Workform Processing: A New Paradigm for Fault-Tolerant Distributed Parallel Computation

With the recent advent of Big Data, developing efficient distributed algorithms for computing Strongly Connected Components of a large dataset has received increasing interests. For example, social networks, information networks and communication networks such as the communities of people that have formed on those networks, what community a person belongs or finding cyclic de-pendencies in the graph.Apache Giraph is an open-source implementation of Google’s Pregel. It is an iterative and real-time graph processing engine designed to be scalable, fault tolerant and highly efficient. This framework provides an accurate platform for the development of parallel algorithms in a distributed environ-ment. It adopts a vertex-centric programming model inspired by Bulk Synchronous Parallel model. A strongly connected component is a maximal sub graph in which all vertices are reachable from every other vertex. Maximal means that it is the largest possible sub graph. It is not possible to find another vertex anywhere in the graph such that it could be added to the sub graph and all the verti-ces in the sub graph would still be connected. In a directed graph G, a pair of vertices u and v are said to be strongly connected to each other if there is a path in each direction between them. Here, we have implemented a parallel algorithm which is based on the new paradigm of graph decomposi-tion for computing strongly connected components. The final outcome mainly focuses on the reduc-tion of total communication costs.

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Fault-Tolerant Parallel Computation

10.1007/978-1-4757-5210-6 ◽

1997 ◽

Cited By ~ 36

Author(s):

Paris Christos Kanellakis ◽

Alex Allister Shvartsman

Keyword(s):

Parallel Computation ◽

Fault Tolerant

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An Efficient Energy Adaptive Clustering LEACH in Wireless Sensor Network

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.439-440.510 ◽

2010 ◽

Vol 439-440 ◽

pp. 510-515

Author(s):

Lian Xing Zhang

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Fault Tolerant ◽

Wireless Sensor ◽

Threat Detection ◽

Critical Systems ◽

New Paradigm ◽

Power Sources ◽

Adaptive Clustering ◽

Programming Tools

Wireless sensor network (WSN) is an emerging class of systems made possible by cheap hardware, advanced programming tools, complex algorithms, long lasting power sources and energy efficient radio interfaces. Wireless sensor network is a new paradigm in designing fault tolerant mission critical systems, to enable varied applications like threat detection, environmental monitoring, traditional sensing and actuation and much more. The algorithmic approach to WSN differentiates itself from the protocol approach by the fact that the mathematical models used are more abstract, more general, but sometimes less realistic than the models used for protocol design. Experimental results prove that the scheme can get better effect.

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Combining Health Monitoring and Control

Diagnostics and Prognostics of Engineering Systems ◽

10.4018/978-1-4666-2095-7.ch012 ◽

2013 ◽

pp. 230-255

Author(s):

Teresa Escobet ◽

Joseba Quevedo ◽

Vicenç Puig ◽

Fatiha Nejjari

Keyword(s):

Health Monitoring ◽

Fault Tolerant ◽

Operating Conditions ◽

Monitoring And Control ◽

New Paradigm ◽

System Health Monitoring ◽

On Line ◽

And Control ◽

System Characteristics ◽

Feature System

This chapter proposes the combination of system health monitoring with control and prognosis creating a new paradigm, the health-aware control (HAC) of systems. In this paradigm, the information provided by the prognosis module about the component system health should allow the modification of the controller such that the control objectives will consider the system’s health. In this way, the control actions will be generated to fulfill the control objectives, and, at the same time, to extend the life of the system components. HAC control, contrarily to fault-tolerant control (FTC), adjusts the controller even when the system is still in a non-faulty situation. The prognosis module, with the main feature system characteristics provided by condition monitoring, will estimate on-line the component aging for the specific operating conditions. In the non-faulty situation, the control efforts are distributed to the system based on the proposed health indicator. An example is used throughout the chapter to illustrate the ideas and concepts introduced.

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