Layout-Independent Wireless Facility Constructing and Scheduling for Data Center Networks

Wireless Communications and Mobile Computing ◽

10.1155/2017/1354273 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Xianglin Wei ◽

Qin Sun

Keyword(s):

Data Center ◽

Network Performance ◽

Data Center Networks ◽

Data Packets ◽

Typical Data ◽

Calculation Step ◽

Scheduling Method ◽

Series Of Experiments ◽

Traffic Estimation ◽

And Control

The control packets in the data center networks (DCNs) have to contest with the data packets although they are usually much shorter in size and much more important in network management. Moreover, the uneven distribution of the packets may create potential traffic hotspots in the DCN which could degrade network performance drastically. To bridge these gaps, a layout-independent constructing algorithm and a scheduling method are put forward towards layout-independent wireless facility in data centers. First of all, a conflict aware spanning tree algorithm is developed to construct the wireless facility network (WFN). Secondly, a scheduling method which contains three steps, route calculation, traffic estimation, and flow scheduling, is presented. In the route calculation step, a route set between each node pair is calculated in advance for later usage. The scheduler estimates the traffic loads on the links on a regular basis in the traffic estimation step. Then, arrived data and control flows are scheduled according to multiple policies based on given route sets and scheduling objectives in the flow scheduling step. Finally, a series of experiments have been conducted on NS3 based on two typical data center layouts. Experimental results in both scenarios have validated our proposal’ effectiveness.

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Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Electronics ◽

10.3390/electronics10151774 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1774

Author(s):

Ming-Chin Chuang ◽

Chia-Cheng Yen ◽

Chia-Jui Hung

Keyword(s):

Data Center ◽

Completion Time ◽

Network Performance ◽

Data Locality ◽

Task Completion ◽

Data Center Networks ◽

Task Completion Time ◽

Data Packets ◽

Network Bandwidth ◽

Data Process

Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.

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HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00248-4 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Jiawei Huang ◽

Shiqi Wang ◽

Shuping Li ◽

Shaojun Zou ◽

Jinbin Hu ◽

...

Keyword(s):

Data Center ◽

Large Scale ◽

Network Performance ◽

Rooted Tree ◽

Heterogeneous Traffic ◽

Data Center Networks ◽

Packet Reordering ◽

Traffic Characteristics ◽

Network Utilization ◽

The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

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An Efficient Flow Detection and Scheduling Method in Data Center Networks

The 2nd International Conference on Computing and Data Science ◽

10.1145/3448734.3450819 ◽

2021 ◽

Author(s):

Zhiyu Liu ◽

Mangui Lian ◽

Jin Guo ◽

Guangchao Wang

Keyword(s):

Data Center ◽

Data Center Networks ◽

Scheduling Method ◽

Flow Detection

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S-CDCA: a semi-cluster directive-congestion protocol for priority-based data in WSNs

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i1.pp438-444 ◽

2021 ◽

Vol 23 (1) ◽

pp. 438

Author(s):

Marwan Ihsan Shukur

Keyword(s):

Sensor Networks ◽

Network Performance ◽

Great Part ◽

Base Station ◽

Wireless Sensor ◽

Habitat Monitoring ◽

Data Packets ◽

Data Source ◽

And Control ◽

Network Interfaces

The internet of things (IoT) protocols and regulations are being developed forvarious applications includes: habitat monitoring, machinery control, general health-care, smart-homes and more. A great part of I0T comprised of sensors nodes in connected networks (i.e. sensor networks.). A sensor network is a group of nodes with sensory module and computational elements connected through network interfaces. The most interesting type of sensor networks are wireless sensor networks. The nodes here are connected through wirless interfaces. The shared medium between these nodes, creates different challenges. Congestion in such network is ineavitable. Different models andmethods were proposed to alleviate congestion in wireless sensor networks.This paper presents a semi-cluster directive congestion method that allivatenetwork congestion forpriority-baseddata transmission. The method simprove the network performance by implementing temporary cluster forlow level priority data packets while providing a clear link between highpriority data source node and the network base station. Simulation resultsshow that. The proposed method outperformes ad hocOn-demand distance vector (AODV) reactive procotol approach and priority-based congestion control dynamic clustering (PCCDC) a cluster-based methodin network energy consumption and control packets overhead during network operation.The proposed method also shows comparative improvments in end-to-enddelays versus PCCDC.

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Application and Analysis of Multicast Blocking Modelling in Fat-Tree Data Center Networks

Complexity ◽

10.1155/2018/7563170 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12

Author(s):

Guozhi Li ◽

Songtao Guo ◽

Guiyan Liu ◽

Yuanyuan Yang

Keyword(s):

Data Center ◽

Network Performance ◽

Blocking Probability ◽

Scheduling Algorithm ◽

Operation Time ◽

Data Center Networks ◽

Scheduling Strategy ◽

Traffic Efficiency ◽

Network Bandwidth ◽

Multicast Scheduling

Multicast can improve network performance by eliminating unnecessary duplicated flows in the data center networks (DCNs). Thus it can significantly save network bandwidth. However, the network multicast blocking may cause the retransmission of a large number of data packets and seriously influence the traffic efficiency in data center networks, especially in the fat-tree DCNs with multirooted tree structure. In this paper, we build a multicast blocking model and apply it to solve the problem of network blocking in the fat-tree DCNs. Furthermore, we propose a novel multicast scheduling strategy. In the scheduling strategy, we select the uplink connecting to available core switch whose remaining bandwidth is close to and greater than the three times of bandwidth multicast requests so as to reduce the operation time of the proposed algorithm. Then the blocking probability of downlink in the next time-slot is calculated in multicast subnetwork by using Markov chains theory. With the obtained probability, we select the optimal downlink based on the available core switch. In addition, theoretical analysis shows that the multicast scheduling algorithm has close to zero network blocking probability as well as lower time complexity. Simulation results verify the effectiveness of our proposed multicast scheduling algorithm.

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Resource management and control in converged optical data center networks: Survey and enabling technologies

Computer Networks ◽

10.1016/j.comnet.2015.05.011 ◽

2015 ◽

Vol 88 ◽

pp. 121-135 ◽

Cited By ~ 9

Author(s):

Weigang Hou ◽

Lei Guo ◽

Yejun Liu ◽

Cunqian Yu ◽

Yue Zong

Keyword(s):

Resource Management ◽

Data Center ◽

Optical Data ◽

Data Center Networks ◽

Enabling Technologies ◽

And Control

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Advantages and Control of Hybrid Packet Optical-Circuit-Switched Data Center Networks

Advanced Photonics for Communications ◽

10.1364/ps.2014.pm2c.4 ◽

2014 ◽

Cited By ~ 7

Author(s):

John Bowers ◽

Atiq Raza ◽

Daniel Tardent ◽

Jitender Miglani

Keyword(s):

Data Center ◽

Data Center Networks ◽

Optical Circuit ◽

And Control

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An Adaptive Authenticated Model for Big Data Stream SAVI in SDN-Based Data Center Networks

Security and Communication Networks ◽

10.1155/2021/5451820 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Qizhao Zhou ◽

Junqing Yu ◽

Dong Li

Keyword(s):

Big Data ◽

Data Center ◽

Data Stream ◽

Network Performance ◽

Rapid Development ◽

High Growth ◽

Ratio Test ◽

Data Center Networks ◽

Time Data ◽

Two Phase

With the rapid development of data-driven and bandwidth-intensive applications in the Software Defined Networking (SDN) northbound interface, big data stream is dynamically generated with high growth rates in SDN-based data center networks. However, a significant issue faced in big data stream communication is how to verify its authenticity in an untrusted environment. The big data stream traffic has the characteristics of security sensitivity, data size randomness, and latency sensitivity, putting high strain on the SDN-based communication system during larger spoofing events in it. In addition, the SDN controller may be overloaded under big data stream verification conditions on account of the fast increase of bandwidth-intensive applications and quick response requirements. To solve these problems, we propose a two-phase adaptive authenticated model (TAAM) by introducing source address validation implementation- (SAVI-) based IP source address verification. The model realizes real-time data stream address validation and dynamically reduces the redundant verification process. A traffic adaptive SAVI that utilizes a robust localization method followed by the Sequential Probability Ratio Test (SPRT) has been proposed to ensure differentiated executions of the big data stream packets forwarding and the spoofing packets discarding. The TAAM model could filter out the unmatched packets with better packet forwarding efficiency and fundamental security characteristics. The experimental results demonstrate that spoofing attacks under big data streams can be directly mitigated by it. Compared with the latest methods, TAAM can achieve desirable network performance in terms of transmission quality, security guarantee, and response time. It drops 97% of the spoofing attack packets while consuming only 9% of the controller CPU utilization on average.

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