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Research Paper | Computer Science & Engineering | India | Volume 3 Issue 9, September 2014

Fault Tolerance for Adaptive Replication in Grid Using Fused Data Structures

J. Vengadasubramanikandan

Abstract: This paper describes a technique to tolerate faults in large data structures hosted on distributed servers, based on the concept of fused backups. The prevalent solution to this problem is replication. To tolerate the faults (dead/unresponsive data structures) among the whole distinct data structures, replication requires replicas of each data structure, resulting in number of servers and the number of fault for additional backups. This paper present a solution, referred to as fusion that uses a combination of erasure codes and selective replication to tolerate f crash faults using just additional fused backups. This paper shows that the solution achieves savings in space over replication. Further, this work present a solution to tolerate Byzantine faults (malicious data structures), that requires only backups as compared to the 2nf backups required by replication. We ensure that the overhead for normal operation in fusion is only as much as the overhead for replication. Though recovery is costly in fusion, in a system with infrequent faults, the savings in space outweighs the cost of recovery. This paper explores the theory of fused backups and provides a library of such backups for all the data structures in the Visual Studio Collection Framework. The experimental evaluation confirms that fused backups are space-efficient as compared to replication (approximately n times), while they cause very little overhead for updates. To illustrate the practical usefulness of fusion, this work use fused backups for reliability in Amazons highly available key-value store, Dynamo. While the current replication based solution uses 300 backup structures, we present a solution that only requires 120 backup structures. This results in savings in space as well as other resources such as power.

Keywords: Tolerance, Grid Computing, Data Structure, Adaptive Replication

Edition: Volume 3 Issue 9, September 2014,

Pages: 869 - 874

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