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Friday, 23 February 2018

Grid Scheduling based on Collaborative Random Early Detection Strategies(2010)

Grid Scheduling based on Collaborative Random Early Detection Strategies(2010)

Abstract
A fundamental problem in large scale Grids is the need for efficient and scalable techniques for resource discovery and scheduling. In traditional resource scheduling systems a single scheduler handles information about all computing resources and schedules jobs. This centralized approach has a serious scalability problem, since it introduces a bottleneck, as well as a single point of failure. Some decentralized scheduling systems have been proposed to improve scalability. However, the main contributions in this area are generally carried out under the assumption of several coordinated schedulers. Nevertheless this approach leads to high communication costs. Such costs are mainly caused by the strong dependency on negotiation among scheduler-to-scheduler and scheduler-to-resource communication. Current approaches to decentralized resource management - in particularly approaches based on Random Early Detection (RED) - are non-coordinated since these schedulers make scheduling related decisions in an independent way. This paper introduces a collaborative model of decentralized scheduling that improves resource scheduling based on RED strategies via gossiping. With this approach, schedulers can receive information from other schedulers without creating a high communication overhead and continue scheduling jobs in an independent way. The simulation results shows that our proposal is scalable and it handles large resources efficiently on large scale Grids.
Existing system
It consists of both reactive and proactive components. It does not maintain routes to all possible destinations at all times (like the original ACO algorithms for wired networks), but only sets up paths when they are needed at the start of a data session. This is done in a reactive route setup phase, where ant agents called reactive forward ants are launched by the source in order to find multiple paths to the destination, and backward ants return to the source to set up the paths. According to the common practice in ACO algorithms, the paths are set up in the form of pheromone tables indicating their respective quality. After the route setup, data packets are routed stochastically over the different paths following these pheromone tables. While the data session is going on, the paths are monitored, maintained and improved proactively using different agents, called proactive forward ants. The algorithm reacts to link failures with either a local route repair or by warning preceding nodes on the paths.

SYSTEM SPECIFICATION
 Hardware Requirements
Processor                                          :     Pentium IV
Speed                                                          :     above 1GHz  
Ram capacity                                    :     2GB
Hard disk capacity                                     :     20 GB
Monitor                                             :     14 “samtron monitor
Printer                                               :     TVS 80 color
Motherboard                                     :     Intel
Keyboard                                          :     Logitech Of 104 Keys
Mouse                                               :     Logitech Mouse
Software Requirements
Operating system                              :      Windows
Front end                                          :      Java 

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