Good put-Aware Load Distribution For Real-Time Traffic Over Multipath Networks

 ABSTRACT:

Load distribution is a key research issue in deploying the limited network resources available to support traffic transmissions. Developing an effective solution is critical for enhancing traffic performance and network utilization. In this paper, we investigate the problem of load distribution for real-time traffic over multipath networks. Due to the path diversity and unreliability in heterogeneous overlay networks, large end-to-end delay and consecutive packet losses can significantly degrade the traffic flow’s good put, whereas existing studies mainly focus on the delay or throughput performance. To address the challenging problems, we propose a Good put- Aware Load distribution (GALTON) model that includes three phases: (1) path status estimation to accurately sense the quality of each transport link, (2) flow rate assignment to optimize the aggregate good put of input traffic, and (3) deadline-constrained packet interleaving to mitigate consecutive losses. We present a mathematical formulation for multipath load distribution and derive the solution based on utility theory. The performance of the proposed model is evaluated through semi-physical emulations in Exacta involving both real Internet traffic traces and H.264 video streaming. Experimental results show that GALTON outperforms existing traffic distribution models in terms of good put, video PSNR (Peak Signal-to-Noise Ratio), end-to-end delay, and aggregate loss rate.

AIM

The aim of this paper is to investigate the problem of load distribution for real-time traffic over multipath networks.

SCOPE

 GALTON outperforms existing traffic distribution models in terms of good put, video PSNR (Peak Signal-to-Noise Ratio), end-to-end delay, and aggregate loss rate.

EXISTING SYSTEM

The key research issue in utilizing the available paths between multi homed communication terminals is to effectively distribute input traffic load for providing adequate QoS perceived by end users Indeed, inefficient load distribution can significantly degrade the traffic performance and network utilization, e.g., load imbalance, packet reordering, large end-to-end delay, etc. Therefore, many algorithms  have been proposed to optimize the delay or throughput performance. However, these network-level criteria cannot properly indicate the benefits of upper-layer applications. For instance, a live streaming video application cannot effectively leverage the throughput gains since its streaming rate is typically fixed or bounded by the encoding schemes. Furthermore, the increased throughput may lead to larger end-to-end delays, which in turn induce video quality degradation. Consequently, the load distribution of real-time traffic to achieve excellent QoS still remains problematic

DISADVANTAGES:

1. Load distribution is a key research issue in deploying the limited network resources available to support traffic transmissions
2. Due to the path diversity and unreliability in heterogeneous overlay networks, large end-to-end delay and consecutive packet losses can significantly degrade the traffic flow’s good put, whereas existing studies mainly focus on the delay or throughput performance

PROPOSED SYSTEM

In this paper to be propose a Good put- Aware Load distribution (GALTON) model that includes three phases: (1) path status estimation to accurately sense the quality of each transport link, (2) flow rate assignment to optimize the aggregate good put of input traffic (3) deadline-constrained packet interleaving to mitigate consecutive losses. We present a mathematical formulation for multipath load distribution and derive the solution based on utility theory. The performance of the proposed model is evaluated through semi-physical emulations in Exata  involving both real Internet traffic traces and H.264 video streaming. Experimental results show that GALTON outperforms existing traffic distribution models in terms of good put, video PSNR (Peak Signal-to-Noise Ratio), end-to-end delay, and aggregate loss rate.

ADVANTAGES

1. To optimize the good put performance of real-time traffic over multipath networks
2. To optimize the delay or throughput performance.

 SYSTEM ARCHITECTURE:

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                Processor          -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board         -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      : Windows  7                                     

·                Front End                  : JSP AND SERVLET

·                Database                   : MYSQL

·                Tool                           :NETBEANS

REFERENCE:

Cheng, B. Shang, Y. Yuen, C. Wu, J “Good put-Aware Load Distribution for Real-time Traffic over Multipath Networks”, IEEE Transactions on Parallel and Distributed Systems, Volume 26 Issue 8, AUGUST  2014.

CAMF: Context-Aware Message Forwarding In Selfish Mobile Social Networks

ABSTRACT:

Nodes may exhibit selfish behaviors in mobile social networks (MSN) selfish nodes refuse to forward messages for all or some nodes in a network to conserve limited resources. Previous work mainly focuses on promoting selfish nodes transmit messages for others. In this paper, we consider selfishness from a different viewpoint; we regard selfishness as a basic requirement of systems and allow nodes to behave selfish behaviors. However, selfishness has a profound influence on routing performance. To achieve a good routing performance when node selfishness is considered, we first put forward a stateless approach which does not need collecting and storing state information to measure the similarity of nodes, and then evaluate the forwarding capability of nodes by combining the acquired similarity with node selfishness. We then quantify the receiving capability of nodes based on their available buffer size and energy. Incorporating forwarding and receiving capability, we present a forwarding set mechanism, which formulates the forwarding set optimization problem as a multiple knapsack problem to maximize the forwarding profit. Consequently, we take all the results above into our context-aware message forwarding design. Extensive trace-driven simulations show that our proposed algorithm achieves good routing performance with low transmission cost and resource consumption in selfish MSN.

AIM

The aims of this paper achieve a good routing performance when node selfishness is considered we first put forward a stateless approach which does not need collecting and storing state information to measure the similarity of nodes, and then evaluate the forwarding capability of nodes by combining the acquired similarity with node selfishness.

SCOPE

 The Scope of this project is Extensive trace-driven simulations show that our proposed algorithm achieves good routing performance with low transmission cost and resource consumption in selfish MSN.

EXISTING SYSTEM

In the real world, some or all individuals may exhibit various degrees of selfishness when forwarding messages, in particular when nodes are constrained with energy and storage space, e.g., a node may refuse to accept and store messages for others in order to conserve limited buffer and power resources. In general, node’s selfish behaviors are considered from two aspects: individual and social selfishness. For individual selfishness, a node exhibits the same forwarding willingness to any others. While, for social selfishness, nodes are more interested in receiving and forwarding messages for the nodes in the same community, but are less interested in receiving and forwarding messages for the nodes outside their communities.

 

DISADVANTAGES:

1. Selfish  nodes refuse to forward messages for all or some nodes in a network to conserve limited resources
2. Selfishness  has a profound influence on routing performance

PROPOSED SYSTEM

In this paper, to be consider node selfishness from a different perspective; we regard node selfishness as a basic requirement of applications or systems, and utilize the selfish characteristic of nodes to reduce resource consumption and extend the network lifetime. Nevertheless, it is difficult to achieve an acceptable routing performance when selfishness is considered. Because selfish nodes may refuse to accept or forward messages from others. Thus, our goal is to achieve high delivery performance with a low cost when nodes are allowed to exhibit selfish behaviors develop a context-aware message forwarding algorithm (CAMF), which exploits the context information to quantify the forwarding and receiving capability of nodes and to determine the forwarding set. 

ADVANTAGES

1. CAMF achieves good routing performance with low transmission cost when nodes are allowed to exhibit selfish behaviors.
2. To design incentive schemes to stimulate selfish nodes to forward messages for other nodes to achieve a good routing performance.

SYSTEM ARCHITECTURE:

 

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                Processor          -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board        -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      : Windows  7                                     

·                Front End                  : JSP AND SERVLET

·                Database                   : MYSQL

·                Tool                           :NETBEANS

 REFERENCE:

Dong, M.  Ota, K.  Xu, K., Wei, K. “CAMF: Context-aware Message Forwarding in Selfish Mobile Social Networks”, IEEE Transactions on Parallel and Distributed Systems, Volume 26 ,  Issue 8, AUGUST  2014.