A reputation-based approach for choosing reliable resources in peer-to-peer networks
Proceedings of the 9th ACM conference on Computer and communications security
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
A Utility-Based Two Level Market Solution for Optimal Resource Allocation in Computational Grid
ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
Adaptive Reputation-Based Scheduling on Unreliable Distributed Infrastructures
IEEE Transactions on Parallel and Distributed Systems
Incentive-Based Scheduling for Market-Like Computational Grids
IEEE Transactions on Parallel and Distributed Systems
Reliability in grid computing systems
Concurrency and Computation: Practice & Experience - A Special Issue from the Open Grid Forum
A reputation-driven scheduler for autonomic and sustainable resource sharing in Grid computing
Journal of Parallel and Distributed Computing
Cloud Security with Virtualized Defense and Reputation-Based Trust Mangement
DASC '09 Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing
ADREA: A Framework for Adaptive Resource Allocation in Distributed Computing Systems
PDCAT '10 Proceedings of the 2010 International Conference on Parallel and Distributed Computing, Applications and Technologies
Hi-index | 0.00 |
The scale of the parallel and distributed systems (PDSs), such as grids and clouds, and the diversity of applications running on them put reliability a high priority performance metric. This paper presents a reputation-based resource allocation strategy for PDSs with a market model. Resource reputation is determined by availability and reliable execution. The market model helps in defining a trust interaction between provider and consumer that leverages dependable computing. We also have explicitly taken into account data staging and its delay when making the decisions. Results demonstrate that our approach significantly increases successful execution, while exploiting diversity in tasks and resources.