Data Specific Ranking in Cloud

Data Specific Ranking in Cloud

Anirban Kundu (Shenzhen Key Laboratory of Transformation Optics and Spatial Modulation, Kuang-Chi Institute of Advanced Technology, Shenzhen, China), Guanxiong Xu (Shenzhen Key Laboratory of Transformation Optics and Spatial Modulation, Kuang-Chi Institute of Advanced Technology, Shenzhen, China) and Chunlin Ji (Shenzhen Key Laboratory of Transformation Optics and Spatial Modulation, Kuang-Chi Institute of Advanced Technology, Shenzhen, China)
Copyright: © 2014 |Pages: 10
DOI: 10.4018/ijcac.2014100103
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Abstract

In this paper, the major goal is to achieve searching in distributed network with proper ranking. Heterogeneous framework is going to be utilized using existing machines and high-end workstations. Data redundancy is responsible for enhancing dynamic scheduling in run-time. Dynamic scheduling is going to be exploited determining possible sub-network and related servers for specific user targeted tasks using active and busy ranking status of servers. Minimization of time and maximization of speed are objectives of proposed system structure.
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Several researchers exhibit various angles of distributed artificial systems based on real-time activities. A system is a collection of machines, workstations, servers and some other resources connected by networks. Distributed performance computing (Buschmann, Henney, Schmidt, 2007; Spinnato, Albada, Sloot, 2004) in heterogeneous systems employs the distributed objects as applications (Radojevic, Salcic, Roop, 2011). These applications are arranged in such a manner that the same type of user requests can be executed in distinct machines which are situated in different locations. Sometimes, these machines fall in the same group or cluster at same location (Liu, Xiao, Liu, Ni, Zhang, 2005). Huge collection of heterogeneous resources offers an opportunity for delivering high performance on a range of applications. Successful scheduling of system resources achieves high performance (Xu, Tang, Lee, 2006). It is being shown that scheduling can be performed automatically, efficiently, and profitably for a range of computations in this environment. Load balancing techniques can be used to balance the network based activities. Effective Scheduling is quite necessary for maintaining loosely coupled processors (Anger, Hwang, Chow, 1990). Software issues have been resolved by some well-known methods in effective processing systems (Armstrong, Watson, Siegel, 1993). The success of any system network depends on its distribution (Black, Hutchinson, Jul, Levy, 1985). Heterogeneous multi-computers are designed to form the backbone of the network (Arnould, Bitz, Cooper, Kung, Sansom, Steenkiste, 1989). High computation is required in these types of networks. So, modeling of algorithms should be maintained in a robust and generic way for scheduling computation intensive tasks at any time instance (Atallah, Black, Marinescu, Siegel, Casavant, 1992). Formation of distributed network requires transferring information in a high speed (Bergman, Braun, Chinoy, Kolawa, et al., 1993; Eckart, He, Wu, Xie, 2010).

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