A decentralized and fault‐tolerant Desktop Grid system for distributed applications

Abbes, Heithem; Cérin, Christophe; Jemni, Mohamed

doi:10.1002/cpe.1477

Cited by 19 publications

(4 citation statements)

References 35 publications

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“…Zhang et al (2006) proposed an algorithm for dynamically locating data replica servers within a grid to optimise performance and improve fault tolerance, though scalability issues were not examined. Abbes and Crin (2010), proposed to eliminate the need for a centralised server, to remove therefore the single point of failure and bottleneck of existing desktop grids. Instead, each computer can play alternatively the role of client or server.…”

Section: Related Workmentioning

confidence: 99%

Fault tolerance in grid computing by resource clustering

Khaldi

Rebbah

Meftah

et al. 2020

IJITST

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Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, the characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. In this paper, we propose FT-GRC a fault tolerance model that seeks to find the best substitute for the failed node by the clustering of the grid resources. This model is based on dynamic coloured graphs without replication of computer resources. The proposed fault tolerance mechanism uses scoring function to determine the appropriate substitute for each failed node by calculating the performance level of each node, and later exploits clustering to determine optimally the choice of substitute. Experimental results show the efficiency of the scoring method and the gain obtained by looking for the substitutes in the same cluster and then by the research for the nearest substitutes.

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Section: Related Workmentioning

confidence: 99%

Fault tolerance in grid computing by resource clustering

Khaldi

Rebbah

Meftah

et al. 2020

IJITST

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“…The architecture used in our approach is a distributed cloud environment that is deployed on "volunteer PCs" at home, office or in small data centres and run SlapOS [2] open source cloud provisioning system, either standalone or in combination with existing virtualization technologies [4] SlapOS is an open source Cloud Operating system which was inspired by recent research in Grid Computing and in particular by BonjourGrid [9]- [11] a meta Desktop Grid middleware for the coordination of multiple instances of Desktop Grid middleware. It is based on the motto that "everything is a process".…”

Section: Cloud Architecturementioning

confidence: 99%

A solution for implementing resilience in open source Cloud platforms

Suciu

Cernat

Todoran

et al. 2012

2012 9th International Conference on Communications (COMM)

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SlapOS is the first open source operating system for Distributed Cloud Computing. SlapOS is based on a grid computing daemon -called slapgrid -which is capable of installing any software on a PC and instantiate any number of processes of potentially infinite duration of any installed software. Slapgrid daemon receives requests from a central scheduler -the SlapOS Master -which collects back accounting information from each process. SlapOS Master follows an Enterprise Resource Planning (ERP) model to handle at the same time process allocation optimization and billing. Recent research on Cloud Computing has focused on the implementation of Service Level Agreements (SLA) and operation of large Data Centers. However, in case of Force Majeure such as natural disaster, strike, terrorism, unpreventable accident, etc., Service Level Agreements (SLA) no longer apply. Rather than centralizing Cloud Computing resources in large data centers, Distributed Cloud Computing resources are aggregated from a grid of standard PCs hosted in homes, offices and small data centers.

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“…PastryGrid [23] is built on Pastry [8], and a job scheduling is performed by sequentially traversing nodes in a pool until all queued jobs are assigned. In WaveGrid [22], a timezone-aware overlay network is formed to consider time zone information for a job scheduling.…”

Section: Related Workmentioning

confidence: 99%

Parallel Processing Framework on a P2P System Using Map and Reduce Primitives

Lee

Choi

Ganguly

et al. 2011

2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PHD Forum

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Abstract-This paper presents a parallel processing framework for structured Peer-To-Peer (P2P) networks. A parallel processing task is expressed using Map and Reduce primitives inspired by functional programming models. The Map and Reduce tasks are distributed to a subset of nodes within a P2P network for execution by using a self-organizing multicast tree. The distribution latency cost of multicast method is O(log(N )), where N is a number of target nodes for task processing. Each node getting a task performs the Map task, and the task result is summarized and aggregated in a distributed fashion at each node of the multicast tree during the Reduce task. We have implemented this framework on the Brunet P2P system, and the system currently supports predefined Map and Reduce tasks or tasks inserted through Remote Procedure Call (RPC) invocations. A simulation result demonstrates the scalability and efficiency of our parallel processing framework. An experiment result on PlanetLab which performs a distributed K-Means clustering to gather statistics of connection latencies among P2P nodes shows the applicability of our system in applications such as monitoring overlay networks.

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A decentralized and fault‐tolerant Desktop Grid system for distributed applications

Cited by 19 publications

References 35 publications

Fault tolerance in grid computing by resource clustering

Fault tolerance in grid computing by resource clustering

A solution for implementing resilience in open source Cloud platforms

Parallel Processing Framework on a P2P System Using Map and Reduce Primitives

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