TCP congestion control algorithm for heterogeneous Internet

Wang, Zhiming; Zeng, Xiaoping; Liu, Xue; Xu, Man; Wen, Ya; Chen, Li

doi:10.1016/j.jnca.2016.03.018

Cited by 26 publications

(13 citation statements)

References 17 publications

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“…Then the performance of the algorithm becomes slow, and transmission flow also decreases. [ 15 , 27 , 28 ]. Since Slow Start, Congestion Avoidance, and Fast Retransmit techniques followed in Tahoe.…”

Section: Related Workmentioning

confidence: 99%

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

Tamizhselvi

Muthuswamy

2021

Peer-to-Peer Netw. Appl.

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In recent years, smartphone users are interested in large volumes to view live videos and sharing video resources over social media (e.g., Youtube, Netflix). The continuous streaming of video in mobile devices faces many challenges in network parameters namely bandwidth estimation, congestion window, throughput, delay, and transcoding is a challenging and time-consuming task. To perform these resource-intensive tasks via mobile is complicated, and hence, the cloud is integrated with smartphones to provide Mobile Cloud Computing (MCC). To resolve the issue, we propose a novel framework called delay aware bandwidth estimation and intelligent video transcoder in mobile cloud. In this paper, we introduced four techniques, namely, Markov Mobile Bandwidth Cloud Estimation (MMBCE), Cloud Dynamic Congestion Window (CDCW), Queue-based Video Processing for Cloud Server (QVPS), and Intelligent Video Transcoding for selecting Server (IVTS). To evaluate the performance of the proposed algorithm, we implemented a testbed using the two mobile configurations and the public cloud server Amazon Web Server (AWS). The study and results in a real environment demonstrate that our proposed framework can improve the QoS requirements and outperforms the existing algorithms. Firstly, MMBCE utilizes the well-known Markov Decision Process (MDP) model to estimate the best bandwidth of mobile using reward function. MMBCE improves the performance of 50% PDR compared with other algorithms. CDCW fits the congestion window and reduces packet loss dynamically. CDCW produces 40% more goodput with minimal PLR. Next, in QVPS, the M/M/S queueing model is processed to reduce the video processing delay and calculates the total service time. Finally, IVTS applies the M/G/N model and reduces 6% utilization of transcoding workload, by intelligently selecting the minimum workload of the transcoding server. The IVTS takes less time in slow and fast mode. The performance analysis and experimental evaluation show that the queueing model reduces the delay by 0.2 ms and the server’s utilization by 20%. Hence, in this work, the cloud minimizes delay effectively to deliver a good quality of video streaming on mobile.

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Section: Related Workmentioning

confidence: 99%

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

Tamizhselvi

Muthuswamy

2021

Peer-to-Peer Netw. Appl.

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“…In order to boost the traffic level in the network, the receiver has to intimate the sender to immediately increase the data traffic generation rate in the network based on the rwnd value. e rwnd value is in the range of 101 to 120, and once the sender receives the rwnd value, it does further computation to increase (19) if ((current_time − last_time) > targetperiod) or (counter > targetcounter) then (20) datarate � totaldata/totaldelay (21) currentpercentage � (datarate/datarate max ) × 100% 22Diffpercentage � 100% − currentpercentage (23) if Diffpercentage ≥ limit high then (24) rwnd � (Diffpercentage − limit low ) (25) else if (Diffpercentage ≥ limit low ) then (26) rwnd � (Diffpercentage − limit low ) × (Diffpercentage − limit low /limit high − limit low ) (27) else if (Diffpercentage ≤ lowest) then (28) rwnd � (lowest − Diffpercentage) + 100 (29) end if (30) Avoiding aggressive reduction in congestion window size (31) if ((current_time − last_time) < targetperiod) and (rwnd < 100) then (32) if rwnd > oldrwnd then (33) rwnd � rwnd − oldrwnd (34) oldrwnd � rwnd (35) else (36) oldrwnd � rwnd (37) end if (38) else (39) oldrwnd � 0 (40) end if (41) last_time � current_time (42) totaldata � totaldelay � counter � 0 (43) end if (44) end procedure ALGORITHM 1: Network congestion estimation at the receiver side.…”

Section: Network Congestion Estimation At Receiver Sidementioning

confidence: 99%

“…is leads to increase in packet delivery time which adversely affects the delay sensitive applications known as bufferbloat problem [13]. Another problem in the loss-based TCP congestion control method is that it detects the network congestion only after the packet gets lost [17,31], that is, when congestion window (cwnd) size is large and user queue at eNodeB overflows. To overcome these problems, the delay-based congestion detection approach was introduced in TCP Vegas [20] and TCP Westwood [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Receiver-Window Adjustment for Delay Reduction in LTE Networks

Adesh

Renuka

2019

Journal of Computer Networks and Communications

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The cellular network keeps the vast capacity of queue space at eNodeBs (base stations) to reduce the queue overflow during the burst in data traffic. However, this adversely affects the delay sensitive applications and user quality of experience. Recently, few researchers have focused on reducing the packet delay, but it has a negative impact on the utilization of network resource by the users. Further, it fails to maintain fairness among the users, when competing for a shared resource in coexistence with conventional TCP or UDP users. Therefore, in this paper, the adaptive receiver-window adjustment (ARWA) algorithm is proposed to efficiently utilize the network resources and ensure fairness among the users in resource competitive environment, which requires slight modification of TCP at both the sender and receiver. The proposed mechanism dynamically varies the receiver window size based on the data rate and delay information of the packets, to enhance the performance of the system. Based on extensive experiments, the results illustrate that the ARWA algorithm reduces the delay of TCP packet and increases fairness among the users. In addition to that, it enhances the packet delivery fraction (PDF) and maintains the throughput of the system. Moreover, it competes with other conventional TCP users for the shared network resources in a fair manner.

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“…The work on new TCP congestion control algorithms includes work on adapting to the changing characteristics of the internet such as the higher and more variable bandwidths offered by cellular accesses [1,11,34,50,52,80,84], possibly using cross layer approaches [6,10,59,61], but also simple tuning of existing TCP such as increasing the size of the initial window [13,16,66,82].…”

Section: Developments In Transport Control Protocolsmentioning

confidence: 99%

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

Mei

Berg²,

Ганчев

et al. 2018

Lecture Notes in Computer Science

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The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like service-oriented architecture (SOA), Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Network as a Service (NaaS) and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring quality and reliability of these services. The goal of this book chapter is to first analyze the state-of-the-art in the area of autonomous control for a reliable IoS and then to identify the main research challenges within it. A general background and high-level description of the current state of knowledge is presented. Then, for each of the three subareas, namely the autonomous management and real-time control, methods and tools for monitoring and service prediction, and smart pricing and competition in multi-domain systems, a brief general introduction and background are presented, and a list of key research challenges is formulated. Keywords: Internet of Services (IoS) Á Autonomous control Autonomous management Á Service monitoring Á Service prediction Smart pricing 3 Autonomous Management and Real-Time Control On a fundamental level, the working group WG1, associated with this research sub-area, was primarily concerned with the management and control of networks, services, applications, and compositions of services or applications. Of particular interest were management and control techniques that span multiple levels, e.g., the network and service level.

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TCP congestion control algorithm for heterogeneous Internet

Cited by 26 publications

References 17 publications

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

Delay - aware bandwidth estimation and intelligent video transcoder in mobile cloud

Adaptive Receiver-Window Adjustment for Delay Reduction in LTE Networks

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

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