performance, by saturating the network. Introduction: Cloud computing has become an indispensable part of Information Technology (IT), which supports the backbone of digitization throughout the world. It provides shared pool of IT resources, which are: always on, accessible from anywhere, at anytime and delivered on demand, as a service. The scalability and pay-per-use benefits of cloud computing has driven the entire world towards on-demand IT services that facilitates increased usage of virtualized resources. The rapid growth in the demands of cloud resources has amplified the network traffic in and out of the datacenter. Cisco Global Cloud Index predicts that by the year 2021, the network traffic among the devices within the datacenter will grow at Compound Annual Growth Rate (CAGR) of 23.4% Methods: To address these issues, a communication cost aware and resource efficient load balancing (CARE-LB) framework is presented, that minimizes communication cost, power consumption and maximize resource utilization. To reduce the communication cost, VMs with high affinity and inter-dependency are intentionally placed closer to each other. The VM placement is carried out by applying the proposed integration of Particle Swarm Optimization and non-dominated sorting based Genetic Algorithm i.e. PSOGA algorithm encoding VM allocation as particles as well as chromosomes. Results: The performance of proposed framework is evaluated by the execution of numerous experiments in the simulated datacenter environment and it is compared with the state-of-the-art methods like, Genetic Algorithm, First-Fit, Random-Fit and Best-Fit heuristic algorithms. The experimental outcome reveals that the CARE-LB framework improves 11% resource utilization, minimize 4.4% power consumption, 20.3% communication cost with reduction of execution time up to 49.7% over Genetic Algorithm based Load Balancing framework. Conclusion: The proposed CARE-LB framework provides promising solution for faster execution of data-intensive applications with improved resource utilization and reduced power consumption. Discussion: In the observed simulation, we analyze all the three objectives, after execution of the proposed multi-objective VM allocations and results are shown in Table 4. To choose the number of users for analysis of communication cost, the experiments are conducted with different number of users. For instance, for 100 VMs we choose 10, 20,...,80 users, and their request for VMs (number of VMs and type of VMs) are generated randomly, such that the total number of requested VMs do not exceed number of available VMs.
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Das, Shrayan, Kirtan Gopal Panda, Debarati Sen et Wasim Arif. « Maximizing Last-Minute Backup in Endangered Time-Varying Inter-Datacenter Networks ». IEEE/ACM Transactions on Networking, 2021, 1–18. http://dx.doi.org/10.1109/tnet.2021.3098766.

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Luo, Long, Yijing Kong, Mohammad Noormohammadpour, Zilong Ye, Gang Sun, Hongfang Yu et Bo Li. « Deadline-Aware Fast One-to-Many Bulk Transfers over Inter-Datacenter Networks ». IEEE Transactions on Cloud Computing, 2019, 1. http://dx.doi.org/10.1109/tcc.2019.2935435.

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Yao, Jingjing, Ping Lu, Long Gong et Zuqing Zhu. « On Fast and Coordinated Data Backup in Geo-Distributed Optical Inter-Datacenter Networks ». Journal of Lightwave Technology, 2015, 1. http://dx.doi.org/10.1109/jlt.2015.2425303.

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Dong, Xiaodong, et Binlei Cai. « Slardar : Scheduling information incomplete inter-datacenter deadline-aware coflows with a decentralized framework ». Computer Networks, juillet 2022, 109178. http://dx.doi.org/10.1016/j.comnet.2022.109178.

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Dong, Xiaodong, Zhao Laiping, Xiaobo Zhou, Keqiu Li, Tie Qiu et Deke Guo. « An Online Cost-efficient Transmission Scheme for Information-agnostic Traffic in Inter-datacenter Networks ». IEEE Transactions on Cloud Computing, 2020, 1. http://dx.doi.org/10.1109/tcc.2019.2941688.

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