检索结果-内蒙古大学图书馆

elephant flow detection intelligence for software-defined networks: a survey on current techniques and future direction

引用

EVOLUTIONARY INTELLIGENCE 2024年第4期17卷 2125-2143页

作者： Hamdan, Mosab Elshafie, Hashim Salih, Sayeed Abdelsalam, Samah Husain, Omayma Gismalla, Mohammed S. M. Ghaleb, Mustafa Marsono, M. N. KFUPM Interdisciplinary Res Ctr Intelligent Secure Syst Dhahran Saudi Arabia King Khalid Univ Coll Comp Sci Abha Saudi Arabia King Saud Univ Fac Comp & Informat Technol Riyadh Saudi Arabia Univ Khartoum Dept Informat Technol Khartoum Sudan KFUPM Interdisciplinary Res Ctr Commun Syst & Sensing IR Dhahran Saudi Arabia Univ Teknol Malaysia Fac Elect Engn Johor Baharu Malaysia Univ Hail Coll Business Adm Dept Management & Informat Syst Hail Saudi Arabia Ajman Univ Dept Informat Technol Ajman U Arab Emirates

Software-defined networking (SDN) is characterized by the separation of the packet forwarding plane from the network control plane. This separation offers an extensive view of the network's state, enhancing network resilience and management. Network traffic classification can improve SDN control and resource provisioning, particularly for elephant flows (EFs) detection. Existing techniques for detecting EFs utilize preset thresholds and bandwidth that are inadequate for changing traffic concepts. Moreover, these techniques consume high data plane-controller bandwidth and have a high detection time. This research first describes the related management techniques in SDN. Then according to the detecting location, elephant flow detection approaches are classified into four kinds: host-based, switch-based, controller-based, and hybrid controller-switch-based detection. This research examined four types of detection approaches and concluded that host-based detection primarily relies on the flow statistics threshold. Such approaches frequently gather flow statistics by monitoring the socket buffer or via the hypervisor. In contrast, switch-based detection can leverage both the flow statistics threshold and flow characteristics. Controller-based detection techniques in SDN focus on extracting flow feature statistics at the controller level, aiming to reduce switch overhead while potentially increasing controller loads. Finally, hybrid controller-switch-based detection combines both routing aspects, offering fine-grained flow control. However it faces challenges in maintaining a balance in timeliness, accuracy, and cost. Furthermore, the survey incorporates recent SDN advancements such as machine learning-based methods, programmable switches, and real-world SDN applications in data centers, global content delivery networks, healthcare, and IoT. Finally, the article makes a comprehensive comparison and puts forward several points of future prediction in terms of elephant flow

关键词： Software-defined networking elephant flow detection flow classification Network traffic classification Scheduling Re-routing Load balancing

来源：评论

学校读者我要写书评

暂无评论

flow-Aware elephant flow detection for Software-Defined Networks

引用

IEEE ACCESS 2020年 8卷 72585-72597页

作者： Hamdan, Mosab Mohammed, Bushra Humayun, Usman Abdelaziz, Ahmed Khan, Suleman Ali, M. Akhtar Imran, Muhammad Marsono, M. N. Univ Teknol Malaysia Fac Engn Sch Elect Engn Johor Baharu 81310 Malaysia Future Univ Fac Comp Sci Khartoum 11115 Sudan Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England King Saud Univ Coll Appl Comp Sci Riyadh 11451 Saudi Arabia

Software-defined networking (SDN) separates the network control plane from the packet forwarding plane, which provides comprehensive network-state visibility for better network management and resilience. Traffic classification, particularly for elephant flow detection, can lead to improved flow control and resource provisioning in SDN networks. Existing elephant flow detection techniques use pre-set thresholds that cannot scale with the changes in the traffic concept and distribution. This paper proposes a flow-aware elephant flow detection applied to SDN. The proposed technique employs two classifiers, each respectively on SDN switches and controller, to achieve accurate elephant flow detection efficiently. Moreover, this technique allows sharing the elephant flow classification tasks between the controller and switches. Hence, most mice flows can be filtered in the switches, thus avoiding the need to send large numbers of classification requests and signaling messages to the controller. Experimental findings reveal that the proposed technique outperforms contemporary methods in terms of the running time, accuracy, F-measure, and recall.

关键词： Real-time systems Switches Monitoring Data centers Feature extraction Task analysis Software-defined networking flow classification elephant flow detection

来源：评论

学校读者我要写书评

暂无评论

Regression Based Dynamic elephant flow detection in Airborne Network

引用

IEEE ACCESS 2020年 8卷 217123-217133页

作者： Liu, Pengfei Lv, Na Chen, Kefan Tang, Liang Zhou, Jiaxin Air Force Engn Univ Sch Informat & Nav Xian 710077 Peoples R China Peoples Liberat Army PLA 94860 Beijing Peoples R China Peoples Liberat Army PLA 61932 Beijing Peoples R China

As an important part of the spatial information network, airborne network (AN), which connects air platforms with upper satellites and ground devices, has been increasingly important right now. Due to the heavy-tailed distribution of network traffic, elephant flow detection is usually used to catch and control the key part of network traffic with low costs, which is a practical strategy to strengthen network management and improve network performance. In this paper, we consider the problem of dynamic threshold elephant flow detection in AN, and an intelligent method based on regression with pre-classification is proposed to adapt to the limited and dynamically changing bandwidth. The filtering mechanism with waiting-window is used firstly to filter out parts of small flows to decrease the detection cost. Then, the pre-classification is used to divide the range to be predicted and the flow size regression can be carried out in a compressed range, which makes the results more accurate. Finally, the predicted size is compared with the specific detection threshold related to the specific moment, and the elephant flow is identified. Numerical experiments demonstrate that the proposed method has a better adaptability to dynamic threshold and the performance is much better.

关键词： Computer networks Bandwidth Feature extraction Telecommunication traffic Data models Training Mice elephant flow detection dynamic threshold machine learning regression learning airborne network

来源：评论

学校读者我要写书评

暂无评论

A Classification-Based elephant flow detection Method using Application Round on SDN Environments 19

A Classification-Based Elephant Flow Detection Method using ...

引用

19th Asia-Pacific Network Operations and Management Symposium (APNOMS) - Managing a World of Things

作者： Huang, Yuan-Hao Shih, Wen-Yueh Huang, Jiun-Long Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781538611012

We propose in this paper a classification-based elephant flow detection method for SDN environments. The proposed elephant flow detection method consists of two classifiers running on the switch and the controller, respectively. For better performance, the concept of application round is used to build the classifier running on the controller. Experimental results show that our elephant flow detection method is able to obtain high recall and F-measure.

关键词： elephant flow detection SDN Software-Defined Networking

来源：评论

学校读者我要写书评

暂无评论

elephant flow detection in Datacenters Using Openflow-based Hierarchical Statistics Pulling

Elephant Flow Detection in Datacenters Using OpenFlow-based ...

引用

IEEE Global Communications Conference (GLOBECOM)

作者： Lin, Chun-Yu Chen, Chien Chang, Je-Wei Chu, Yu Huang Natl Chiao Tung Univ Informat Technol Serv Ctr Hsinchu Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan Chunghwa Telecom Co Ltd Chunghwa Telecom Labs Taoyuan Taiwan

ISBN: (纸本)9781479935123

This paper proposes an effective elephant flow detection in datacenters. Equal Cost MultiPath (ECMP) is a popular routing mechanism to achieve load balancing in datacenter networks. However, ECMP only considers equally distributing the flows over equal-length paths, but does not take the size of the flows into account. Previous studies show that detecting and rerouting elephant flows (flows that transfer significant amount of data) effectively can lead to a 113% improvement in aggregate throughput compared with the simple use of ECMP. A naive way to detect the elephant flows is to pull statistics from each flow independently. Since "the elephant and mouse phenomenon" suggests that there are only very few elephant flows in a datacenter network, it's not efficient to collect information from each flow. Therefore, we propose a Hierarchical Statistics Pulling (HSP) mechanism using Openflow protocol to save bandwidth consumption and processing time. In order to further improve the performance of HSP, two supplement functions called elephant store and range splitting are developed. Our approaches can detect elephant flows with only a small amount of network bandwidth consumption. We use Mininet emulator and mathematical analysis to verify our methods. Both of them confirm the benefits of our approaches.

关键词： elephant flow detection Software Defined Networking Traffic Engineering Statistics Pulling elephant and Mice Phenomenon

来源：评论

学校读者我要写书评

暂无评论

elephant flow detection in Datacenters Using Openflow-based Hierarchical Statistics Pulling

Elephant Flow Detection in Datacenters Using OpenFlow-based ...

引用

IEEE Global Communications Conference

作者： Chun-Yu Lin Chien Chen Je-Wei Chang Yu Huang Chu Department of Computer Science National Chiao Tung University Information Technology Service Center National Chiao Tung University Chunghwa Telecom Laboratories Chunghwa Telecom Co. Ltd.

ISBN: (纸本)9781479935130

关键词： elephant flow detection Software Defined Networking Traffic Engineering Statistics Pulling elephant and Mice Phenomenon

来源：评论

学校读者我要写书评

暂无评论

Online elephant flow Prediction for Load Balancing in Programmable Switch-Based DCN

引用

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第1期21卷 745-758页

作者： Xie, Shengxu Hu, Guyu Xing, Changyou Liu, Yaqun Army Engn Univ PLA Inst Command Nanjing 210007 Peoples R China Army Engn Univ PLA Control Engn Coll Nanjing 210007 Peoples R China

In the data center network, traffic has a distinct heavy-tailed distribution characteristic, with the minority of throughput-sensitive elephant flows occupy most of the bandwidth and the majority of latency-sensitive mice flows require low latency. Therefore, it is very important to predict the network flow size and make a reasonable balanced scheduling. Currently, the traditional elephant flow detection schemes based on thresholds have poor accuracy and low granularity, while the intelligent detection schemes based on SDN has a certain flow scheduling response delay. For this reason, a two-stage online elephant flow prediction method for load balancing (OPLB) is proposed. Based on the programmable data plane, OPLB first pre-identifies the elephant flow by extracting the stateless features of the first packet of the network flow arriving at the switch. Secondly, the size of the elephant flow is predicted by extracting the features of the first ${n}$ packets of the flow. Finally, the detected elephant and mice flows are balanced to high throughput and low latency paths. Combined with the computing and storage capabilities of the programmable switch, the models and parameters in OPLB can be updated online by mapping the trained classification and prediction decision tree models to the matching-action pipelines of the programmable switch, thus achieving dynamic load balancing. We prototype OPLB in P4 software simulation environment and evaluate it with packet traces from the university data centers (UNI). The experiment shows that the accuracy of classification and prediction reached 89.3% when the proportion of elephant flow was 20%. At the same time, compared to the scheme that only uses single stage elephant flow prediction, OPLB reduces the amount of information collected by the switch by about 40% when the elephant flow proportion is 20%.

关键词： Computer networks Switches Load management Feature extraction Communication system traffic Bandwidth Data centers Data center networks programmable data plane elephant flow detection load balancing

来源：评论

学校读者我要写书评

暂无评论

DPLBAnt: Improved load balancing technique based on detection and rerouting of elephant flows in software-defined networks

引用

COMPUTER COMMUNICATIONS 2021年 180卷 315-327页

作者： Hamdan, Mosab Khan, Suleman Abdelaziz, Ahmed Sadiah, Shahidatul Shaikh-Husin, Nasir Al Otaibi, Sattam Maple, Carsten Marsono, M. N. Univ Teknol Malaysia Fac Engn Sch Elect Engn Johor Baharu 81310 Malaysia Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Univ Malaysia Sabah Fac Comp & Informat Kota Kinabalu 88400 Sabah Malaysia Taif Univ Coll Engn Innovat & Entrepreneurship Ctr At Taif 26571 Saudi Arabia Univ Warwick Warwick Mfg Grp Coventry CV4 7AL W Midlands England

Traffic management in software-defined networks (SDNs) is critical for efficient bandwidth utilization and resource provisioning. Recent works on SDN load balancing (LB) have focused on identifying and rerouting elephant flows (EFs) for effective bandwidth usage. These techniques have some limitations, such as using source-to-destination hop count as the primary rerouting metric and not differentiating the types of flow that result in frequent resource conflicts when handling EF with long-lived bandwidth. Besides, current EF detection techniques use predefined bandwidth-use thresholds that cannot adapt to the ever-changing traffic condition. Also, detecting EF on switches results in high controller-switch bandwidth and high EF detection time. This study presents an ant colony optimization-based technique for rerouting EFs while considering load-balancing in the SDN links. This technique, called DPLBAnt, is formulated as a shortest-path problem in SDN that can alleviate the high controller-switch load. The proposed technique first detects EF by using a pair of classifiers on both SDN controller and switches. Most EF candidates are sifted on the switches, resulting in accurate and efficient detection of EF. Then, DPLBAnt obtains the global state of the SDN from which the most optimal paths for congested links are retrieved, and EF are redirected accordingly. The performance of the proposed DPLBAnt has been extensively simulated. Results indicate its superior performance over Equal-Cost Multi-Path (ECMP) and flowSeer techniques in terms of average end-to-end delay (54% and 7.9% better), average network throughput (3.5x and 1.5x better), and average packet loss (18% and 10% better) respectively. The overall performance indicates that the proposed LB technique based on detection and rerouting of EFs can improve SDN's overall performance.

关键词： Software-defined network elephant flow detection flow classification Load balancing Re-routing Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

A Congestion Aware Multi-Path Label Switching in Data Centers Using Programmable Switches

A Congestion Aware Multi-Path Label Switching in Data Center...

引用

15th IEEE International Conference on Networking, Architecture and Storage (NAS)

作者： Chang, Yeim-Kuan Wang, Hung-Yen Lin, Yu-Hsiang Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781728177441

The equal-cost multi-path routing (ECMP) [4] achieves load balance in data centers network. Without network's congestion status, ECMP may cause significant imbalance between paths. In this paper, we propose a better congestion aware routing protocol for Software Defined Network (SDN) to provide a better average link utilization. We follow the idea of In-band Network Telemetry (INT) to collect link congestion status in data center networks. Edge switches are responsible for detecting elephant flows by running a heavy hitter detection algorithm. When an elephant flow is reported to the controller by an edge switch, controller will use the collected congestion status to find the least congested path. In order to make the switches forward packets more efficiently and reduce the number of rules in switches' forwarding table, we adopt label switching. We develop a Programming Protocol-independent Packet Processors (P4) program to design our novel routing scheme, which contains a heavy hitter detection algorithm. We further validate that our heavy hitter detection algorithm can run on Banzai machine. We also write a Python controller to communicate with P4 switches through P4 Runtime protocol. Our experimental results shows that the probing process in CAMP minimizes the bandwidth overhead in data centers. We use Mininet to construct fat-tree topologies and the emulated software P4switches run BMv2. The data mining workload is used to generate the traffic in our experiment. CAMP achieves better FCT compared to ECMP and HULA [6]. Also, the number of routing rules in CAMP maintains the smallest when network grows.

关键词： Fat-Tree Load Balancing SDN elephant flow detection In-Band Network Telemetry P4

来源：评论

学校读者我要写书评

暂无评论

QRVE: QoS-Aware Routing and Energy-Efficient VM Placement for Software-Defined DataCenter Networks 8

QRVE: QoS-Aware Routing and Energy-Efficient VM Placement fo...

引用

8th International Symposium on Telecommunications (IST)

作者： Habibi, Pooyan Mokhtari, Masoud Sabaei, Masoud Amirkabir Univ Technol Tehran Polytech Dept Comp Engn & Informat Technol CEIT Tehran Iran

ISBN: (纸本)9781509034352

today's cloud computing Data Centers (DCs) contain tens of thousands of servers with highly significant and remarkable bandwidth requirements as the demands of cloud computing applications, social applications, multimedia contents, and data intensive analysis are dramatically rising. Even thus, current DC technologies have following problems. First, power consumption of a Data Center Network (DCN) are constant irrespective of the use of network resources. Second, violation of the users' SLA according to a static routing technique, a few links in the DCNs are experiencing congestion while other majority links are being underutilized. Third, VM placement and VM problems are essential in datacenter because current server hardware capacities are more than the requirements of the demands from users. To overcome these limitations of the current DCNs, we proposed a distributed Software Defined Networking (SDN) -based mechanism, which consists of energy efficient VM placement and QoS Aware routing with elephant flow detection scheme. our mechanism reduces power consumption of the DCN by turning off/on links, switches and servers. To satisfy user experienced in terms of users' QoS and decreased network congestion, QRVE distributes traffics using traffic aware multipath routing scheme which recognizes elephant flows at the edge layer (Access Layer) across DCN.

关键词： Software-Defined Networks (SDN) Cloud Computing Data Center (DC) Data Center Networks (DCN) Quality of Service (QoS) Multipath Routing Energy Efficiency VM Placement VM Migration elephant flow detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：