Analysis of Performance Improvement on TCP / IP Network with Active Queue Management Controlled Delay

As we all know, the internet is the biggest source of information where we can get information and share information to others. There are many applications in the internet where each application is increasingly increasing its needs in the scope of performance in order to be satisfied by internet users satisfactorily. The higher level of performance required for each application it automatically also leads to increased traffic in the internet network when internet users access this application online. This causes the utilization of internet network is not optimal. The issue of a full buffer is persistent by a queue of data packets that flood the internet network waiting to be served. The buffer trend is always full-hold by the queue of data packets known as Bufferbloat. Initially, all routers on the internet network use the PQM (Passive Queue Management) DropTail mechanism to against Bufferbloat. Therefore in 1998, the IETF (Internet Engineering Task Force) recommended AQM (Active Queue Management) mechanism to be implemented on next-generation Internet routers. Then in 2012, Van Jacobson created an innovative method to become the current Internet service solution that is CoDel. CoDel is an algorithm designed to overcome bufferbloat on network links by setting limits on packet delays in the network. In this research we mainly focus to the influence of traffic load variation on Packetloss, Mean Delay, Mean Jitter and Throughput in TCP / IP network using Passive Queue Management Droptail mechanism using mechanism Active Queue Management Controlled Delay. The concentration is preferred in Active Queue Management Controlled Delay mechanism analysis with variation of traffic load on Packetloss ratio, Average Delay, Average Jitter and Throughput in TCP / IP network. Based on the simulation results obtained, we discuss the advantages Active Queue Management CoDel in improving QoS TCP / IP network. CoDel\u27s performance in improving QoS TCP / IP network for packet loss ratio is better at 26.288%; for average delay is better at 97.755%; for average jitter is better at 69.284% and for throughput is better at 4.448%. This percentage is obtained by packet flow variation from 1 Mbyte to 1 Gbyte. Keywords : Bufferbloat, PQM, AQM, DropTail, CoDe

Delay-oriented active queue management in TCP/IP networks

PhDInternet-based applications and services are pervading everyday life. Moreover, the growing popularity of real-time, time-critical and mission-critical applications set new challenges to the Internet community. The requirement for reducing response time, and therefore latency control is increasingly emphasized. This thesis seeks to reduce queueing delay through active queue management. While mathematical studies and research simulations reveal that complex trade-off relationships exist among performance indices such as throughput, packet loss ratio and delay, etc., this thesis intends to find an improved active queue management algorithm which emphasizes delay control without trading much on other performance indices such as throughput and packet loss ratio. The thesis observes that in TCP/IP network, packet loss ratio is a major reflection of congestion severity or load. With a properly functioning active queue management algorithm, traffic load will in general push the feedback system to an equilibrium point in terms of packet loss ratio and throughput. On the other hand, queue length is a determinant factor on system delay performance while has only a slight influence on the equilibrium. This observation suggests the possibility of reducing delay while maintaining throughput and packet loss ratio relatively unchanged. The thesis also observes that queue length fluctuation is a reflection of both load changes and natural fluctuation in arriving bit rate. Monitoring queue length fluctuation alone cannot distinguish the difference and identify congestion status; and yet identifying this difference is crucial in finding out situations where average queue size and hence queueing delay can be properly controlled and reasonably reduced. However, many existing active queue management algorithms only monitor queue length, and their control policies are solely based on this measurement. In our studies, our novel finding is that the arriving bit rate distribution of all sources contains information which can be a better indication of congestion status and has a correlation with traffic burstiness. And this thesis develops a simple and scalable way to measure its two most important characteristics, namely the mean ii and the variance of the arriving rate distribution. The measuring mechanism is based on a Zombie List mechanism originally proposed and deployed in Stabilized RED to estimate the number of flows and identify misbehaving flows. This thesis modifies the original zombie list measuring mechanism, makes it capable of measuring additional variables. Based on these additional measurements, this thesis proposes a novel modification to the RED algorithm. It utilizes a robust adaptive mechanism to ensure that the system reaches proper equilibrium operating points in terms of packet loss ratio and queueing delay under various loads. Furthermore, it identifies different congestion status where traffic is less bursty and adapts RED parameters in order to reduce average queue size and hence queueing delay accordingly. Using ns-2 simulation platform, this thesis runs simulations of a single bottleneck link scenario which represents an important and popular application scenario such as home access network or SoHo. Simulation results indicate that there are complex trade-off relationships among throughput, packet loss ratio and delay; and in these relationships delay can be substantially reduced whereas trade-offs on throughput and packet loss ratio are negligible. Simulation results show that our proposed active queue management algorithm can identify circumstances where traffic is less bursty and actively reduce queueing delay with hardly noticeable sacrifice on throughput and packet loss ratio performances. In conclusion, our novel approach enables the application of adaptive techniques to more RED parameters including those affecting queue occupancy and hence queueing delay. The new modification to RED algorithm is a scalable approach and does not introduce additional protocol overhead. In general it brings the benefit of substantially reduced delay at the cost of limited processing overhead and negligible degradation in throughput and packet loss ratio. However, our new algorithm is only tested on responsive flows and a single bottleneck scenario. Its effectiveness on a combination of responsive and non-responsive flows as well as in more complicated network topology scenarios is left for future work

Controlling Network Latency in Mixed Hadoop Clusters: Do We Need Active Queue Management?

With the advent of big data, data center applications are processing vast amounts of unstructured and semi-structured data, in parallel on large clusters, across hundreds to thousands of nodes. The highest performance for these batch big data workloads is achieved using expensive network equipment with large buffers, which accommodate bursts in network traffic and allocate bandwidth fairly even when the network is congested. Throughput-sensitive big data applications are, however, often executed in the same data center as latency-sensitive workloads. For both workloads to be supported well, the network must provide both maximum throughput and low latency. Progress has been made in this direction, as modern network switches support Active Queue Management (AQM) and Explicit Congestion Notifications (ECN), both mechanisms to control the level of queue occupancy, reducing the total network latency. This paper is the first study of the effect of Active Queue Management on both throughput and latency, in the context of Hadoop and the MapReduce programming model. We give a quantitative comparison of four different approaches for controlling buffer occupancy and latency: RED and CoDel, both standalone and also combined with ECN and DCTCP network protocol, and identify the AQM configurations that maintain Hadoop execution time gains from larger buffers within 5%, while reducing network packet latency caused by bufferbloat by up to 85%. Finally, we provide recommendations to administrators of Hadoop clusters as to how to improve latency without degrading the throughput of batch big data workloads.The research leading to these results has received funding from the European Unions Seventh Framework Programme (FP7/2007–2013) under grant agreement number 610456 (Euroserver). The research was also supported by the Ministry of Economy and Competitiveness of Spain under the contracts TIN2012-34557 and TIN2015-65316-P, Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), HiPEAC-3 Network of Excellence (ICT- 287759), and the Severo Ochoa Program (SEV-2011-00067) of the Spanish Government.Peer ReviewedPostprint (author's final draft

Analisis Mekanisme Active Queue Management (AQM) Berbasis Controlled Delay (CoDel) terhadap Bufferbloat pada Koneksi Asymmetric Digital Subscriber Line (ADSL)

Bufferbloat adalah kondisi ketika buffer dengan ukuran besar cenderung selalu penuh, menyebabkan antrian panjang di dalam buffer, bila terjadi secara terus menerus dapat menyebabkan delay transmisi menjadi tinggi. Active Queue Management (AQM) merupakan salah satu mekanisme untuk mengatasi bufferbloat. Dalam Tugas Akhir ini, dilakukan implementasi dan pengujian AQM Controlled Delay atau CoDel untuk penanganan bufferbloat, kemudian membandingkannya dengan Drop Tail. CoDel akan diuji terhadap berbagai kondisi bottleneck dan intensitas traffic. Parameter keluaran yang ditinjau adalah latency, throughput dan packet loss. CoDel akan diterapkan pada PC Router berbasis Linux yang terhubung ke Internet menggunakan koneksi ADSL. Hasil pengujian menunjukkan bahwa pada parameter CoDel menunjukkan bahwa nilai target 1 ms lebih baik dari parameter yang direkomendasikan CoDel yang bernilai 5 ms dengan mendapatkan delay yang lebih rendah. Pada perbandingan AQM, CoDel jauh mengungguli Drop Tail bila ditinjau dari kemampuanya dalam menjaga latency yang rendah. Namun, apabila ditinjau dari parameter throughput dan packet loss-nya, CoDel lebih buruk daripada Drop Tail. Kata kunci: bufferbloat, AQM, CoDel, delay, ADS

Improving TCP performance during the intra LTE handover

Abstract-Ensuring a seamless connection when users are moving across radio cells is essential to guarantee a high communication quality. In this paper, performance of TCP during the handover in a Long Term Evolution (LTE) network is investigated. Specifically, mobile users with high bit rates TCP services are considered, and the impacts of the intra LTE handover over their perceived throughput are studied. Due to the mobility of the users across radio cells, the high bandwidth required, and possible network congestions, it is shown that the handover may cause sudden degradation of the quality of the communication if the process is not correctly controlled. To alleviate these problems, three solutions are proposed: fast path switch, handover prediction, and active queue management. The first two solutions avoids excessive delay in the packet delivery during the handover, whereas the second solution acts at the transport network with an active queue management. Simulation results, obtained by an extension of the ns-2 simulator, show that the proposed solutions present advantages, and that the handover prediction used with the active queue management increases TCP performance significantly

Perbandingan Mean Opinion Score (MOS) Pada VoIP Menggunakan Controlled Delay (CoDel) & DropTail

Voice over Internet Protocol (VoIP) merupakan sebuah teknologi yang memungkinkan terjadi komunikasi jarak jauh dengan memanfaatkan jaringan internet sebagai penghubung. Perkembangan VoIP saat ini sangat lah pesat karena trend komunikasi saat ini dikuasai oleh smartphone. Hal tersebut mengakibatkan terjadinya congestion pada jaringan seiring dengan meningkatnya penggunaan layanan VoIP pada smartphone. Permasalahan ini dapat diatasi dengan menerapkan mekanisme antrian pada layanan VoIP dalam mengatasi antrian paket data. Mekanisme antrian ini disebut sebagai Active Queue Management (AQM). Active Queue Management (AQM) menyediakan berbagai macam mekanisme antrian seperti Controlling Delay (CoDel) dan DropTail yang bertujuan untuk mengurangi terjadinya congestion. Dalam penelitian ini diimplementasikan dan dianalisis kualitas layanan VoIP dengan menerapkan Controlled Delay (CoDel) dan DropTail berdasarkan perhitungan delay, throughput, packet loss, dan Mean Opinion Score (MOS) yang didapatkan. Hasil pengujian menunjukan performansi algoritma CoDel lebih baik jika dilihat dari nilai delay dan throughput yang didapat, sedangkan algoritma Droptail secara meyakinkan lebih baik dalam penanganan packet loss. Jika dilihat dari perbandingan nilai MOS, algoritma DropTail lebih baik dari algoritma CoDel secara subjektif ataupun secara objektif. Ini mengindikasikan implementasi algoritma CoDel terhadap layanan VoIP masih lebih buruk daripada DropTai

End-to-end active queue management with Named-Data Networking

The innovative information-based Named-Data Networking (NDN) architecture provides a good opportunity to rethink many of the design decisions that are taken for granted in the Internet today. For example, active queue management (AQM) tasks have been traditionally implemented in the routers to alleviate network congestion before their buffers fill up. However, AQM operations could be performed on an end-to-end basis by taking advantage of NDN features. In this paper, we provide an implementation of an AQM algorithm for the NDN architecture that we use to drive a classical AIMD-based congestion control protocol at the receivers. To accomplish this, we take advantage of the 64-bit Congestion Mark field present in the link layer of NDN packets to encode both rate and delay information about each transmission queue along a network path. In order to make the solution scalable, this information is delivered stochastically, guaranteeing that receivers get accurate and updated information about every pertinent queue. This information is enough to implement the well-known controlled delay (CoDel) AQM algorithm. Simulation results show that our client-located CoDel implementation is able to react to congestion when the bottleneck queuing delay surpasses the 5 ms target set by the usual in-network CoDel implementation and, at the same time, get a fair and efficient share of the available transmission capacityAgencia estatal de investigación | Ref. PID2020-113240RB-I00Universidade de Vigo/CISU

Active Queue Management for Fair Resource Allocation in Wireless Networks

This paper investigates the interaction between end-to-end flow control and MAC-layer scheduling on wireless links. We consider a wireless network with multiple users receiving information from a common access point; each user suffers fading, and a scheduler allocates the channel based on channel quality,but subject to fairness and latency considerations. We show that the fairness property of the scheduler is compromised by the transport layer flow control of TCP New Reno. We provide a receiver-side control algorithm, CLAMP, that remedies this situation. CLAMP works at a receiver to control a TCP sender by setting the TCP receiver's advertised window limit, and this allows the scheduler to allocate bandwidth fairly between the users