Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
Task Allocation for Maximizing Reliability of Distributed Computer Systems
IEEE Transactions on Computers
Task scheduling in parallel and distributed systems
Task scheduling in parallel and distributed systems
IEEE Transactions on Parallel and Distributed Systems
Hypertool: A Programming Aid for Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Dynamic, Competitive Scheduling of Multiple DAGs in a Distributed Heterogeneous Environment
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Sinfonia: a new paradigm for building scalable distributed systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Dynamo: amazon's highly available key-value store
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Reliability and Scheduling on Systems Subject to Failures
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
Dynamic Load-Balanced Multicast for Data-Intensive Applications on Clouds
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Cloud-DLS: Dynamic trusted scheduling for Cloud computing
Expert Systems with Applications: An International Journal
Trusted Dynamic Scheduling for Large-Scale Parallel Distributed Systems
ICPPW '11 Proceedings of the 2011 40th International Conference on Parallel Processing Workshops
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Science is increasingly becoming more and more data-driven. The ability of a geographically distributed community of scientists to access and analyze large amounts of data has emerged as a significant requirement for furthering science. In data intensive computing environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. Novel algorithms are needed to schedule the jobs on the trusty nodes to execute, assure the high speed of communication, reduce the jobs execution time, lower the ratio of failure execution, and improve the security of execution environment of important data. In this paper, a kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among computing nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayesian cognitive method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling (DLS) algorithm, the Trust-Dynamic Level Scheduling (Trust-DLS) algorithm is proposed. Moreover, a benchmark is structured to span a range of data intensive computing characteristics for evaluation the proposed method. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of data intensive workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in large-scale data intensive computing environment.