Introduction to the theory of neural computation
Introduction to the theory of neural computation
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Reliable Distributed Computing with the ISIS Toolkit
Reliable Distributed Computing with the ISIS Toolkit
Distributed Systems: Concepts and Design
Distributed Systems: Concepts and Design
SETI @ home project and its website
Crossroads
Fault tolerant cluster computing through replication
ICPADS '97 Proceedings of the 1997 International Conference on Parallel and Distributed Systems
Highly fault-tolerant parallel computation
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
BOINC: A System for Public-Resource Computing and Storage
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Achieving high availability and performance computing with an HA-OSCAR cluster
Future Generation Computer Systems - Special issue: High-speed networks and services for data-intensive grids: The DataTAG project
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Optimal task partition and distribution in grid service system with common cause failures
Future Generation Computer Systems - Special section: Information engineering and enterprise architecture in distributed computing environments
Hi-index | 0.00 |
Traditionally, network and computation failure on a heterogeneous network are viewed as an unfortunate obstacle to reliable, efficient computation. We propose that such noise can be incorporated into the algorithm design as part of the necessary source of randomness used in stochastic computation. This paradigm incorporates network and computation failure at a high level in the solution-discovery algorithm, rather than attempting to hide and suppress all such noise at the lowest possible levels in the computation tool. This idea enables the creation of a network solution system with extremely small amounts of global state. This lack of required system state allows for heightened degrees of scalability in the computation engine, and fewer resources are consumed by system management. Algorithms with a stochastic component are easily adapted to this system; various types of evolutionary computation are particularly well adapted to this hybrid paradigm. A specific example using a modified steady-state genetic algorithm is provided to explore the functionality of the resulting composite system. The developed architecture is used to calculate the solution to a number of problems, in each case converging on a solution measurably faster than that of a fully ''fault-tolerant'' scheme, thereby resulting in lower overhead and faster execution time.