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Balancing the Tradeoffs between Query Delay And Data Availability in MANETs

Platform : IEEE PARALLEL AND DISTRIBUTED SYSTEMS

IEEE Projects Years : 2012

Balancing the Tradeoffs between Query Delay And Data Availability in MANETs

Abstract:

                  In mobile ad hoc networks (MANETs), nodes move freely and link/node failures are common, which leads to frequent network partitions. When a network partition occurs, mobile nodes in one partition are not able to access data hosted by nodes in other partitions, and hence significantly degrade the performance of data access. To deal with this problem, we apply data replication techniques. Existing data replication solutions in either wired or wireless networks aim at either reducing the query delay or improving the data availability, but not both. As both metrics are important for mobile nodes, we propose schemes to balance the tradeoffs between data availability and query delay under different system settings and requirements. Extensive simulation results show that the proposed schemes can achieve a balance between these two metrics and provide satisfying system performance.

 

Architecture:

 

 

 

 

 

 

 

 

Algorithm:

Super-optimal algorithm

                                         A super optimal solution for 𝑁𝑖 would be allocating 𝐶 most frequently access data items in 𝑁𝑖, but allocating the other data items in a way that they are all accessible from 𝑁𝑖’s neighbors (this may not be possible in practice). Its data availability, denoted as 𝐴𝑠𝑢𝑝𝑒𝑟. The solution given by the super-optimal algorithm is not a tight upper bound. It may be better than optimal and it may not be feasible. However, it is too difficult to find the tight upper bound and this super-optimal algorithm can be used for performance comparison.

 

Existing System:

                               Existing data replication solutions in either wired or wireless networks aim at either reducing the query delay or improving the data availability, but not both. However, most mobile nodes only have limited storage space, bandwidth and power, and hence it is impossible for one node to collect and hold all the data considering these constraints.

 

Disadvantages:

                          One drawback of the greedy scheme is that it does not consider the cooperation between the neighboring nodes and hence its performance may be limited.

 

Proposed System:

                            In this paper, we propose new data replication techniques to address query delay and data availability issues. As both metrics are important for mobile nodes, we propose techniques to balance the tradeoffs between data availability and query delay under different system settings and requirements. Simulation results show that the proposed schemes can achieve a balance between these two metrics and provide satisfying system performance.

 

 

 

 

 

Advantages:

  1. Low query delay.

 

  1. Data Availability is high.

 

 

 

      Modules:   

  1. Data Replication
  2.        The One-To-One Optimization (OTOO) Scheme   

 

  1. The Reliable Neighbor (RN) Scheme
  2. Reliable Grouping (RG) Scheme

 

 

1. Data Replication:

                               Data replication has been extensively studied in the Web environment and distributed database systems. However, most of them either do not consider the storage constraint or ignore the link failure issue. Before addressing these issues by proposing new data replication schemes, we first introduce our system model. In a MANET, mobile nodes collaboratively share data. Multiple nodes exist in the network and they send query requests to other nodes for some specified data items. Each node creates replicas of the data items and maintains the replicas in its memory (or disk) space. During data replication, there is no central server that determines the allocation of replicas, and mobile nodes determine the data allocation in a distributed manner.

 

  1. 2.     The One-To-One Optimization (OTOO) Scheme:   

1)      It considers the access frequency from a neighboring node to improve data availability.

 

2)  It considers the data size. If other criteria are the same, the data item with                    smaller size is given higher priority for replicating because this can improve the performance while reducing memory space.

 

3) It gives high priority to local data access, and hence the interested data should be replicated locally to improve data availability and reduce query delay.

 

 4) It considers the impact of data availability from the neighboring node and link quality. Thus, if the links between two neighboring nodes are stable, they can have more cooperation’s in data replication.

 

 

3. The Reliable Neighbor (RN) Scheme:

                                       OTOO considers neighboring nodes when making data replication choices. However, it still considers its own access frequency as the most important factor because the access frequency from a neighboring node is reduced by a factor of the link failure probability. To further increase the degree of cooperation, we propose the Reliable Neighbor (RN) scheme which contributes more memory to replicate data for neighboring nodes. In this scheme, part of the node’s memory is used to hold data for its Reliable Neighbors. If links are not stable, data on neighboring nodes have low availability and may incur high query delay. Thus, cooperation in this case cannot improve data availability and nodes should be more “selfish” in order to achieve better performance.

 

4. Reliable Grouping (RG) Scheme:

                   OTOO only considers one neighboring node when making data replication decisions. RN further considers all one-hop neighbors. However, the cooperation’s in both OTOO and RN are not fully exploited. To further increase the degree of cooperation, we propose the reliable grouping (RG) scheme which shares Replicas in large and reliable groups of nodes, whereas OTOO and RN only share replicas among neighboring nodes. The basic idea of the RG scheme is that it always picks the most suitable data items to replicate on the most suitable nodes in the group to maximize the data availability and minimize the data access delay within the group. The RG scheme can reduce the number of hops that the data need to be transferred to serve the query.

 

 

 

 

 

 

HARDWARE & SOFTWARE REQUIREMENTS:

 HARDWARE REQUIREMENTS: 

  • System                  :         Pentium IV 2.4 GHz.
  • Hard Disk              :         40 GB.
  • Floppy Drive         :         1.44 Mb.
  • Monitor                 :         15 VGA Color.
  • Mouse                   :         Logitech.
  • Ram                      :         512 MB.

  SOFTWARE REQUIREMENTS: 

  • Operating system   :         Windows XP Professional.
  • Coding Language   :         C#.NET

 

 


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