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Review:data center network architecture in cloud computing:review, taxonomy, and open research issues

Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第9期15卷 776-793页

作者： Han QI Muhammad SHIRAZ Jie-yao LIU Abdullah GANI Zulkanain ABDUL RAHMAN Torki A.ALTAMEEM Mobile Cloud Computing Research Lab Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur 50603 Malaysia Department of History Faculty of Arts and Social Sciences University of MalayaKuala Lumpur 50603 Malaysia Department of Computer Science Riyadh Community College King Saud UniversityRiyadh 11533 Saudi Arabia

The data center network(DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.

关键词： data center network Cloud computing Architecture network topology

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Rethinking data center networks:Machine Learning Enables network Intelligence

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Journal of Communications and Information networks 2022年第2期7卷 157-169页

作者： Bo Li Ting Wang Peng Yang Mingsong Chen Mounir Hamdi Shanghai Key Laboratory of Trustworthy Computing the Engineering Research Center of Soft-ware/Hardware Co-design Technology and Applicationthe Ministry of EducationEast China Normal UniversityShanghai 200062China College of Science and Engineering Hamad Bin Khalifa UniversityDoha 999043Qatar

To support the needs of ever-growing cloudbased services,the number of servers and network devices in data centers is increasing exponentially,which in turn results in high complexities and difficulties in network optimization.Machine learning(ML)provides an effective way to deal with these challenges by enabling network intelligence.To this end,numerous creative ML-based approaches have been put forward in recent years.Nevertheless,the intelligent optimization of data center networks(DCN)still faces enormous challenges.To the best of our knowledge,there is a lack of systematic and original investigations with in-depth analysis on intelligent DCN.To this end,in this paper,we investigate the application of ML to DCN optimization and provide a general overview and in-depth analysis of the recent works,covering flow prediction,flow classification,and resource management.Moreover,we also give unique insights into the technology evolution of the fusion of DCN and ML,together with some challenges and future research opportunities.

关键词： data center network intelligent optimiza-tion machine learning network intelligence

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BCDC： A High-Performance, Server-Centric data center network

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Journal of Computer Science & Technology 2018年第2期33卷 400-416页

作者： Xi Wang Jian-Xi Fan Cheng-Kuan Lin Jing-Ya Zhou Zhao Liu School of Computer Science and Technology Soochow University Suzhou 215006 China School of Software and Services Outsourcing Suzhou Institute of Industrial Technology Suzhou 215004 China

The capability of the data center network largely decides the performance of cloud computing. However, the number of servers in the data center network becomes increasingly huge, because of the continuous growth of the application requirements. The performance improvement of cloud computing faces great challenges of how to connect a large number of servers in building a data center network with promising performance. Traditional tree-based data center networks have issues of bandwidth bottleneck, failure of single switch, etc. Recently proposed data center networks such as DCell, FiConn, and BCube, have larger bandwidth and better fault-tolerance with respect to traditional tree-based data center networks. Nonetheless, for DCell and FiConn, the fault-tolerant length of path between servers increases in case of failure of switches; BCube requires higher performance in switches when its scale is enlarged. Based on the above considerations, we propose a new server-centric data center network, called BCDC, based on crossed cube with excellent performance. Then, we study the connectivity of BCDC networks. Furthermore, we propose communication algorithms and fault-tolerant routing algorithm of BCDC networks. Moreover, we analyze the performance and time complexities of the proposed algorithms in BCDC networks. Our research will provide the basis for design and implementation of a new family of data center networks.

关键词： data center network interconnection network crossed cube server-centric fault-tolerant

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data center network Architecture

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ZTE Communications 2013年第1期11卷 54-61页

作者： Yantao Sun Jing Cheng Konggui Shi Qiang Liu School of Computer and Information Technology Beijing Jiaotong University

1 Introduction The history of data centers can be traced back to the 1960s. Early data centers were deployed on main- frames that were time-shared by users via remote terminals. The boom in data centers came duringthe... 详细信息

关键词： data center network network architecture network topology virtual machine migration

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Collaborative network Security in Multi-Tenant data center for Cloud Computing

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Tsinghua Science and Technology 2014年第1期19卷 82-94页

作者： Zhen Chen Wenyu Dong Hang Li Peng Zhang Xinming Chen Junwei Cao Research Institute of Information Technology and Tsinghua National Lab for Information Science and Technology Tsinghua University Department of Computer Science and Technology Tsinghua University Department of Computer Science and Technology PLA Univ. of Info. & Eng. Department of Electronic and Information Engineering Xi’an Jiaotong University Department of Electrical and Computer Engineering University of Massachusetts

A data center is an infrastructure that supports Internet service. Cloud comput the face of the Internet service infrastructure, enabling even small organizations to quickly ng is rapidly changing build Web and mobile applications for millions of users by taking advantage of the scale and flexibility of shared physical infrastructures provided by cloud computing. In this scenario, multiple tenants save their data and applications in shared data centers, blurring the network boundaries between each tenant in the cloud. In addition, different tenants have different security requirements, while different security policies are necessary for different tenants. network virtualization is used to meet a diverse set of tenant-specific requirements with the underlying physical network enabling multi-tenant datacenters to automatically address a large and diverse set of tenants requirements. In this paper, we propose the system implementation of vCNSMS, a collaborative network security prototype system used n a multi-tenant data center. We demonstrate vCNSMS with a centralized collaborative scheme and deep packet nspection with an open source UTM system. A security level based protection policy is proposed for simplifying the security rule management for vCNSMS. Different security levels have different packet inspection schemes and are enforced with different security plugins. A smart packet verdict scheme is also integrated into vCNSMS for ntelligence flow processing to protect from possible network attacks inside a data center network

关键词： data center network network security software defined network collaborative network security multi- tenant network virtualization intelligent flow processing cloud computing

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Load Balancing Fat-Tree on Long-Lived Flows:Avoiding Congestion in a data center network

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ZTE Communications 2014年第2期12卷 57-62页

作者： Wen Gao Xuyan Li Boyang Zhou Chunming Wu College of Computer Science Zhejiang University

In a data center network （DCN）, load balancing is required when servers transfer data on the same path. This is necessary to avoid congestion. Load balancing is challenged by the dynamic transferral of demands and complex routing control. Because of the distributed nature of a traditional network, previous research on load balancing has mostly focused on improving the performance of the local network; thus, the load has not been optimally balanced across the entire network. In this paper, we propose a novel dynamic load-balancing algorithm for fat-tree. This algorithm avoids congestions to the great possible extent by searching for non-conflicting paths in a centralized way. We implement the algorithm in the popular software-defined networking architecture and evaluate the algorithm＇ s performance on the Mininet platform. The results show that our algorithm has higher bisection band- width than the traditional equal-cost multi-path load-balancing algorithm and thus more effectively avoids congestion.

关键词： data center network software-defined networking load balancing network management

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data center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

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Computers, Materials & Continua 2023年第5期75卷 4377-4393页

作者： Chunzhi Wang Weidong Cao Yalin Hu Jinhang Liu School of Computer Science Hubei University of TechnologyWuhan430068China

According to Cisco’s Internet Report 2020 white paper,there will be 29.3 billion connected devices worldwide by 2023,up from 18.4 billion in 2018.5G connections will generate nearly three times more traffic than 4G connections.While bringing a boom to the network,it also presents unprecedented challenges in terms of flow forwarding decisions.The path assignment mechanism used in traditional traffic schedulingmethods tends to cause local network congestion caused by the concentration of elephant flows,resulting in unbalanced network load and degraded quality of service.Using the centralized control of software-defined networks,this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing(MCQG).The ideal transmission path is selected for data flows while considering the network congestion rate and quality of service.Different traffic scheduling strategies are used according to the characteristics of different service types in data centers.Reroute scheduling for elephant flows that tend to cause local congestion.The path evaluation function is formed by the maximum link utilization on the path,the number of elephant flows and the time delay,and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant flows.It is used to reduce the possibility of local network congestion occurrence.Equal cost multi-path(ECMP)protocols with faster response time are used to schedulemouse flows with shorter duration.Used to guarantee the quality of service of the network.To achieve isolated transmission of various types of data streams.The experimental results show that the proposed strategy has higher throughput,better network load balancing,and better robustness compared to ECMP under different traffic models.In addition,because it can fully utilize the resources in the network,MCQG also outperforms another traffic scheduling strategy that does rerouting for eleph

关键词： Software-defined network data center network OpenFlow network congestion quality of service

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Fault-Tolerant Hamiltonicity and Hamiltonian Connectivity of BCube with Various Faulty Elements

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Journal of Computer Science & Technology 2020年第5期35卷 1064-1083页

作者： Gui-Juan Wang Cheng-Kuan Lin Jian-Xi Fan Jing-Ya Zhou Bao-Lei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China School of Computer Science and Technology Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China

BCube is one kind of important data center networks.Hamiltonicity and Hamiltonian connectivity have significant applications in communication networks.So far,there have been many results concerning fault-tolerant Hamiltonicity and fault-tolerant Hamiltonian connectivity in some data center networks.However,these results only consider faulty edges and faulty servers.In this paper,we study the fault-tolerant Hamiltonicity and the fault-tolerant Hamiltonian connectivity of BCube(n,k)under considering faulty servers,faulty links/edges,and faulty switches.For any integers n≥2 and k≥0,let BCn,k be the logic structure of BCube(n,k)and F be the union of faulty elements of BCn,k,Let fv/fe,and fs be the number of faulty servers,faulty edges,and faulty switches of BCiLbe(n,k),respectively.We show that BCnik-F is fault-tolerant Hamiltonian if fv+fe+(n-1)/s≤(n-1)(k+1)-2 and BCn,k-F is fault-tolerant Hamiltonian-connected ifv,+fe+(n-1)fs≤(n-1)(k+1)-3.To the best of our knowledge,this paper is the first work which takes faulty switches into account to study the fault-tolerant Hamiltonicity and the fault-tolerant Hamiltonian connectivity in data center networks.

关键词： data center network BCube fault-tolerance Hamiltonicity Hamiltonian connectivity

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Virtual machine placement optimizing to improve network performance in cloud data centers

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The Journal of China Universities of Posts and Telecommunications 2014年第3期21卷 62-70页

作者： DONG Jian-kang WANG Hong-bo LI Yang-yang CHENG Shi-duan State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine （VM） is becoming a major issue for cloud providers. The existing virtual machine placement （VMP） solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization （MLU）. On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.

关键词： cloud computing data center network virtual machine placement traffic engineering network performance

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MABP: an optimal resource allocation approach in data center networks

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Science China(Information Sciences) 2014年第10期57卷 230-245页

作者： LI XiaoLing WANG HuaiMin DING Bo LI XiaoYong National Key Laboratory for Parallel and Distributed Processing School of ComputerNational University of Defense Technology

In data center networks, resource allocation based on workload is an effective way to allocate the infrastructure resources to diverse cloud applications and satisfy the quality of service for the users, which refers to mapping a large number of workloads provided by cloud users/tenants to substrate network provided by cloud providers. Although the existing heuristic approaches are able to find a feasible solution, the quality of the solution is not guaranteed. Concerning this issue, based on the minimum mapping cost, this paper solves the resource allocation problem by modeling it as a distributed constraint optimization problem. Then an efficient approach is proposed to solve the resource allocation problem, aiming to find a feasible solution and ensuring the optimality of the solution. Finally, theoretical analysis and extensive experiments have demonstrated the effectiveness and efficiency of our proposed approach.

关键词： data center network resource allocation workload substrate network optimality distributed constraint optimization

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