Abstrait

Balancing the Load to Reduce Network Traffic in Private Cloud

A.Shenbaga Bharatha Priya, J.Ganesh

Infrastructure-As-A-Service (IAAS) provides an environmental setup under anyone type of cloud. In Distributed file system (DFS), nodes simultaneously serve computing and storage functions; that is parallel Data processing and storage in cloud. Here, file is considered as a data. That file is partitioned into a number of chunks allocated in distinct nodes so that MapReduce tasks can be performed in parallel over the nodes. Files and Nodes can be dynamically created, deleted, and appended. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed as uniformly as possible among the nodes. Emerging distributed file systems in production systems strongly depend on a central node for chunk reallocation or Distributed node to maintain global knowledge of all chunks. This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size; it may thus become the performance bottleneck and the single point of failure and memory wastage in distributed nodes. In this paper, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem. Our algorithm is compared against a centralized approach in a production system and a competing distributed solution presented in the paper.