ABSTRACT
This paper prefers a fuzzy-logic-based sending rate adaption scheme named FSR(Fuzzy Sending Rate) intending to improve the evenness of TCPFriendly Multicast Congestion Control (TFMCC). To mitigate fluctuation of sending rate for TFMCC sender, FSR intends, five actions and link utilization for tuning sending rate and uses a fuzzy controller to determine which operation should be reaped according to the feedback information from CLR (current limiting receiver). Asymmetrical membership functions and biased fuzzy inference rules make FSR as friendly to TCP flows as TFMCC. Simulation results show that FSR has exceptional smoothness and fine TCP Friendliness.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A packet drop guesser module for congestion Control protocols for high speed ...ijcseit
This document summarizes research on congestion control protocols for high-speed networks. It discusses how existing protocols like CUBIC consider every packet drop as congestion and reduce throughput. The document proposes a packet drop guesser module using k-NN to differentiate between packet drops due to congestion versus other factors like noise. It evaluates CUBIC integrated with this module and finds significant performance improvements over CUBIC alone in noisy conditions. Related work on high-speed protocols like BIC, FAST and CUBIC is also summarized.
IMPACT OF CONTENTION WINDOW ON CONGESTION CONTROL ALGORITHMS FOR WIRELESS ADH...cscpconf
TCP congestion control mechanism is highly dependent on MAC layer Backoff algorithms that
predict the optimal Contention Window size to increase the TCP performance in wireless adhoc
network. This paper critically examines the impact of Contention Window in TCP congestion
control approaches. The modified TCP congestion control method gives the stability of
congestion window which provides higher throughput and shorter delay than the traditional TCP. Various Backoff algorithms that are used to adjust Contention Window are simulatedusing NS2 along with modified TCP and their performance are analyzed to depict the influence of Contention Window in TCP performance considering the metrics such as throughput, delay, packet loss and end-to-end delay
This document discusses TCP flow and congestion control in high speed networks. It covers topics such as TCP flow control using a credit allocation scheme, TCP header fields for flow control, credit allocation flexibility, effects of window size, complicating factors, retransmission strategy using timers, adaptive retransmission timer algorithms, implementation policy options, congestion control difficulties, slow start, dynamic window sizing, fast retransmit, fast recovery, limited transmit, performance of TCP over ATM networks using UBR service, effects of switch buffer size, observations, partial packet discard techniques, and TCP over ABR service.
The document describes a proposed Fuzzy-AQM algorithm for congestion control in wireless ad-hoc networks. It begins by summarizing common Active Queue Management (AQM) policies and their issues. It then discusses congestion in ad-hoc networks and how the proposed Fuzzy-AQM algorithm uses fuzzy logic rules based on queue size and neighbor density to dynamically calculate packet drop probability, aiming to improve network performance. Simulation results showed the effectiveness of Fuzzy-AQM for congestion detection and avoidance.
Traffic and Congestion Control in ATM Networks Chapter 13daniel ayalew
This document discusses traffic and congestion control in ATM networks. It describes how ATM networks require different approaches than other networks due to factors like high speeds, small cell sizes, and the need to support both real-time and bursty traffic. It outlines the ITU-T and ATM forum frameworks for congestion control, including schemes for delay-sensitive traffic like voice and video as well as bursty traffic using techniques like Available Bit Rate and Guaranteed Frame Rate. Key issues discussed include latency effects, cell delay variation, and how the network contributes to delay variation.
Fault tolerant wireless sensor mac protocol for efficient collision avoidancegraphhoc
In sensor networks communication by broadcast methods involves many hazards, especially collision. Several MAC layer protocols have been proposed to resolve the problem of collision namely ARBP, where the best achieved success rate is 90%. We hereby propose a MAC protocol which achieves a greater success rate (Success rate is defined as the percentage of delivered packets at the source reaching the destination successfully) by reducing the number of collisions, but by trading off the average propagation delay of transmission. Our proposed protocols are also shown to be more energy efficient in terms of energy dissipation per message delivery, compared to the currently existing protocol.
This chapter discusses TCP traffic control and congestion control. It introduces TCP flow control using a credit allocation scheme and sliding window mechanism. TCP congestion control dynamically adjusts the transmission window size in response to packet loss to control bandwidth. Key TCP congestion control algorithms discussed are slow start, congestion avoidance, fast retransmit, and fast recovery. The performance of TCP over ATM networks is also examined.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A packet drop guesser module for congestion Control protocols for high speed ...ijcseit
This document summarizes research on congestion control protocols for high-speed networks. It discusses how existing protocols like CUBIC consider every packet drop as congestion and reduce throughput. The document proposes a packet drop guesser module using k-NN to differentiate between packet drops due to congestion versus other factors like noise. It evaluates CUBIC integrated with this module and finds significant performance improvements over CUBIC alone in noisy conditions. Related work on high-speed protocols like BIC, FAST and CUBIC is also summarized.
IMPACT OF CONTENTION WINDOW ON CONGESTION CONTROL ALGORITHMS FOR WIRELESS ADH...cscpconf
TCP congestion control mechanism is highly dependent on MAC layer Backoff algorithms that
predict the optimal Contention Window size to increase the TCP performance in wireless adhoc
network. This paper critically examines the impact of Contention Window in TCP congestion
control approaches. The modified TCP congestion control method gives the stability of
congestion window which provides higher throughput and shorter delay than the traditional TCP. Various Backoff algorithms that are used to adjust Contention Window are simulatedusing NS2 along with modified TCP and their performance are analyzed to depict the influence of Contention Window in TCP performance considering the metrics such as throughput, delay, packet loss and end-to-end delay
This document discusses TCP flow and congestion control in high speed networks. It covers topics such as TCP flow control using a credit allocation scheme, TCP header fields for flow control, credit allocation flexibility, effects of window size, complicating factors, retransmission strategy using timers, adaptive retransmission timer algorithms, implementation policy options, congestion control difficulties, slow start, dynamic window sizing, fast retransmit, fast recovery, limited transmit, performance of TCP over ATM networks using UBR service, effects of switch buffer size, observations, partial packet discard techniques, and TCP over ABR service.
The document describes a proposed Fuzzy-AQM algorithm for congestion control in wireless ad-hoc networks. It begins by summarizing common Active Queue Management (AQM) policies and their issues. It then discusses congestion in ad-hoc networks and how the proposed Fuzzy-AQM algorithm uses fuzzy logic rules based on queue size and neighbor density to dynamically calculate packet drop probability, aiming to improve network performance. Simulation results showed the effectiveness of Fuzzy-AQM for congestion detection and avoidance.
Traffic and Congestion Control in ATM Networks Chapter 13daniel ayalew
This document discusses traffic and congestion control in ATM networks. It describes how ATM networks require different approaches than other networks due to factors like high speeds, small cell sizes, and the need to support both real-time and bursty traffic. It outlines the ITU-T and ATM forum frameworks for congestion control, including schemes for delay-sensitive traffic like voice and video as well as bursty traffic using techniques like Available Bit Rate and Guaranteed Frame Rate. Key issues discussed include latency effects, cell delay variation, and how the network contributes to delay variation.
Fault tolerant wireless sensor mac protocol for efficient collision avoidancegraphhoc
In sensor networks communication by broadcast methods involves many hazards, especially collision. Several MAC layer protocols have been proposed to resolve the problem of collision namely ARBP, where the best achieved success rate is 90%. We hereby propose a MAC protocol which achieves a greater success rate (Success rate is defined as the percentage of delivered packets at the source reaching the destination successfully) by reducing the number of collisions, but by trading off the average propagation delay of transmission. Our proposed protocols are also shown to be more energy efficient in terms of energy dissipation per message delivery, compared to the currently existing protocol.
This chapter discusses TCP traffic control and congestion control. It introduces TCP flow control using a credit allocation scheme and sliding window mechanism. TCP congestion control dynamically adjusts the transmission window size in response to packet loss to control bandwidth. Key TCP congestion control algorithms discussed are slow start, congestion avoidance, fast retransmit, and fast recovery. The performance of TCP over ATM networks is also examined.
This document discusses integrated services architecture (ISA) and differentiated services (DS) for providing quality of service (QoS) in computer networks. It describes the components and functions of ISA, including reservation protocol, admission control, routing, queuing disciplines, and services. It also covers traffic classification, scheduling, and dropping policies implemented in routers. Random early detection (RED) is presented as a proactive packet discard mechanism for congestion management. Differentiated services is introduced as a simpler alternative to ISA that uses traffic classes in packet headers to provide different performance levels.
Link-Level Flow and Error Control Chapter11daniel ayalew
This document summarizes flow and error control mechanisms at the link layer. It discusses stop-and-wait, go-back-N, and selective reject protocols for automatic repeat request (ARQ). These protocols use sliding windows, acknowledgments, and retransmissions to provide reliable data transfer over unreliable links. Performance analysis is presented showing how throughput is affected by window size, propagation delay, and error rates. High-level data link control (HDLC) is also introduced as an important link layer protocol.
Effective Router Assisted Congestion Control for SDN IJECEIAES
This document proposes a new congestion control method called PACEC (Path Associativity Centralized Congestion Control) that works within the Software Defined Networking (SDN) framework. PACEC aims to overcome weaknesses of traditional Router Assisted Congestion Control (RACC) methods by utilizing global network information available in SDN. It calculates an aggregate rate for the entire data path rather than individual links. The controller collects switch utilization data and uses it to determine the path rate (Rp), updating it each control period. Simulation results show PACEC achieves better efficiency and fairness than TCP and RCP.
This document discusses admission control in internet networks. It provides an overview of quality of service (QoS) and how organizations can achieve QoS through tools like jitter buffering and traffic shaping. It then discusses several techniques used for admission control, including scheduling, traffic shaping, and resource reservation. Specific admission control systems are also outlined, such as asynchronous transfer mode (ATM), audio video bridging (AVB), IEEE 1394, integrated services, and the public switched telephone network (PSTN).
This document provides an overview of high speed networks including Frame Relay networks, Asynchronous Transfer Mode (ATM), ATM protocol architecture, logical connections, cells, service categories, and high speed LANs. It discusses the architecture, user data transfer, and call control of Frame Relay networks. For ATM, it describes the protocol model, logical connections, cells, adaptation layer, and service categories. It also provides an introduction to emerging high speed LAN technologies.
This document provides an overview of the BICC protocol and application in R4 networks. It discusses BICC protocol structure and message introduction, signaling flows including examples of call setup with forward and backward bearer establishment. It also covers topics like BICC protocol model, message structure, blocking and unblocking of call instances, main BICC messages, tunnel bearer setup, codec negotiation and call release scenarios. Signaling flows and examples are provided to illustrate different call setup scenarios.
Traffic management provides optimal utilization of network resources by managing network traffic and providing service guarantees to user connections. It includes functions such as traffic contract management, traffic shaping, traffic policing, priority control, flow control, and congestion control. Connection admission control is used to determine whether new connection requests can be accepted while ensuring sufficient resources and quality of service for existing connections. Traffic shaping techniques such as leaky bucket algorithm alter traffic characteristics to make them more predictable and conforming to network requirements.
Improvement of Congestion window and Link utilization of High Speed Protocols...IOSR Journals
This document summarizes a research paper that proposes using a k-nearest neighbors (k-NN) algorithm to help high-speed transport layer protocols like CUBIC better distinguish between packet drops due to network congestion versus other factors like noise. The k-NN algorithm would analyze patterns in packet drop history to classify new drops, helping protocols avoid unnecessary window size reductions when drops are not actually due to congestion. The document provides background on high-speed protocols, issues like underutilization from treating all drops as congestion, and how incorporating k-NN classification could improve protocols' performance in noisy network conditions.
This document discusses various transport layer protocols for mobile networks. It begins with an overview of TCP and UDP, and then describes several strategies for improving TCP performance over mobile networks, including indirect TCP (I-TCP), snooping TCP, and Mobile TCP. It also discusses congestion control strategies like slow start and fast retransmit. Overall, the document analyzes how TCP can be optimized through techniques like connection splitting, buffering, and selective retransmission to better accommodate the characteristics of wireless networks.
The network layer is concerned with routing packets from the source to the destination across multiple networks. It must understand the topology of connected networks and choose optimal paths while avoiding overloading some lines. The network layer provides either connection-oriented or connectionless services to the transport layer and deals with differences when sources and destinations are in different networks. Dynamic routing algorithms like distance vector routing are used to adaptively route packets based on current network conditions.
This document discusses unit 2 of a course on high speed networks. It covers queuing analysis and models, including single server queues, effects of congestion and congestion control, traffic management, and congestion control in packet switching networks and frame relay. It provides an overview of key concepts like performance measures, solution methodologies, queuing system concepts, stability and steady-state, and causes of delay and bottlenecks. It also discusses analytical and simulation approaches to modeling queues and provides examples.
Congestion Control in Computer Networks - ATM and TCPAttila Balazs
This document discusses congestion control in networks using ATM and TCP protocols. It defines network congestion and outlines various congestion control possibilities including admission control, traffic access control, packet scheduling, buffer management, and flow control. It then describes the key concepts and congestion control mechanisms for ATM, including call admission control, the GCRA algorithm, and use of resource management cells. It also outlines TCP congestion control using additive increase/multiplicative decrease and slow start/fast retransmit, and evaluates the pros and cons of ATM and TCP approaches.
Transmitting urgent data using ANKM method.IRJET Journal
This document proposes a new mechanism called ANKM to transmit urgent data in wireless sensor networks. It discusses existing transport layer protocols that provide reliability but do not prioritize urgent data transmission. The ANKM mechanism uses an assured path to transmit urgent data with reliability while blocking normal data packets. It operates in three phases - selecting an assured path, transmitting urgent data along that path using reliability mechanisms, and then resuming normal network operations. The goal is to transmit urgent data with reliability and congestion control while still allowing normal data to flow when the urgent transmission is not occurring.
The document discusses various transport layer protocols for mobile computing environments:
- Traditional TCP faces problems with high error rates and mobility-induced packet losses in wireless networks. It can lead to severe performance degradation.
- Indirect TCP segments the TCP connection and uses a specialized TCP for the wireless link, isolating wireless errors. But it loses end-to-end semantics.
- Snooping TCP buffers packets near the mobile host and performs local retransmissions transparently. But wireless errors can still propagate to the server.
- Mobile TCP splits the connection and uses different mechanisms on each segment. It chokes the sender window during disconnections to avoid retransmissions and slow starts. This maintains throughput during
Mobile transport layer - traditional TCPVishal Tandel
This document summarizes several mechanisms proposed to improve TCP performance in wireless networks. It discusses approaches like indirect TCP, snooping TCP, and mobile TCP that split the TCP connection to isolate the wireless link. It also covers fast retransmit/recovery techniques, transmission freezing, and selective retransmission to more efficiently handle packet losses due to mobility. While each approach aims to address TCP issues in wireless networks, they often do so by mixing layers or requiring changes to the basic TCP protocol stack.
This document presents an overview of computer network congestion and congestion control techniques. It defines congestion as occurring when too many packets are present in a network link, causing queues to overflow and packets to drop. It then discusses factors that can cause congestion as well as the costs. It outlines open-loop and closed-loop congestion control approaches. Specific algorithms covered include leaky bucket, token bucket, choke packets, hop-by-hop choke packets, and load shedding. The document concludes by noting the importance of efficient congestion control techniques with room for improvement.
This document discusses network congestion and congestion control. It defines congestion as occurring when there are too many packets present in part of a subnet, degrading performance. Factors that can influence congestion include bursty traffic patterns, insufficient router memory or bandwidth, and slow router processing. Congestion control techniques aim to prevent or remove congestion through open-loop methods like traffic scheduling, or closed-loop methods using feedback to adjust system operations. Traffic-aware routing and admission control are also discussed as ways to minimize congestion.
Recital Study of Various Congestion Control Protocols in wireless networkiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document discusses and compares several congestion control protocols for wireless networks, including TCP, RCP, and RCP+. It implemented an enhanced version of RCP+ in the NS-2 simulator. Simulation results showed that the proposed approach achieved higher throughput and packet delivery ratio than TCP and RCP+ in a wireless network with 10-50 nodes, with performance degrading as the number of nodes increased beyond 20 due to increased congestion. The paper analyzes the mechanisms and equations of each protocol and argues the proposed approach combines benefits of improved AIMD and RCP+ to address their individual shortcomings.
Transmission Control Protocol (TCP) is a fundamental protocol of the Internet Protocol Suite. TCP complements the Internet Protocol (IP), therefore it is common to refer to the internet protocol suit as TCP/IP. TCP is used for error detection, detection of packet loss or out of order delivery of data. TCP requests retransmission, rearranges data and helps with network congestion.
Several congestion control algorithms have been developed, over the last years, to improve TCP's performance over various technologies and network conditions.
The purpose of this assignment is to present TCP, network congestion, congestion algorithms and simulate different algorithms in different network conditions to measure their performance. For this assignment's needs, OPNET IT Guru Academic Edition software was used to accomplish the reproduction of projects that have been already published and gave the wanted results.
Abstract - The Transmission Control Protocol (TCP) is
connection oriented, reliable and end-to-end protocol that support
flow and congestion control, with the evolution and rapid growth
of the internet and emergence of internet of things IoT, flow and
congestion have clear impact in the network performance. In this
paper we study congestion control mechanisms Tahoe, Reno,
Newreno, SACK and Vegas, which are introduced to control
network utilization and increase throughput, in the performance
evaluation we evaluate the performance metrics such as
throughput, packets loss, delivery and reveals impact of the cwnd.
Showing that SACK had done better performance in terms of
numbers of packets sent, throughput and delivery ratio than
Newreno, Vegas shows the best performance of all of them.
This document discusses integrated services architecture (ISA) and differentiated services (DS) for providing quality of service (QoS) in computer networks. It describes the components and functions of ISA, including reservation protocol, admission control, routing, queuing disciplines, and services. It also covers traffic classification, scheduling, and dropping policies implemented in routers. Random early detection (RED) is presented as a proactive packet discard mechanism for congestion management. Differentiated services is introduced as a simpler alternative to ISA that uses traffic classes in packet headers to provide different performance levels.
Link-Level Flow and Error Control Chapter11daniel ayalew
This document summarizes flow and error control mechanisms at the link layer. It discusses stop-and-wait, go-back-N, and selective reject protocols for automatic repeat request (ARQ). These protocols use sliding windows, acknowledgments, and retransmissions to provide reliable data transfer over unreliable links. Performance analysis is presented showing how throughput is affected by window size, propagation delay, and error rates. High-level data link control (HDLC) is also introduced as an important link layer protocol.
Effective Router Assisted Congestion Control for SDN IJECEIAES
This document proposes a new congestion control method called PACEC (Path Associativity Centralized Congestion Control) that works within the Software Defined Networking (SDN) framework. PACEC aims to overcome weaknesses of traditional Router Assisted Congestion Control (RACC) methods by utilizing global network information available in SDN. It calculates an aggregate rate for the entire data path rather than individual links. The controller collects switch utilization data and uses it to determine the path rate (Rp), updating it each control period. Simulation results show PACEC achieves better efficiency and fairness than TCP and RCP.
This document discusses admission control in internet networks. It provides an overview of quality of service (QoS) and how organizations can achieve QoS through tools like jitter buffering and traffic shaping. It then discusses several techniques used for admission control, including scheduling, traffic shaping, and resource reservation. Specific admission control systems are also outlined, such as asynchronous transfer mode (ATM), audio video bridging (AVB), IEEE 1394, integrated services, and the public switched telephone network (PSTN).
This document provides an overview of high speed networks including Frame Relay networks, Asynchronous Transfer Mode (ATM), ATM protocol architecture, logical connections, cells, service categories, and high speed LANs. It discusses the architecture, user data transfer, and call control of Frame Relay networks. For ATM, it describes the protocol model, logical connections, cells, adaptation layer, and service categories. It also provides an introduction to emerging high speed LAN technologies.
This document provides an overview of the BICC protocol and application in R4 networks. It discusses BICC protocol structure and message introduction, signaling flows including examples of call setup with forward and backward bearer establishment. It also covers topics like BICC protocol model, message structure, blocking and unblocking of call instances, main BICC messages, tunnel bearer setup, codec negotiation and call release scenarios. Signaling flows and examples are provided to illustrate different call setup scenarios.
Traffic management provides optimal utilization of network resources by managing network traffic and providing service guarantees to user connections. It includes functions such as traffic contract management, traffic shaping, traffic policing, priority control, flow control, and congestion control. Connection admission control is used to determine whether new connection requests can be accepted while ensuring sufficient resources and quality of service for existing connections. Traffic shaping techniques such as leaky bucket algorithm alter traffic characteristics to make them more predictable and conforming to network requirements.
Improvement of Congestion window and Link utilization of High Speed Protocols...IOSR Journals
This document summarizes a research paper that proposes using a k-nearest neighbors (k-NN) algorithm to help high-speed transport layer protocols like CUBIC better distinguish between packet drops due to network congestion versus other factors like noise. The k-NN algorithm would analyze patterns in packet drop history to classify new drops, helping protocols avoid unnecessary window size reductions when drops are not actually due to congestion. The document provides background on high-speed protocols, issues like underutilization from treating all drops as congestion, and how incorporating k-NN classification could improve protocols' performance in noisy network conditions.
This document discusses various transport layer protocols for mobile networks. It begins with an overview of TCP and UDP, and then describes several strategies for improving TCP performance over mobile networks, including indirect TCP (I-TCP), snooping TCP, and Mobile TCP. It also discusses congestion control strategies like slow start and fast retransmit. Overall, the document analyzes how TCP can be optimized through techniques like connection splitting, buffering, and selective retransmission to better accommodate the characteristics of wireless networks.
The network layer is concerned with routing packets from the source to the destination across multiple networks. It must understand the topology of connected networks and choose optimal paths while avoiding overloading some lines. The network layer provides either connection-oriented or connectionless services to the transport layer and deals with differences when sources and destinations are in different networks. Dynamic routing algorithms like distance vector routing are used to adaptively route packets based on current network conditions.
This document discusses unit 2 of a course on high speed networks. It covers queuing analysis and models, including single server queues, effects of congestion and congestion control, traffic management, and congestion control in packet switching networks and frame relay. It provides an overview of key concepts like performance measures, solution methodologies, queuing system concepts, stability and steady-state, and causes of delay and bottlenecks. It also discusses analytical and simulation approaches to modeling queues and provides examples.
Congestion Control in Computer Networks - ATM and TCPAttila Balazs
This document discusses congestion control in networks using ATM and TCP protocols. It defines network congestion and outlines various congestion control possibilities including admission control, traffic access control, packet scheduling, buffer management, and flow control. It then describes the key concepts and congestion control mechanisms for ATM, including call admission control, the GCRA algorithm, and use of resource management cells. It also outlines TCP congestion control using additive increase/multiplicative decrease and slow start/fast retransmit, and evaluates the pros and cons of ATM and TCP approaches.
Transmitting urgent data using ANKM method.IRJET Journal
This document proposes a new mechanism called ANKM to transmit urgent data in wireless sensor networks. It discusses existing transport layer protocols that provide reliability but do not prioritize urgent data transmission. The ANKM mechanism uses an assured path to transmit urgent data with reliability while blocking normal data packets. It operates in three phases - selecting an assured path, transmitting urgent data along that path using reliability mechanisms, and then resuming normal network operations. The goal is to transmit urgent data with reliability and congestion control while still allowing normal data to flow when the urgent transmission is not occurring.
The document discusses various transport layer protocols for mobile computing environments:
- Traditional TCP faces problems with high error rates and mobility-induced packet losses in wireless networks. It can lead to severe performance degradation.
- Indirect TCP segments the TCP connection and uses a specialized TCP for the wireless link, isolating wireless errors. But it loses end-to-end semantics.
- Snooping TCP buffers packets near the mobile host and performs local retransmissions transparently. But wireless errors can still propagate to the server.
- Mobile TCP splits the connection and uses different mechanisms on each segment. It chokes the sender window during disconnections to avoid retransmissions and slow starts. This maintains throughput during
Mobile transport layer - traditional TCPVishal Tandel
This document summarizes several mechanisms proposed to improve TCP performance in wireless networks. It discusses approaches like indirect TCP, snooping TCP, and mobile TCP that split the TCP connection to isolate the wireless link. It also covers fast retransmit/recovery techniques, transmission freezing, and selective retransmission to more efficiently handle packet losses due to mobility. While each approach aims to address TCP issues in wireless networks, they often do so by mixing layers or requiring changes to the basic TCP protocol stack.
This document presents an overview of computer network congestion and congestion control techniques. It defines congestion as occurring when too many packets are present in a network link, causing queues to overflow and packets to drop. It then discusses factors that can cause congestion as well as the costs. It outlines open-loop and closed-loop congestion control approaches. Specific algorithms covered include leaky bucket, token bucket, choke packets, hop-by-hop choke packets, and load shedding. The document concludes by noting the importance of efficient congestion control techniques with room for improvement.
This document discusses network congestion and congestion control. It defines congestion as occurring when there are too many packets present in part of a subnet, degrading performance. Factors that can influence congestion include bursty traffic patterns, insufficient router memory or bandwidth, and slow router processing. Congestion control techniques aim to prevent or remove congestion through open-loop methods like traffic scheduling, or closed-loop methods using feedback to adjust system operations. Traffic-aware routing and admission control are also discussed as ways to minimize congestion.
Recital Study of Various Congestion Control Protocols in wireless networkiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document discusses and compares several congestion control protocols for wireless networks, including TCP, RCP, and RCP+. It implemented an enhanced version of RCP+ in the NS-2 simulator. Simulation results showed that the proposed approach achieved higher throughput and packet delivery ratio than TCP and RCP+ in a wireless network with 10-50 nodes, with performance degrading as the number of nodes increased beyond 20 due to increased congestion. The paper analyzes the mechanisms and equations of each protocol and argues the proposed approach combines benefits of improved AIMD and RCP+ to address their individual shortcomings.
Transmission Control Protocol (TCP) is a fundamental protocol of the Internet Protocol Suite. TCP complements the Internet Protocol (IP), therefore it is common to refer to the internet protocol suit as TCP/IP. TCP is used for error detection, detection of packet loss or out of order delivery of data. TCP requests retransmission, rearranges data and helps with network congestion.
Several congestion control algorithms have been developed, over the last years, to improve TCP's performance over various technologies and network conditions.
The purpose of this assignment is to present TCP, network congestion, congestion algorithms and simulate different algorithms in different network conditions to measure their performance. For this assignment's needs, OPNET IT Guru Academic Edition software was used to accomplish the reproduction of projects that have been already published and gave the wanted results.
Abstract - The Transmission Control Protocol (TCP) is
connection oriented, reliable and end-to-end protocol that support
flow and congestion control, with the evolution and rapid growth
of the internet and emergence of internet of things IoT, flow and
congestion have clear impact in the network performance. In this
paper we study congestion control mechanisms Tahoe, Reno,
Newreno, SACK and Vegas, which are introduced to control
network utilization and increase throughput, in the performance
evaluation we evaluate the performance metrics such as
throughput, packets loss, delivery and reveals impact of the cwnd.
Showing that SACK had done better performance in terms of
numbers of packets sent, throughput and delivery ratio than
Newreno, Vegas shows the best performance of all of them.
Comparative Analysis of Different TCP Variants in Mobile Ad-Hoc Network partha pratim deb
The document analyzes the performance of different TCP variants (New Reno, Reno, Tahoe) with MANET routing protocols (AODV, DSR, TORA) through simulation. It finds that in scenarios with 3 and 5 nodes, AODV has better throughput than DSR and TORA for all TCP variants. Throughput decreases for all variants as node count increases. New Reno provides multiple packet loss recovery and is the best choice for AODV in MANETs due to its consistent performance with changes in node count. Further analysis of additional protocols and TCP variants is recommended.
Exponential MLWDF (EXP-MLWDF) Downlink Scheduling Algorithm Evaluated in LTE ...IJECEIAES
The document summarizes a research paper that evaluates the performance of a new downlink scheduling algorithm called Exponential Modified Largest Weighted Delay First (EXP-MLWDF) in an LTE network under high mobility and dense user scenarios. It compares the performance of EXP-MLWDF to other scheduling algorithms such as Proportional Fair (PF), Exponential Proportional Fairness (EXP/PF), Logarithm Rule (LOG-Rule), Exponential Rule (EXP-Rule) and Modified Largest Weighted Delay First (MLWDF) in terms of system throughput, delay and packet loss ratio based on simulations. The simulations showed that EXP-MLWDF satisfies quality of service requirements for real-time traffic better than the other algorithms
PERFORMANCE EVALUATION OF SELECTED E2E TCP CONGESTION CONTROL MECHANISM OVER ...ijwmn
TCP is one of the main protocols that govern the Internet traffic nowadays. However, it suffers significant
performance degradation over wireless links. Since wireless networks are leading the communication
technologies recently, it is imperative to introduce effective solutions for the TCP congestion control
mechanisms over such networks. In this research four End-to-End TCP implementations are discussed,
they are TCP Westwood, Hybla, Highspeed, and NewReno. The performance of these variants is compared
using LTE emulated environment in terms of throughput, delay, and fairness. Ns-3 simulator is used to
simulate the LTE networks environment. The simulation results showed that TCP Highspeed achieves the
best throughput results. Although TCP Westwood recorded the lowest latency values comparing to others,
it behaved unfairly among different traffic flows. Moreover, TCP Hybla demonstrated the best fairness
behaviour among other TCP variants
A Packet Drop Guesser Module for Congestion Control Protocols for High speed ...ijcseit
Different high speed Transport layer protocols have been designed and proposed in the literature to
improve the performance of standard TCP on high BDP links. They are mainly different in their increase
and decrease formulas of their respective congestion control algorithm. Most of these high speed protocols
consider every packet drop in the network as an indication of congestion and they immediately reduce their
congestion window size. Such an approach will usually result in under utilization of available bandwidth in
case of noisy channel conditions. We take CUBIC as a test case and have compared its performance in
case of normal and noisy channel conditions. The throughput of CUBIC was drastically degraded from
50Mbps to 0.5Mbps when we introduced a random packet drops with 0.001 probability. When the
probability of the packet drops increases then the throughput gets decreases. Indeed, we need to
complement existing congestion control algorithms with some intelligent mechanisms that can differentiate
whether a certain packet drop is because of congestion or channel error thus avoid unnecessary window
reduction. In order to distinguish between packets drops, we have developed a k-NN based module to guess
whether the packet drops are due to the congestion or any other reasons. After integrating this module with
CUBIC algorithm, we have observed significant performance improvement.
Towards Seamless TCP Congestion Avoidance in Multiprotocol EnvironmentsIDES Editor
In this paper we explore the area of congestion
avoidance in computer networks. We provide a brief overview
of the current state of the art in congestion avoidance and also
list our extension to the TCP congestion avoidance mechanism.
This extension was previously published on an international
forum and in this paper we describe an improved version which
allows multiprotocol support. We list preliminary results
carried out in a simulation environment.
New introduced approach called Advanced Notification
Congestion System (ACNS) allows TCP flows prioritization
based on the TCP flow age and priority carried in the header
of the network layer protocol. The aim of this approach is to
provide more bandwidth for young and high prioritized TCP
flows by means of penalizing old greedy flows with a low
priority. Using ACNS, substantial network performance
increase can be achieved.
Fuzzy type 1 PID controllers design for TCP/AQM wireless networksnooriasukmaningtyas
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In the last few years, video streaming facilities over TCP or UDP, such as YouTube, Facetime, Daily-motion, Mobile video calling have become more and more popular. The important
challenge in streaming broadcasting over the Internet is to spread the uppermost potential quality,
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Media Data Congestion Control protocol (SMDCC), a new adaptive broadcasting streaming
congestion management protocol in which the connection’s data packets transmission frequency is
adjusted allowing to the dynamic bandwidth share of connection using SMDCC, the bandwidth share
of a connection is projected using algorithms similar to those introduced in TCP Westwood. SMDCC
avoids the Slow Jump phase in TCP. As a result, SMDCC does not show the pronounced rate
alternations distinguishing of modern TCP, so providing congestion control that is more appropriate
for streaming broadcasting applications. Besides, SMDCC is fair, sharing the bandwidth equitably
among a set of SMDCC connections. Main benefit is robustness when packet harms are due to
indiscriminate errors, which is typical of wireless links and is becoming an increasing concern due to
the emergence of wireless Internet access. In the presence of indiscriminate errors, SMDCC is also
approachable to TCP Tahoe and Reno (TTR). We provide simulation results using the ns3 simulator
for our protocol running together with TCP Tahoe and Reno.
Iaetsd an effective approach to eliminate tcp incastIaetsd Iaetsd
This document proposes an Incast Congestion Control for TCP (ICTCP) scheme to eliminate TCP incast collapse in datacenter environments. TCP incast collapse occurs when multiple synchronized servers send data to the same receiver in parallel, overwhelming the switch buffer and causing packet loss. ICTCP is a receiver-side approach that proactively adjusts the TCP receive window size of connections to control their aggregate burstiness and prevent switch buffer overflow before packet loss occurs. It estimates available bandwidth and uses this as a quota to coordinate receive window increases. For each connection, the receive window is adjusted based on the ratio of the difference between measured and expected throughput. This allows adaptive tuning of receive windows to meet sender throughput needs while avoiding congest
Improved SCTP Scheme To Overcome Congestion Losses Over ManetIJERA Editor
Transmission control conventions have been utilized for data transmission process. TCP has been pre-possessed
for information transmission over wired correspondence having diverse transfer speeds and message delays over
the system. TCP gives correspondence utilizing 3-handshake which sends RTS and ACK originate from server
end and information message has been transmitted over the data transmission gave. This does not give security
over flooding assault happened on the system. TCP gives correspondence between distinctive hubs of the wired
correspondence however when multi-spilling happens in a system TCP does not gives legitimate throughput of
the framework which is significant issue that happened in the past framework. In the proposed work, to beat this
issue SCTP and Improved SCTP transmission control convention has been executed for the framework
execution of the framework. SCTP gives 4-handshake correspondence in the message transmit and improved
SCTP gives the performance when the queue length comes to its full value then it divides the message to other
nodes because of which security element get expansions and this likewise gives correspondence administrations
over multi-spilling and multi-homing. Numerous sender and recipients can impart over wired system utilizing
different methodologies of correspondence through same routers, which debases in the TCP convention. In last
we assess parameters for execution assessment. Here, we composed and actualized our proving ground utilizing
Network Simulator (NS-2.35) to test the execution of both Routing conventions.
A THROUGHPUT ANALYSIS OF TCP IN ADHOC NETWORKScsandit
This document analyzes the throughput of TCP in mobile ad hoc networks through simulations. It finds that TCP throughput decreases initially as the number of hops increases, then stabilizes at higher hop counts. This is due to hidden terminal problems at low hops. The number of retransmissions increases with payloads and flows due to buffering and congestion. TCP performance degrades in wireless networks because it cannot differentiate between congestion and non-congestion packet losses. Mobility, interference, and dynamic topology changes specific to wireless networks cause unnecessary triggering of TCP congestion control mechanisms.
A throughput analysis of tcp in adhoc networkscsandit
Transmission Control Protocol (TCP) is a connection oriented end-end reliable byte stream
transport layer protocol. It is widely used in the Internet.TCP is fine tuned to perform well in
wired networks. However the performance degrades in mobile ad hoc networks. This is due to
the characteristics specific to wireless networks, such as signal fading, mobility, unavailability
of routes. This leads to loss of packets which may arise either from congestion or due to other
non-congestion events. However TCP assumes every loss as loss due to congestion and invokes
the congestion control procedures. TCP reduces congestion window in response, causing unnecessary
degradation in throughput. In mobile ad hoc networks multi-hop path forwarding further
worsens the packet loss and throughput. To understand the TCP behavior and improve the
TCP performance over mobile ad hoc networks considerable research has been carried out. As
the research is still active in this area a comprehensive and in-depth study on the TCP throughput
and the various parameters that degrade the performance of TCP have been analyzed. The
analysis is done using simulations in Qualnet 5.0
Ctcp a cross layer information based tcp for manetijasuc
Traditional TCP cannot detect link contention losses and route failure losses which occur in MANET and
considers every packet loss as congestion. This results in severe degradation of TCP performance. In this
research work, we modified the operations of TCP to adapt to network states. The cross-layer notifications
are used for adapting the congestion window and achieving better performance. We propose Cross-layer
information based Transmission Control Protocol (CTCP) which consists of four network states.
Decelerate state to recover from contention losses, Cautionary state to deal with route failures, Congested
state to handle network congestion and Normal state to be compatible with traditional TCP. Decelerate
state makes TCP slow down if the packet loss is believed to be due to contention rather than congestion.
Cautionary state suspends the TCP variables and after route reestablishment resumes with conservative
values. Congestion state calls congestion control when network is actually congested and normal state
works as standard TCP. Simulation results show that network state based CTCP is more appropriate for
MANET than packet loss based traditional TCP.
Efficient and Fair Bandwidth Allocation AQM Scheme for Wireless NetworksCSCJournals
Heterogeneous Wireless Networks are considered nowadays as one of the potential areas in research and development. The traffic management’s schemes that have been used at the fusion points between the different wireless networks are classical and conventional. This paper is focused on developing a novel scheme to overcome the problem of traffic congestion in the fusion point router interconnected the heterogeneous wireless networks. The paper proposed an EF-AQM algorithm which provides an efficient and fair allocation of bandwidth among different established flows. Finally, the proposed scheme developed, tested and validated through a set of experiments to demonstrate the relative merits and capabilities of a proposed scheme
PERFORMANCE ANALYSIS OF RESOURCE SCHEDULING IN LTE FEMTOCELLS NETWORKScscpconf
3GPP has introduced LTE Femtocells to manipulate the traffic for indoor users and to minimize the charge on the Macro cells. A key mechanism in the LTE traffic handling is the packet
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VEGAS: Better Performance than other TCP Congestion Control Algorithms on MANETsCSCJournals
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EFFICIENT ADAPTATION OF FUZZY CONTROLLER FOR SMOOTH SENDING RATE TO AVOID CONGESTION IN MULTICAST NETWORKS
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
DOI: 10.5121/ijcsit.2018.10606 53
EFFICIENT ADAPTATION OF FUZZY
CONTROLLER FOR SMOOTH SENDING RATE
TO AVOID CONGESTION IN MULTICAST
NETWORKS
Deepa V B 1
and Ushadevi M B2
1
Department of Computer Engineering, Information Science and Engineering, JawaharaLal Nehru
National College of Engineering.Shimogga, Karnataka, India
2
Department of Telecommunication and Engineering, JawaharaLal Nehru National College of
Engineering.Shimogga, Karnataka, India
ABSTRACT
This paper prefers a fuzzy-logic-based sending rate adaption scheme named FSR(Fuzzy Sending Rate)
intending to improve the evenness of TCPFriendly Multicast Congestion Control (TFMCC). To mitigate
fluctuation of sending rate for TFMCC sender, FSR intends, five actions and link utilization for tuning
sending rate and uses a fuzzy controller to determine which operation should be reaped according to the
feedback information from CLR (current limiting receiver). Asymmetrical membership functions and biased
fuzzy inference rules make FSR as friendly to TCP flows as TFMCC. Simulation results show that FSR has
exceptional smoothness and fine TCP Friendliness.
KEYWORDS
Tfmcc, Rtt, Link Utilization, Sending Rate, Fuzzy Controller
1. INTRODUCTION
Multicast Congestion Control (MCC) is one of the critical methods to weave congestion and
make network perform steadily. It is needed that MCC mechanism must not only assurance the
QoS (Quality of Service) of users, but also ensure that multicast flows could share the resource
adequately with existing flows specifically TCP flows, which is called TCP-Friendliness[1].
MCC functioning can be divided into two division with relating to the manner of sending rate[1]:
single-rate mechanism versus multi-rate mechanism. Although single-rate MCC is poor in
performance and expandability, it is easy to achieve, has fine friendliness and suits to the
conditions that is not so heterogonous. Some newly proposed multi-rate MCC which are also
called hybrid MCC[2-4] make single-rate MCC as building block that each layer applies single-
rate MCC.
One of the utilizations that IP multicast transmits most is multimedia applications (video or voice)
which have smooth transmitting rate. Repeated changes in the transmission rate may disintegrate
the quality of multimedia and import more difficulty and complicatedness to encoder/decoder.
How to sustain smooth transmitting rate is a technology obstacle associated with all MCC
mechanisms. In this paper, we prefer a sending rate adaption scheme based on Fuzzy-logic
intended to smoothen the sending rate of TFMCC 5].The rest of this paper is categorized as
follows. Section 2 summarizes related work. Section 3 interprets the excessive decrease
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No
phenomenon that make sending rate of TFMCC fluctuate and presents
adaption scheme. Section 4 elaborates the link Utilization of the network to decrease congestion
Section 5 introduces Fuzzy Controller for multicast congestion control having smooth sending
Rate. Section 6 gives results and disc
2. RELATED WORK
There are different mechanisms have been adopted till now to control the congestion in the
network. We know that TFMCC [2] is a steady state equation based multi
calculate the throughput of the network.
the Congestion representative and therefore it is slow in reacting to changes
condition. Secondly, the CLR drag down the whole TFMCC session. Ther
modifications are made to TFMCC us
8]. Once congestion is detected, it is notified by using Im
signaling. After receiving this signal the int
congestion doesn’t occur. Further conges
2.1 Proposed Work
We have seen that many algorithms have been proposed to control the congestion in the multicast
network. These algorithms used different protocols to reduce congestion by adjusting the sending
rate of the sender and different mechanisms has been proposed to indicate the congestion
representative. The heterogeneous behavior of the network leads to the more uti
bandwidth which results in congestion in the network. We propose an algorithm to improve the
utilization by keeping the same sending rate while congestion occurs in the network. And also we
adapt efficient fuzzy controller for sending smooth ra
and Link Utilization as input and Sending Rate as output for Fuzzy Controller.
3. ADAPTION SCHEME FOR
TFMCC has high quality performance including good TCPFriendliness and
suppression mechanisms[3].In that idea TFMCC is extensively accepted and preferred as building
block in some multi-rate MCC[2].
expected sending rate by a control equation derived fro
throughput[6]:
where RTT is Round-Trip time,
regulates sending rate by the predicted throughput of CLR(current limiting receiver), which is the
receiver who has the minimum expected throughput of the group. Once the expected throughput
of CLR T(k) is lesser than the current sending rate
the expected rate. Actually, this operation may decrease the sending rate enormously and
indirectly make throughput change, which is adverse to undergo for multimedia applications.
the intensification following, p(v
the sending rate with v ;T( p,
parameters p and RTT .We consider two consecutive steady st
network is in state1 and at time t
more dangerous. In state1 TFMCC has sending rate of
correspondingly state2 with S2
variables fulfill (1). Let p’ (v1) denote the loss rate es
during when the congestion has been more serious but
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, Decem
phenomenon that make sending rate of TFMCC fluctuate and presents Fuzzy-logic based
adaption scheme. Section 4 elaborates the link Utilization of the network to decrease congestion
Section 5 introduces Fuzzy Controller for multicast congestion control having smooth sending
Rate. Section 6 gives results and discussion as well as section 7 conclusion.
There are different mechanisms have been adopted till now to control the congestion in the
network. We know that TFMCC [2] is a steady state equation based multi-cast technique to
e the throughput of the network. But it has some problems. First, it is slow in identify
the Congestion representative and therefore it is slow in reacting to changes in the congestion
condly, the CLR drag down the whole TFMCC session. Ther
ns are made to TFMCC using Additive Increase Multiplicative Decrease (AIMD)
ted, it is notified by using Implicit Congestion Notific
ceiving this signal the intermediate nodes adjust its sending rate so that
n doesn’t occur. Further congestion is implemented using Fuzzy logic controller [9,10
We have seen that many algorithms have been proposed to control the congestion in the multicast
These algorithms used different protocols to reduce congestion by adjusting the sending
rate of the sender and different mechanisms has been proposed to indicate the congestion
representative. The heterogeneous behavior of the network leads to the more uti
bandwidth which results in congestion in the network. We propose an algorithm to improve the
utilization by keeping the same sending rate while congestion occurs in the network. And also we
adapt efficient fuzzy controller for sending smooth rate to avoid congestion using parameter RTT
and Link Utilization as input and Sending Rate as output for Fuzzy Controller.
OR SENDING RATE USING FUZZY (FSR)
TFMCC has high quality performance including good TCPFriendliness and efficient feedback
suppression mechanisms[3].In that idea TFMCC is extensively accepted and preferred as building
rate MCC[2]. To be TCP-Friendly, each TFMCC receiver estimates its
expected sending rate by a control equation derived from a model of TCP’s long
Trip time, s is packet size and p is packet loss event rate. TFMCC sender
regulates sending rate by the predicted throughput of CLR(current limiting receiver), which is the
receiver who has the minimum expected throughput of the group. Once the expected throughput
n the current sending rate S(k) , sender will adopt the new sending rate to
the expected rate. Actually, this operation may decrease the sending rate enormously and
indirectly make throughput change, which is adverse to undergo for multimedia applications.
v) and RTT(v) are the loss event rate and RTT correspondingly, at
, RTT) is the predictable throughput estimated by (1) with the
.We consider two consecutive steady states: state1 and state2.Basically, the
network is in state1 and at time t1 the backdrop traffic increases, which means that congestion is
e1 TFMCC has sending rate of S1 = v1 , loss rate of p1 and RTT of
2 = v2 < v , p2 and RTT2 . During each steady state, the three
denote the loss rate estimated by the CLR after t1 but before
during when the congestion has been more serious but the feedback has not received by the
6, December 2018
54
logic based rate
adaption scheme. Section 4 elaborates the link Utilization of the network to decrease congestion,
Section 5 introduces Fuzzy Controller for multicast congestion control having smooth sending
There are different mechanisms have been adopted till now to control the congestion in the
cast technique to
. First, it is slow in identifying
in the congestion
condly, the CLR drag down the whole TFMCC session. Therefore, some
ing Additive Increase Multiplicative Decrease (AIMD)[7-
plicit Congestion Notification (ICN)
ing rate so that
sing Fuzzy logic controller [9,10].
We have seen that many algorithms have been proposed to control the congestion in the multicast
These algorithms used different protocols to reduce congestion by adjusting the sending
rate of the sender and different mechanisms has been proposed to indicate the congestion
representative. The heterogeneous behavior of the network leads to the more utilization of
bandwidth which results in congestion in the network. We propose an algorithm to improve the
utilization by keeping the same sending rate while congestion occurs in the network. And also we
te to avoid congestion using parameter RTT
efficient feedback
suppression mechanisms[3].In that idea TFMCC is extensively accepted and preferred as building
Friendly, each TFMCC receiver estimates its
m a model of TCP’s long-term
(1)
is packet loss event rate. TFMCC sender
regulates sending rate by the predicted throughput of CLR(current limiting receiver), which is the
receiver who has the minimum expected throughput of the group. Once the expected throughput
) , sender will adopt the new sending rate to
the expected rate. Actually, this operation may decrease the sending rate enormously and
indirectly make throughput change, which is adverse to undergo for multimedia applications. In
) are the loss event rate and RTT correspondingly, at
) is the predictable throughput estimated by (1) with the
ates: state1 and state2.Basically, the
the backdrop traffic increases, which means that congestion is
and RTT of RTT1 ;
. During each steady state, the three
but before t2 ,
the feedback has not received by the
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No
sender, so the sending rate is still
change after t1. It is obvious that the higher sending rate is, the larger loss rate and RTT will be if
the background traffic is steady. So we get
P’ > (V1) P(V
RTT’(V1) > RTT’(V
At least one of the two inequations above is absolute because of
predicted throughput is,
T(p’(v1), RTT’(v
Once the feedback with as T(p’(v
sender decreases the sending rate directly to T’
Afterward, the CLR will estimate new los
p(S’
RTT(S’
Then the new calculated expected throughput will be higher than
arrive at the sender, the sender will increase the sending rate additively. Finally with several step
of adjusting, the sending rate will be close to
Figure 3.1 Excessive decrease phenomena in TFMCC and Multiplicative Decrease action in FSR.
Now, we can analyze that TFMCC will decrease the sending rate excessively and it will take a
long time to converge to the new state as depicted in Fig
by simulations in section 5.
3.1 Rate Adjusting Actions
n order to alleviate the excessive decrease phenomenon in TFMCC, we introduce five rate
adjusting actions into FSR for different congestion degree.
• Additive Increase (AI): The additive increase component should be such that at no instant of
time should the sending rate undergo an increment greater than about 10% the current size. This
serves to distinguish an additive increase form a multiplicative increase. To ensure
Friendliness, AI action is taken when the expected rate is a little higher than sending rate.
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, Decem
sender, so the sending rate is still v1.. We also further assume that the background traffic doesn’t
. It is obvious that the higher sending rate is, the larger loss rate and RTT will be if
nd traffic is steady. So we get
) P(V2)
) > RTT’(V2)
t least one of the two inequations above is absolute because of v1 > v2 . Then the calculated
), RTT’(v1)) < T(p(v2), RTT’(v1))=v2
T(p’(v1), RTT’(v1)) referred to as T’1 for short, reaches the sender, the
sender decreases the sending rate directly to T’1,
S’=T’< v2
Afterward, the CLR will estimate new loss rate and RTT at the sending rate of S’2<
p(S’2)≤p(v2) ≤ p’(v1)
RTT(S’2) ≤ p’(v1)≤ RTT’(v1)
Then the new calculated expected throughput will be higher than S’′. When the new feedback
arrive at the sender, the sender will increase the sending rate additively. Finally with several step
of adjusting, the sending rate will be close to v2 .
Figure 3.1 Excessive decrease phenomena in TFMCC and Multiplicative Decrease action in FSR.
Now, we can analyze that TFMCC will decrease the sending rate excessively and it will take a
long time to converge to the new state as depicted in Figure(3.1). This conclusion will be proved
n order to alleviate the excessive decrease phenomenon in TFMCC, we introduce five rate
adjusting actions into FSR for different congestion degree.
The additive increase component should be such that at no instant of
time should the sending rate undergo an increment greater than about 10% the current size. This
serves to distinguish an additive increase form a multiplicative increase. To ensure
Friendliness, AI action is taken when the expected rate is a little higher than sending rate.
6, December 2018
55
d traffic doesn’t
. It is obvious that the higher sending rate is, the larger loss rate and RTT will be if
(2)
. Then the calculated
(3)
for short, reaches the sender, the
(4)
2< v2:
(5)
When the new feedback
arrive at the sender, the sender will increase the sending rate additively. Finally with several step
Figure 3.1 Excessive decrease phenomena in TFMCC and Multiplicative Decrease action in FSR.
Now, we can analyze that TFMCC will decrease the sending rate excessively and it will take a
s conclusion will be proved
n order to alleviate the excessive decrease phenomenon in TFMCC, we introduce five rate
The additive increase component should be such that at no instant of
time should the sending rate undergo an increment greater than about 10% the current size. This
serves to distinguish an additive increase form a multiplicative increase. To ensure TCP-
Friendliness, AI action is taken when the expected rate is a little higher than sending rate.
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
56
• Additive Decrease (AD): The additive decrease component should be such that the decrement
in the sending rate should never be more 10%. This serves to differentiate it from a multiplicative
decrease. Actual, if the expected sending rate is a little lower than the current sending rate, which
is meaning congestion is not very serious, there is no need to decrease sending sharply, especially
for multimedia applications. In such case, in order to smoothen sending rate we introduce
“additive decrease (AD)” action that the sending rate will decrease by one packet per RTT.
• Multiplicative Increase (MI): The multiplicative increase component should be large enough
so that the increment in size of the sending rate is larger than 10% always. To fully utilize
resource and improve response speed, we introduce “MI” into FSR: if the expected T’1 is much
larger than the sending rate, the new sending rate will increase to S1 +T’1/2.
• Multiplicative Decrease (MD): The multiplicative decrease component should be so chosen
that the sending rate decrement is never less than 10%. From (3) we can see that the actual rate
the receiver can accept is between the two values: T’1< v2 < S1 .In FSR, the sender decreases the
sending rate to S1 + T’1/2 instead of T’1 directly, which is called “MD” action:
3.2 Link Utilization
We are proposing an algorithm to improve the utilization by keeping the same sending rate while
congestion occurs in the network. For this we have to first calculate the link utilization using old
link utilization method. The proposed algorithm entitles LUMCC is given below[11]:
Algorithm:
Link Utilization Based Multicast Congestion Control (LUMCC)
1) Initialize the total link capacity.
2) Initialize the initial sending rate.
3) Initialize the queue size.
4) Initialize the packet size.
5) Set the session time.
6) Calculate the packet loss ratio on the link.
Pls=Pd/Pd+Ps
Where Pls is the Packet loss observed on the link, Pd is the number of Packets dropped, Ps is
the number of Packets sent on the link.
7) Calculate the link utilization, αij.
Where ∑ bw f* max(Xif)f
denotes the value of total traffic demand for all flows fЄF that
are transmitted through link (i, j), Cij is the link capacity.
8) Setting the threshold values:
a) If (99% of link utilization < αij)
Then, Congestion is very high and we adjust
Fnew= αij/β*
2
b) If (99% ≤ αij < 90%) Then, Congestion is high and we adjust
Fnew= αij
*
/ 2β
/2
If (Fnew > 99%) Then go to step “a”.
c) If (90% ≤ αij ≤ 50%) Then,
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
57
Fnew= αij
*
β*
2
Congestion is medium and we adjust
If (Fnew > 90%) Then go to step “b”.
d) If (0 < αij < 49%) Then, Congestion is low and we adjust
Fnew= αij
*
log2α
Else increment Fnew till its value reaches to medium value.
We see the example of propose algorithm given as below:
Example: Suppose the Link Capacity is 100 Mbps, Initial Sending Rate is 80 Mbps, Packet size
is 300, RTT is 150 ms, decreasing factor, β is 0.65 (0 < β < 1), increasing factor α = S/RTT is 2
and we vary the Queue size.
Case 1: Queue size = 50 packets
Link utilization,
α = ∑ bw f* max(Xif)f
/Cij fЄF
=80*50/100
=40%
the link utilization is 40% means that congestion is low. Then we use third condition and the
proposed formula is:
Fnew= αij
*
log2α
=40*log2(S/RTT)=40*log2(2)=40%
Again, the Fnew is 40%, then we go to step “c”.
So, our utilization comes to 65%.
Case 2: Queue size = 80 packets
Link utilization
α = ∑ bw f* max(Xif)f
/Cij for all fЄF
=80*80/100=64%
The link utilization is 64% means that congestion is medium. Then we use second condition and
the proposed formula is:
Fnew= αij
*
β*
2
=64*0.65*2
=83%
So, our utilization comes to 83%.
Case 3: Queue size = 120 packets
Link utilization,
α = ∑ bw f* max(Xif)f
/Cij for all fЄF
=80*120/100=96%
As utilization is 96% which shows the high congestion in the network is according to set
thresholds. So, we have made the congestion medium. For we use first condition and the proposed
formula is:
Fnew= αij */ 2β
/2
96*20.65
*2=5%
We conclude that if our link utilization is high then we need more care about the congestion,
otherwise regularly needs to increase the flow speed according to low and medium include with
6. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No
medium and high factor of speed respectively. Therefore, utilization of link is very important
phenomenon for control the congestion.
4. FUZZY INFERENCE ENGINE
Fuzzy Inference System is the key unit of a fuzzy logic system having decision making as its
primary work as shown in figure (4.1)
“OR” or “AND” for drawing essential decision rules
Following are some characteristics of FIS
• The output from FIS is always a fuzzy set irrespective of its input which can be fuzzy or
crisp.
• It is necessary to have fuzzy output when it is used as a controller.
• A defuzzification unit would be there with FIS to conv
variables.
The following five functional blocks describe the construction of FIS
• Rule Base − It contains fuzzy IF
• Database − It defines the membership functions of fuzzy sets used in fuzzy rules.
• Decision-making Unit − It performs operation on rules.
• Fuzzification Interface Unit
• Defuzzification Interface Unit
Following is a block diagram of fuzzy
4.1.1 Types of Controller
● Self-organising controller – a selfish creation noticing nothing outside itself and always
observing itself only and nothing else.
● Adaptive controller – a system that is just a current situation with neither memory nor
recollections about the past and reflections about the future.
● Learning controller – an industrious student constantly developing and expanding his or her
knowledge and experience.
4.2 Mamdani Fuzzy Logic C
The most commonly used fuzzy inference technique is the so called Mamdani method (Mamdani
& Assilian, 1975) which was proposed[12], by Mamdani and Assilian, as the very first attempt to
control a steam engine and boiler combination by synthesizing a set of linguistic control rules
obtained from experienced human operators. Their work was inspired by an
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, Decem
medium and high factor of speed respectively. Therefore, utilization of link is very important
phenomenon for control the congestion.
NGINE
Fuzzy Inference System is the key unit of a fuzzy logic system having decision making as its
as shown in figure (4.1). It uses the “IF…THEN” rules along with connectors
“OR” or “AND” for drawing essential decision rules[12].
re some characteristics of FIS
The output from FIS is always a fuzzy set irrespective of its input which can be fuzzy or
It is necessary to have fuzzy output when it is used as a controller.
A defuzzification unit would be there with FIS to convert fuzzy variables into crisp
blocks describe the construction of FIS
− It contains fuzzy IF-THEN rules.
− It defines the membership functions of fuzzy sets used in fuzzy rules.
− It performs operation on rules.
Fuzzification Interface Unit − It converts the crisp quantities into fuzzy quantities.
Defuzzification Interface Unit − It converts the fuzzy quantities into crisp quantities.
Following is a block diagram of fuzzy interference system.
Figure 4.1 Fuzzy Inference Engine
a selfish creation noticing nothing outside itself and always
observing itself only and nothing else.
a system that is just a current situation with neither memory nor
recollections about the past and reflections about the future.
an industrious student constantly developing and expanding his or her
Mamdani Fuzzy Logic Controller
The most commonly used fuzzy inference technique is the so called Mamdani method (Mamdani
& Assilian, 1975) which was proposed[12], by Mamdani and Assilian, as the very first attempt to
control a steam engine and boiler combination by synthesizing a set of linguistic control rules
obtained from experienced human operators. Their work was inspired by an equally influential
6, December 2018
58
medium and high factor of speed respectively. Therefore, utilization of link is very important
Fuzzy Inference System is the key unit of a fuzzy logic system having decision making as its
. It uses the “IF…THEN” rules along with connectors
The output from FIS is always a fuzzy set irrespective of its input which can be fuzzy or
ert fuzzy variables into crisp
− It defines the membership functions of fuzzy sets used in fuzzy rules.
− It converts the crisp quantities into fuzzy quantities.
− It converts the fuzzy quantities into crisp quantities.
a selfish creation noticing nothing outside itself and always
a system that is just a current situation with neither memory nor
an industrious student constantly developing and expanding his or her
The most commonly used fuzzy inference technique is the so called Mamdani method (Mamdani
& Assilian, 1975) which was proposed[12], by Mamdani and Assilian, as the very first attempt to
control a steam engine and boiler combination by synthesizing a set of linguistic control rules
equally influential
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
59
publication by Zadeh (Zadeh, 1973). Interest in fuzzy control has continued ever since, and the
literature on the subject has grown rapidly. A survey of the field with fairly extensive references
may be found in (Lee, 1990) or, more recently, in (Sala et al., 2005).In Mamdani’s model the
fuzzy implication is modeled by Mamdani’s minimum operator, the conjunction operator is min,
the t-norm from compositional rule is min and for the aggregation of the rules the max operator is
used. In order to explain the working with this model of FLC will be considered the example from
(Rakic, 2010)[13] where a simple two-input one-output problem that includes three rules is
examined:
Rule1 : IF x is A3 OR y is B1 THEN z is C1
Rule2 : IF x is A2 AND y is B2 THEN z is C2
Rule3 : IF x is A1 THEN z is C3.
Step 1: Fuzzification
The first step is to take the crisp inputs, x0 and y0, and determine the degree to which these inputs
belong to each of the appropriate fuzzy sets. According to Figure (4.2) one obtains
µA1 (x0) = 0.5, µA2 (x0) = 0.2, µB1 (y0) = 0.1, µB2 (y0) = 0.7
Step 2: Rules evaluation
The fuzzified inputs are applied to the antecedents of the fuzzy rules. If a given fuzzy rule has
multiple antecedents, the fuzzy operator (AND or OR) is used to obtain a single number that
represents the result of the antecedent evaluation. To evaluate the disjunction of the rule
antecedents, one uses the OR fuzzy operation. Typically, the classical fuzzy operation union is
used :
µA∪B(x) = max{µA(x), µB(x)}.
Similarly, in order to evaluate the conjunction of the rule antecedents, the AND fuzzy operation
intersection is applied:
µA∩B(x) = min{µA(x), µB(x)}.
The result is given in the Figure (4.3).
Now the result of the antecedent evaluation can be applied to the membership function of the
consequent. The most common method is to cut the consequent membership function at the level
of the antecedent truth; this method is called clipping. Because top of the membership function is
sliced, the clipped fuzzy set loses some information. However, clipping is preferred because it
involves less complex and generates an aggregated output surface that is easier to defuzzify.
Another method, named scaling, offers a better approach for preserving the original shape of the
fuzzy set: the original membership function of the rule consequent is adjusted by multiplying all
its membership degrees by the truth value of the rule antecedent. Figure (4.4).
Step 3: Aggregation of the rule outputs
The membership functions of all rule consequents previously clipped or scaled are combined into
a single fuzzy set as shown in Figure(4.5).
Step 4: Defuzzification
The most popular defuzzification method is the centroid technique. It finds a point representing
the center of gravity (COG) of the aggregated fuzzy set A, on the interval [a, b]. A reasonable
estimate can be obtained by calculating it over a sample of points. According to Figure(3.6), in
our case results,
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
60
COG =(0 + 10 + 20) × 0.1 + (30 + 40 + 50 + 60) × 0.2 + (70 + 80 + 90 + 100) × 0.5
0.1 + 0.1 + 0.1 + 0.2 + 0.2 + 0.2 + 0.2 + 0.5 + 0.5 + 0.5 + 0.5= 67.4
4.3 Universal approximators
Using the Stone-Weierstrass theorem, Wang in (Wang, 1992) showed that fuzzy logic control
systems of the form ,, Ri : IF x is Ai AND y is Bi THEN z is Ci , i = 1, ..., n With,
Figure(4.2) Fuzzification
Figure (4.3) Rules Evaluation
Figure(3.4) Clipping and Scaling
Figure(4.5) Aggregation of the Rules output
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
61
Figure(4.6) Defuzzification
4.2.1 Mamdani fuzzy logic controller
• Gaussian membership functions
where x0 is the position of the peak relative to the universe and σ is the standard deviation
• Singleton fuzzifier
fuzzifier(x) = x
• Fuzzy product conjunction
µAi (u) AND µBi (v) = µAi (u)µBi (v)
• Larsen (fuzzy product) implication
[µAi (u) AND µBi (v)] → µCi (w) = µAi (u)µBi (v)µCi (w)
• Centroid deffuzification method
where ci is the center of Ci , are universal approximators, i.e. they can approximate any
continuous function on a compact set to an arbitrary accuracy.
4.4 Fuzzy Based Congestion Estimation
Fuzzification: The mapping from a real-valued point to a fuzzy set is known as Fuzzification
which receives other robots information in order to convert it into fuzzy linguistic variable inputs.
The fuzzy logic is chosen based upon the following two reasons: a) In between the normal and
abnormal events, clear boundaries are not present, b) Fuzzy rules should level the normality and
abnormality separation. The fuzzy set can be represented using the mathematical formation
known as membership function.
Rule Definition: Conditional statements are used to implement a membership function which
characterizes a fuzzy set A in x. When the fuzzy statement in an antecedent is true to some degree
of membership, the consequent of the same degree also proves to be true.
Rule structure: If antecedent then consequent. The rule, When both the variables have different
values high and low, then we can get a generous output otherwise a malicious output is detected.
For a fuzzy classification system, the case or an object can be classified by applying the set of
fuzzy rules which depend upon the linguistic values of its attributes. The rule is functioned at the
number given by the antecedent which has a value between 0 and 1. The input can be fuzzified by
evaluating the antecedent and then essential fuzzy operators can be applied. The consequent
obtains this result as the inference.
10. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
62
We will now describe our methodology for fuzzy logic approach to control congestion in the
network. In controlling congestion, the three most important variables are the RTT, Link
utilization, Sending Rate. With fuzzy logic, we assign grade values to our three variables. Our
fuzzy set therefore consists of three fuzzy variables.
Fuzzy set = {R,L,S } (R –RTT, L-LinkUtilization, S- SendingRate)
Fuzzy logic implements human experiences and preferences via membership functions and fuzzy
rules. In this work, the fuzzy if-then rules consider the parameters: R –RTT, L-Link Utilization,
S-Sending Rate.
The fuzzy logic uses two input variables and one output variable. The two input variables to be
fuzzified are RTT and Link Utilization. The inputs are fuzzified, implicated, aggregated and
defuzzified to get the output as Sending Rate. The linguistic variables associated with the input
variables are Low (L), Medium(M) and high (H). The output variables use three linguistic
variables H, M, and L where H denotes high Sending Rate, M denotes Medium Sending Rate and
L denotes Low Sending Rate. The rules for the FIS are shown below as shown in Table1. They
utilize the AND method which is based on the min function. The FIS rules of the Fuzzy Inference
System are:
Table 1 Fuzzy Rules (LU-Link Utilization, SR-Sending Rate) (L-Low, M-Medium, H-High)Fuzzy Rules
If RTT is Less and Lu is less then SR is High.
If RTT is Less and Lu is Medium then SR is Medium.
If RTT is Less and Lu is High then SR is Low.
If RTT is Medium and Lu is less then SR is Medium.
If RTT is Medium and Lu is Medium then SR is Medium.
If RTT is Medium and Lu is High then SR is low.
If RTT is High and Lu is less then SR is Medium.
If RTT is High and Lu is Medium then SR is Low.
If RTT is High and Lu is High then SR is Very Low.
Control Action to be taken after defuzzification as shown in table 2,and description is given in
section 3.1 Rate Adjusting Action. (MIAD-Multiplicative Increase and Additive Decrease,
AIAD-Additive Increase and Additive Decrease, AIMD-Additive Increase and Multiplicative
Decrease).
Table 2 (Control Action for smooth Sending Rate)
Sending
Rate
Link Utilization
L M H
RTT L MIAD AIAD AIMD
M AIAD AIMD AIMD
H AIAD MD MD
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63
5. SIMULATION RESULTS AND DISCUSSION
Simulations were carried out using Network Simulation (ns- 2.35). We patch new agent TFMCC
algorithm in transport layer ns-allinone-2.35. Varying links and packet size and bandwidth
calculated throughput with comparing TFMCC Sending Rate and FSR (Fuzzy Sending Rate).
Topology is as shown in figure(5.1). The network topology for single multicast. is below of 15
receivers: Simulation parameters are as shown in Table 3.
Table 3 Simulation Parameters
Parameters Value
Link Bandwidth 15-100Mbps
Link Delay 25ms
Queue Size 60-99 packets
Sending Rate(initial) 85Mbps
No.of Groups 5
No.0f Receivers 15
RTT 150ms
Packet Size 300
Session Time 500ms
Congestion Status High, Medium, Low
Figure (5.1) Multicast Topology
5.1 Results and Comparisons
5.1.1 Packet Delivery Ratio: Number of packets sent to the recovers is more than the TFMCC
comparing FSR(Fuzzy sending Rate as shown in below. The sending rate of FSR sender which
has less monitor and notch is smoother than that of TFMCC sender. In such dynamic network,
the tight track to expected rate calculated by CLR will make multicast flow unstable. On the
contrary, FSR with fuzzy controller can adjust sending rate adaptively with the knowledge of
feedback information, so FSR has smoother sending rate than TFMCC as shown in Figure (5.2)
and (4.3).
12. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
64
Figure (5.2) Packet Delivery Ratio
Figure (5.3) Throughtput TFMCC vs TFMCC-FSR
5.1.2 Packet loss Ratio:The variation is due to the link utilization of the network. The new
proposed sending rate (FSR) shows the better result than the existing TFMCC link utilization
strategy because of the less packet loss ratio. This is because of efficient link utilization using
Fuzzy logic Controller as shown in figure (5.4).
Figure (5.4) Packet loss Ratio
5.1.3 Throuhput Comparing TFMCC vs TFMCC-FSR
Figure(5.5) shows the variation of throughput with the time. It shows the maximum time needed
for the multi-cast source till reaching a steady state throughput. It is clear that proposed approach
Outperforms existing approach.
0
200
400
600
800
10 20 30 40 50
WithoutFSR
WithFSR
Number of PacketSent
Links
P
a
c
0
10
20
30
40
50
60
10 20 30 40 50
WithoutFSR
WithFSR
PacketDropped
D
r
o
p
Links
13. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
65
Figure (5.5) Throughput TFMCC vs TFMCC-FSR
5.1.4 Fuzzy Controller operations
a) RTT is low and LinkUtilization is Low then sending rate High
b) RTT is low and LinkUtilization is High then Sending Rate is Medium
0
0.5
1
1.5
2
2.5
10 20 30 40 50
TFMCC
Links
T
h
r
o
u
g
h
t
p
u
t
TFMCC vs TFMCC-FSR
14. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
66
c) RTT is high and LinkUtilization is Low Then Sending rate is Medium
d) RTT is High and LinkUtilization is High Sending Rate is low
Fuuzy Graph
Figure(5.6) Fuzzy Sending Rate
Control surface of the Fuzzy Congestion Controller is shown in Figure (5.6). The control surface
is shaped by the rule base and the linguistic values of the linguistic variables. By observing the
progress of simulation, and modifying the rules and definitions of the linguistic values, FSR can
be tuned to achieve better Link utilization, and smoothened Sending Rate.
6. CONCLUSION
For the requirement of multimedia application based on IP multicast, we have proposed an
improved rate adaption scheme named FSR to smoothen the sending rate of TFMCC sender.
FSR introduces four actions to adjust sending rate and uses a fuzzy controller for making decision
to choose one of the four actions adaptively. In dynamic network environment, fuzzy controller
uses the difference between expected rate and sending rate to reflect the congestion degree, as
well as the difference between two latest consecutive expected rates to predict the trend of
network. Under the fuzzy controller, MIAD,AIAD,AIMD actions eliminate the “sawtooth”
phenomenon in TFMCC, which is crucial for smoothing sending rate. When the available
15. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, December 2018
67
bandwidth is turning abundant, an algorithm for congestion control which based on utilization of
link and taking decision according to high, medium and low congestion.
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[7] W. Kammoun and H. Youssef, “Improving the Perfor-mance of End-to-End Single Rate Multicast
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[11] Manisha Manjula,Rajesh Mishra,Joysna ,”Link Utilization Based Multicast Congestion
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[12] “Fuzzy Controller”,Leonid Reznic Book, ISBN 0 7506 3429 4An imprint of Butterworth-Heinemann
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[13] Rakic, A. (2010). Fuzzy Logic. Introduction 3. Fuzzy Inference, ETF Beograd. URL:
http://www.docstoc.com/docs/52570644/Fuzzy-logic-3
16. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No
Authors
Mrs Deepa V B working as Assistant Professor in Department of Information Science
and Engineering at JawaharaLal Nehru National College of Engineering Shimoga
Karnataka. Perusing Ph.D in Computer Network in the area of Multicast Congestion
Control.
Dr Ushadevi M B working as Prof and Head of Dept of Telecommunication
Engineering at JawaharLal Nehru National College of Engineering Shimlogga
Karnataka. She completed Ph.D Degree in 2010 at Kuvempu University Shimogga.
Her research area is Switching Networks, interes
Networks, Mobile Networks. She published many conference ,journal papers.
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 6, Decem
working as Assistant Professor in Department of Information Science
and Engineering at JawaharaLal Nehru National College of Engineering Shimoga
Karnataka. Perusing Ph.D in Computer Network in the area of Multicast Congestion
working as Prof and Head of Dept of Telecommunication
Engineering at JawaharLal Nehru National College of Engineering Shimlogga
Karnataka. She completed Ph.D Degree in 2010 at Kuvempu University Shimogga.
Her research area is Switching Networks, interested areas are Wireless Sensor
Networks, Mobile Networks. She published many conference ,journal papers.
6, December 2018
68