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Saturn: Range Queries, Load Balancing And Fault Tolerance in DHT Data Systems

Platform : DOT NET

IEEE Projects Years : 2012 - 13

Saturn: Range Queries, Load Balancing  And Fault Tolerance in DHT Data Systems

 

Abstract:

 

                    In this paper, we present Saturn, overlay architecture for large-scale data networks maintained over Distributed Hash Tables (DHTs) that efficiently processes range queries and ensures access load balancing and fault-tolerance. Placing consecutive data values in neighboring peers is desirable in DHTs since it accelerates range query processing; however, such a placement is highly susceptible to load imbalances. At the same time, DHTs may be susceptible to node departures/failures and high data availability and fault tolerance are significant issues.

 

Saturn deal effectively with these problems through the introduction of a novel multiple ring, order-preserving architecture. The use of a novel order-preserving hash function ensures fast range query processing. Replication across and within data rings (termed vertical and horizontal replication) forms the foundation over which our mechanisms are developed, ensuring query load balancing and fault tolerance, respectively. Our detailed experimentation study shows strong gains in range query processing efficiency, access load balancing, and fault tolerance, with low replication overheads. The significance of Saturn is not only that it effectively tackles all three issues together—i.e., supporting range queries, ensuring load balancing, and providing fault tolerance over DHTs—but also that it can be applied on top of any order-preserving DHT enabling it to dynamically handle replication and, thus, to trade off replication costs for fair load distribution and fault tolerance.

 

 

 

Existing System:

 

Placing consecutive data values in neighboring peers is desirable in DHTs since it accelerates range query processing; however, such a placement is highly susceptible to load imbalances. At the same time, DHTs may be susceptible to node departures/failures and high data availability and fault tolerance are significant issues.

 

 

 

Proposed System:

 

Saturn deals effectively with these problems through the introduction of a novel multiple ring, order-preserving architecture. The use of a novel order-preserving hash function ensures fast range query processing. Replication across and within data rings (termed vertical and horizontal replication) forms the foundation over which our mechanisms are

 

Developed, ensuring query load balancing and fault tolerance, respectively.

 

The main idea behind Saturn is a novel hash function, which

 

1)      Handles multiple data replicas to preserve the data ordering of the underlying DHT,

 

2)       Randomly distributes accesses among peers to balance access load.

 

 

 

HARDWARE & SOFTWARE REQUIREMENTS:

 

 

 

HARDWARE REQUIREMENTS:

 

 

 

Processor:        Intel Pentium-IV

 

Speed:              1.1GHz

 

RAM:               512MB

 

Hard Disk:        40GB

 

General:              Key Board, Monitor, and Mouse

 

 

 

 

 

SOFTWARE REQUIREMENTS:

 

 

 

Operating System:      Windows XP, 7.

 

Software:                     VS .NET 2008,

 

Tool:                             SQL SERVER 2005.

 

MODULES:

 

 

 

MODULE1:

 

 

 

Saturn is not only that it effectively tackles all three issues together—i.e., supporting range queries, ensuring load balancing, and Providing fault tolerance over DHTs—but also that it can be applied on top of any order-preserving DHT enabling it to dynamically

 

Handle replication and, thus, to trade off replication costs for fair load distribution and fault tolerance.

 

The main idea behind Saturn is a novel hash function which

 

1)      Handles multiple data replicas to preserve the data ordering of the underlying DHT.

 

2)      Randomly distributes accesses among peers to balance access load. We should mention that instances of the algorithms run at each peer and no global schema knowledge is required.

 

3)      If a failure is detected, Saturn is able to calculate the range of values that was lost at ring 1 without any additional cost

 

 

 

MODULE2:

 

1.A RANGE QUERIES:

 

A novel multiring hash function used for data placement on top of the order-preserving DHT ring, both preserving the order of values and handling value replication in multiple data rings.

 

We assume that tuples are mapped to peers using consistent hashing .a tuple with identifier id is stored at the peer whose identifier is either equal to id or the closest larger one among all peers; this peer is called the successor of id and is denoted by succ(id). Similarly, the peer whose identifier is the closest smaller one to an id is called its predecessor and denoted by pred(id). Each peer maintains routing state in the form of a routing table, consisting of pointers to

 

1) its predecessor and successor,

 

2) O (log N) peers responsible for ids at exponentially increasing distances from its own id. Routing a message from one peer to another requires O(log N) messages in the worst Case, where N is the number of peers in the network. We assume that each tuple is stored on the peer mapped by securely hashing its primary key. Moreover, pointers to The latter are stored on x nodes whose identifiers are produced by hashing the values of each of the tuple’s x attributes using a set of respective order-preserving hash functions, hashi(). We should mention here that we could also store full instances of each tuple on each of these x nodes. At this point we make no distinction between these two cases.

 

MODULE3:

 

2.LOAD BALANCING:

 

 

 

 A load-driven replication scheme, which dynamically replicates popular data across, rings to ensure access load balancing. This scheme also supports tunable replication: by tweaking the maximum degree of replication, a system parameter, and per node

 

Load thresholds, it trades off replication costs for access load balancing.

 

 

 

MODULE4:

 

 

 

3.FAULT TOLERANCE:

 

 

 

A fault tolerance mechanism, which detects peer failures and retrieves, lost data. An additional replication scheme is proposed, the horizontal replications, which both guarantees data availability and efficiency of range query processing, even with

 

High-failure probabilities (fpr).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

       

 

 

 

 

 

 

 

Conclusion:

 

 

 

This paper presents the first attempt at concurrently attacking three key problems in structured P2P data networks: 1) efficient range query processing,

 

2) Data-access load balancing,

 

3) fault tolerance. The key observation is that replication-based load balancing techniques as well as frequent peer failures tends to obstruct techniques for efficiently processing range queries. Thus, solving these problems concurrently is an important goal and a formidable task  However, Saturn succeeded in combining the good properties of DHTs with range partitioning using a novel hash function, which is both order preserving and randomized Saturn reconciles and tradeoff message-count efficiency gains for improved data access load distribution fairness among the peers, on one hand, and replication costs for high data retrieval rate (i.e., recall) in case of peer failures, on the other. Compared to base architectures, our detailed experimentation clearly shows that Saturn achieves very good message-count efficiency coupled with a significant improvement in the overall access load distribution among peers with small replication overheads. Specifically, with the extra cost of a couple DHT lookups over the optimal performance of an order-preserving DHT, and a small replication degree, Saturn succeeds in balancing load even in highly skewed query distributions .In addition, experimentation results show that Saturn’s fault tolerance techniques provide a high recall with very good message count efficiency, for an extra, relatively low, replication degree. Specifically, even in a network with a high peer failure probability, Saturn is capable of retrieving almost all matching tuples with reasonable messaging and replication costs. Finally, Saturn can be superimposed over any underlying DHT infrastructure ensuring wide applicability and impact.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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