A Novel Approach for Scalable and Efficient Case Recommender System for E-Shoppers

Abstract

Big-Data Computing is a new critical challenge for the ICT industry. Engineers and researchers are dealing with data sets of petabyte scale in the cloud computing paradigm. Thus the demand for building a service stack to distribute, manage and process massive data sets has risen drastically. In this paper, we investigate the Big Data Broadcasting problem for a single source node to broadcast a big chunk of data to a set of nodes with the objective of minimizing the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed datacenters. This problem is one of the fundamental problems in distributed computing and is known to be NP-hard in heterogeneous environments. We model the Big-data broadcasting problem into a LockStep Broadcast Tree (LSBT) problem. The main idea of the LSBT model is to define a basic unit of upload bandwidth, r, such that a node with capacity c broadcasts data to a set of ⌊c=r⌋ children at the rate r. Note that r is a parameter to be optimized as part of the LSBT problem. We further divide the broadcast data into m chunks. These data chunks can then be broadcast down the LSBT in a pipeline manner. In a homogeneous network environment in which each node has the same upload capacity c, we show that the optimal uplink rate r of LSBT is either c=2 or c=3, whichever gives the smaller maximum completion time. For heterogeneous environments, we present an O(nlog2n) algorithm to select an optimal uplink rate r and to construct an optimal LSBT. Numerical results show that our approach performs well with less maximum completion time and lower computational complexity than other efficient solutions in literature.