113 research outputs found

    A Lightweight, Non-intrusive Approach for Orchestrating Autonomously-managed Network Elements

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    Software-Defined Networking enables the centralized orchestration of data traffic within a network. However, proposed solutions require a high degree of architectural penetration. The present study targets the orchestration of network elements that do not wish to yield much of their internal operations to an external controller. Backpressure routing principles are used for deriving flow routing rules that optimally stabilize a network, while maximizing its throughput. The elements can then accept in full, partially or reject the proposed routing rule-set. The proposed scheme requires minimal, relatively infrequent interaction with a controller, limiting its imposed workload, promoting scalability. The proposed scheme exhibits attracting network performance gains, as demonstrated by extensive simulations and proven via mathematical analysis.Comment: 6 pages 7, figures, IEEE ISCC'1

    ๋ฐ์ดํ„ฐ ์„ผํ„ฐ ๋‚ด์˜ ๋‹ค์ค‘๊ฒฝ๋กœ ์ „์†ก์„ ์œ„ํ•œ ๋™์  ๋ถ€ํ•˜ ๊ท ํ˜• ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ๊ถŒํƒœ๊ฒฝ .Various applications require the data center networks to carry their traffic efficiently. The data center networks usually have a hierarchical topology and exhibit distinct traffic patterns, which is different from the traditional Internet. These features have driven the data center networks to reduce the flow completion time (FCT) and to achieve high throughput. One of the possible solutions is balancing network loads across multiple paths by leveraging transport mechanisms like Equal-Cost MultiPath (ECMP) routing. ECMP allows flows to exploit multiple paths by hashing the metadata of the flows. However, due to the random nature of hash functions, ECMP often distributes the traffic unevenly, which makes it hard to utilize the links' full capacity. Thus, we propose an adaptive load balancing mechanism for multiple paths in data centers, called MaxPass, to complement ECMP. A sender adaptively selects and dynamically changes multiple paths depending on the current network status like congestion. To monitor the network status, the corresponding receiver transmits a probe packet periodically to the senderits loss indicates a traffic congestion. We implemented MaxPass using commodity switches and carry out the quantitative analysis on the ns-2 simulator to show that MaxPass can improve the FCT and the throughput.๋ฐ์ดํ„ฐ ์„ผํ„ฐ ๋‚ด์—์„œ ๋™์ž‘ํ•˜๋Š” ๋‹ค์–‘ํ•œ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜๋“ค์€ ๋„คํŠธ์›Œํฌ ํŠธ๋ž˜ํ”ฝ์„ ๋ณด๋‹ค ํšจ์œจ์ ์œผ๋กœ ์‚ฌ์šฉํ•  ๊ฒƒ์„ ์š”๊ตฌํ•œ๋‹ค. ๋ฐ์ดํ„ฐ ์„ผํ„ฐ ๋„คํŠธ์›Œํฌ๋Š” ๊ธฐ์กด์˜ ์ธํ„ฐ๋„ท๊ณผ๋Š” ๋‹ค๋ฅธ ๋‹ค์ค‘ ๋ฃจํŠธ ๊ณ„์ธต์  ํ† ํด๋กœ์ง€๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์œผ๋ฉฐ, ๋‹ค์–‘ํ•œ ํŠธ๋ž˜ํ”ฝ ํŒจํ„ด์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ํŠน์ง•์œผ๋กœ ์ธํ•ด ๋ฐ์ดํ„ฐ ์„ผํ„ฐ ๋„คํŠธ์›Œํฌ๋Š” ์งง์€ ํ”Œ๋กœ์šฐ ์ฒ˜๋ฆฌ ์™„๋ฃŒ ์‹œ๊ฐ„ (Flow Completion Time)๊ณผ ๋†’์€ ์ฒ˜๋ฆฌ๋Ÿ‰ (Throughput)์„ ์š”๊ตฌํ•œ๋‹ค. ๋ฐ์ดํ„ฐ ์„ผํ„ฐ๊ฐ€ ์š”๊ตฌํ•˜๋Š” ์กฐ๊ฑด๋“ค์„ ๋งŒ์กฑ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ• ์ค‘ ํ•˜๋‚˜๋กœ๋Š” ๋“ฑ๊ฐ€ ๋‹ค์ค‘ ๊ฒฝ๋กœ (Equal-Cost Multi-Path)์™€ ๊ฐ™์€ ๋ผ์šฐํŒ… ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ๋„คํŠธ์›Œํฌ ๋ถ€ํ•˜๋ฅผ ์„œ๋กœ ๋‹ค๋ฅธ ๋งํฌ์— ๋ถ„์‚ฐ์‹œํ‚ค๋Š” ๊ฒƒ์ด ์žˆ๋‹ค. ๋“ฑ๊ฐ€ ๋‹ค์ค‘ ๊ฒฝ๋กœ ๋ผ์šฐํŒ… ๊ธฐ๋ฒ•์€ ํ”Œ๋กœ์šฐ์˜ ๋ฉ”ํƒ€ ๋ฐ์ดํ„ฐ๋ฅผ ํ•ด์‹ฑํ•˜์—ฌ ํ”Œ๋กœ์šฐ๊ฐ€ ์—ฌ๋Ÿฌ ๊ฒฝ๋กœ๋ฅผ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋žœ๋คํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜๋Š” ํ•ด์‹œ ํ•จ์ˆ˜์˜ ํŠน์„ฑ์— ๋”ฐ๋ผ ๋“ฑ๊ฐ€ ๋‹ค์ค‘ ๊ฒฝ๋กœ ๋ผ์šฐํŒ… ๊ธฐ๋ฒ•์€ ์ข…์ข… ํŠธ๋ž˜ํ”ฝ์„ ๊ณ ๋ฅด๊ฒŒ ๋ถ„๋ฐฐํ•˜์ง€ ๋ชปํ•จ์œผ๋กœ์จ, ๋งํฌ์˜ ์ „์ฒด ์šฉ๋Ÿ‰์„ ํ™œ์šฉํ•˜๊ธฐ์—๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฐ์ดํ„ฐ ์„ผํ„ฐ ๋„คํŠธ์›Œํฌ ํ™˜๊ฒฝ์— ๋งž๋Š” ์ƒˆ๋กœ์šด ๋ถ€ํ•˜ ๊ท ํ˜• ๋ฐฐ๋ถ„ ๊ธฐ๋ฒ•์ธ ๋งฅ์ŠคํŒจ์Šค (MaxPass)๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋งฅ์ŠคํŒจ์Šค ๋‚ด์—์„œ ๋ฐ์ดํ„ฐ ์†ก์‹ ์ž๋Š” ํ˜„์žฌ ๋„คํŠธ์›Œํฌ ์ƒํƒœ์— ๋”ฐ๋ผ ๊ฒฝ๋กœ๋ฅผ ๋™์ ์œผ๋กœ ์„ ํƒํ•˜๊ณ  ๋ณ€๊ฒฝํ•œ๋‹ค. ๋ฐ์ดํ„ฐ ์ˆ˜์‹ ์ž๋Š” ํ˜„์žฌ ๋„คํŠธ์›Œํฌ ์ƒํƒœ๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ํƒ์ƒ‰ ํŒจํ‚ท์„ ์ฃผ๊ธฐ์ ์œผ๋กœ ์†ก์‹ ์ž์—๊ฒŒ ๋ณด๋‚ด๊ณ , ํƒ์ƒ‰ ํŒจํ‚ท ๋“œ๋ž ์—ฌ๋ถ€์— ๋”ฐ๋ผ ํ˜ผ์žก๋„๋ฅผ ํŒŒ์•…ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‹ค์ œ ์Šค์œ„์น˜์—์„œ ๋งฅ์ŠคํŒจ์Šค๋ฅผ ๊ตฌํ˜„ํ•˜์˜€์œผ๋ฉฐ, ns-2 ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ๊ธฐ๋ฐ˜ํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์•ˆํ•œ ๊ธฐ๋ฒ•์— ๊ด€ํ•˜์—ฌ ์ •๋Ÿ‰์  ์ˆ˜์น˜ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ณ , ํ”Œ๋กœ์šฐ ์ฒ˜๋ฆฌ ์™„๋ฃŒ ์‹œ๊ฐ„๊ณผ ๋งํฌ ์ฒ˜๋ฆฌ๋Ÿ‰์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ์ด ์žˆ์Œ์„ ๋ณด์—ฌ ์ค€๋‹ค.Chapter 1 Introduction 1 Chapter 2 Background 5 2.1 Data Center Network Topology . . . . . . . . . . . . . . . . . 5 2.2 Multipath Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Multipath Transport Protocol . . . . . . . . . . . . . . . . . . . 7 2.4 Credit-based Congestion Control . . . . . . . . . . . . . . . . 8 Chapter 3 MaxPass 10 3.1 Design Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Switch Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.3 Path Probing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.4 Adaptive Path Selection . . . . . . . . . . . . . . . . . . . . . . .15 3.5 Feedback Control Algorithm . . . . . . . . . . . . . . . . . . . 15 3.6 Credit Stop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 Chapter 4 Evaluation 20 4.1 Ns-2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.1.1 Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.1.2 Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23 4.1.3 Flow Completion Time (FCT) . . . . . . . . . . . . . . . .25 4.2 Testbed Experiments . . . . . . . . . . . . . . . . . . . . . . . . . 25 Chapter 5 Related Work 28 5.1 Centralized . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28 5.2 Decentralized/Distributed . . . . . . . . . . . . . . . . . . . . . .30 Chapter 6 Conclusion 32 ์ดˆ๋ก 38Maste

    Improving multipath routing of TCP flows by network exploration

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    Ethernet switched networks are widely used in enterprise and data center networks. However, they have some drawbacks, mainly that, to prevent loops, they cannot take advantage of multipath topologies to balance traffic. Several multipath routing proposals use link-state protocols and Equal Cost Multi-Path routing (ECMP) to distribute the load over multiple paths. But, these proposals are complex and prone to flow collisions that may degrade performance. This paper studies TCP-Path, a protocol that employs a different approach. It uses a distributed network exploration mechanism based on broadcasting the TCPSYN packet to identify and select the fastest available path to the destination host, on the fly. Our evaluation shows that it improves on ECMP by up to 70% in terms of throughput for elephant flows and by up to 60% in terms of flow completion time for mouse flows. Indeed, network exploration offers a better, yet simple alternative to ECMP-based solutions for multipath topologies. In addition, we also study TCP-Path for elephant flows (TFE), which restricts TCP-Path application to elephant flows to reduce the exploration broadcast overhead and the size of forwarding tables, thus improving its scalability. Although elephant flows represent a small fraction (about 5%) of total flows, they have a major impact on overall performance, as we show in our evaluation. TFE reduces both the overhead incurred during path setup and the size of the forwarding tables by a factor of almost 20. Moreover, it achieves results close to those obtained by TCPPath for elephant flows, especially when working with high loads, and yields significant improvements for all types of flow at medium and high load levels.Comunidad de MadridUniversidad de Alcal

    Review of Path Selection Algorithms with Link Quality and Critical Switch Aware for Heterogeneous Traffic in SDN

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    Software Defined Networking (SDN) introduced network management flexibility that eludes traditional network architecture. Nevertheless, the pervasive demand for various cloud computing services with different levels of Quality of Service requirements in our contemporary world made network service provisioning challenging. One of these challenges is path selection (PS) for routing heterogeneous traffic with end-to-end quality of service support specific to each traffic class. The challenge had gotten the research community\u27s attention to the extent that many PSAs were proposed. However, a gap still exists that calls for further study. This paper reviews the existing PSA and the Baseline Shortest Path Algorithms (BSPA) upon which many relevant PSA(s) are built to help identify these gaps. The paper categorizes the PSAs into four, based on their path selection criteria, (1) PSAs that use static or dynamic link quality to guide PSD, (2) PSAs that consider the criticality of switch in terms of an update operation, FlowTable limitation or port capacity to guide PSD, (3) PSAs that consider flow variabilities to guide PSD and (4) The PSAs that use ML optimization in their PSD. We then reviewed and compared the techniques\u27 design in each category against the identified SDN PSA design objectives, solution approach, BSPA, and validation approaches. Finally, the paper recommends directions for further research

    Optimizing Flow Routing Using Network Performance Analysis

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    Relevant conferences were attended at which work was often presented and several papers were published in the course of this project. โ€ข Muna Al-Saadi, Bogdan V Ghita, Stavros Shiaeles, Panagiotis Sarigiannidis. A novel approach for performance-based clustering and management of network traffic flows, IWCMC, ยฉ2019 IEEE. โ€ข M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: Unsupervised Machine Learning-Based Elephant and Mice Flow Identification, Computing Conference 2021. โ€ข M. Al-Saadi, A. Khan, V. Kelefouras, D. J. Walker, and B. Al-Saadi: SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning, Network, 3(1), pp.218-238, 2023.The main task of a network is to hold and transfer data between its nodes. To achieve this task, the network needs to find the optimal route for data to travel by employing a particular routing system. This system has a specific job that examines each possible path for data and chooses the suitable one and transmit the data packets where it needs to go as fast as possible. In addition, it contributes to enhance the performance of network as optimal routing algorithm helps to run network efficiently. The clear performance advantage that provides by routing procedures is the faster data access. For example, the routing algorithm take a decision that determine the best route based on the location where the data is stored and the destination device that is asking for it. On the other hand, a network can handle many types of traffic simultaneously, but it cannot exceed the bandwidth allowed as the maximum data rate that the network can transmit. However, the overloading problem are real and still exist. To avoid this problem, the network chooses the route based on the available bandwidth space. One serious problem in the network is network link congestion and disparate load caused by elephant flows. Through forwarding elephant flows, network links will be congested with data packets causing transmission collision, congestion network, and delay in transmission. Consequently, there is not enough bandwidth for mice flows, which causes the problem of transmission delay. Traffic engineering (TE) is a network application that concerns with measuring and managing network traffic and designing feasible routing mechanisms to guide the traffic of the network for improving the utilization of network resources. The main function of traffic engineering is finding an obvious route to achieve the bandwidth requirements of the network consequently optimizing the network performance [1]. Routing optimization has a key role in traffic engineering by finding efficient routes to achieve the desired performance of the network [2]. Furthermore, routing optimization can be considered as one of the primary goals in the field of networks. In particular, this goal is directly related to traffic engineering, as it is based on one particular idea: to achieve that traffic is routed according to accurate traffic requirements [3]. Therefore, we can say that traffic engineering is one of the applications of multiple improvements to routing; routing can also be optimized based on other factors (not just on traffic requirements). In addition, these traffic requirements are variable depending on analyzed dataset that considered if it is data or traffic control. In this regard, the logical central view of the Software Defined Network (SDN) controller facilitates many aspects compared to traditional routing. The main challenge in all network types is performance optimization, but the situation is different in SDN because the technique is changed from distributed approach to a centralized one. The characteristics of SDN such as centralized control and programmability make the possibility of performing not only routing in traditional distributed manner but also routing in centralized manner. The first advantage of centralized routing using SDN is the existence of a path to exchange information between the controller and infrastructure devices. Consequently, the controller has the information for the entire network, flexible routing can be achieved. The second advantage is related to dynamical control of routing due to the capability of each device to change its configuration based on the controller commands [4]. This thesis begins with a wide review of the importance of network performance analysis and its role for understanding network behavior, and how it contributes to improve the performance of the network. Furthermore, it clarifies the existing solutions of network performance optimization using machine learning (ML) techniques in traditional networks and SDN environment. In addition, it highlights recent and ongoing studies of the problem of unfair use of network resources by a particular flow (elephant flow) and the possible solutions to solve this problem. Existing solutions are predominantly, flow routing-based and do not consider the relationship between network performance analysis and flow characterization and how to take advantage of it to optimize flow routing by finding the convenient path for each type of flow. Therefore, attention is given to find a method that may describe the flow based on network performance analysis and how to utilize this method for managing network performance efficiently and find the possible integration for the traffic controlling in SDN. To this purpose, characteristics of network flows is identified as a mechanism which may give insight into the diversity in flow features based on performance metrics and provide the possibility of traffic engineering enhancement using SDN environment. Two different feature sets with respect to network performance metrics are employed to characterize network traffic. Applying unsupervised machine learning techniques including Principal Component Analysis (PCA) and k-means cluster analysis to derive a traffic performance-based clustering model. Afterward, thresholding-based flow identification paradigm has been built using pre-defined parameters and thresholds. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding the best flow routing based on the type of flow, to be evaluated against a number of traffic data sources and different performance experiments. The validation process of the novel framework performance has been done by making a performance comparison between SDN-Ryu controller and the proposed SDN-external application based on three factors: throughput, bandwidth,and data transfer rate by conducting two experiments. Furthermore, the proposed method has been validated by using different Data Centre Network (DCN) topologies to demonstrate the effectiveness of the network traffic management solution. The overall validation metrics shows real gains, the results show that 70% of the time, it has high performance with different flows. The proposed routing SDN traffic-engineering paradigm for a particular flow therefore, dynamically provisions network resources among different flow types
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