An approach to the management of non-functional concerns in massively parallel and/or distributed architectures that marries parallel programming patterns with autonomic computing is presented. The necessity and suita...
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ISBN:
(纸本)9781424437511
An approach to the management of non-functional concerns in massively parallel and/or distributed architectures that marries parallel programming patterns with autonomic computing is presented. The necessity and suitability of the adoption of autonomic techniques are evidenced. Issues arising in the implementation of autonomic managers taking care of multiple concerns and of coordination among hierarchies of such autonomic managers are discussed. Experimental results are presented that demonstrate the feasibility of the approach.
Scheduling multiple applications on heterogeneous multi-clusters is challenging as the different applications have to compete for resources. A scheduler thus has to ensure a fair distribution of resources among the ap...
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ISBN:
(纸本)9781424437511
Scheduling multiple applications on heterogeneous multi-clusters is challenging as the different applications have to compete for resources. A scheduler thus has to ensure a fair distribution of resources among the applications and prevent harmful selfish behaviors while still trying to minimize their respective completion time. In this paper we consider mixed-parallel applications, represented by graphs whose nodes are data-parallel tasks, that are scheduled in two steps: allocation and mapping. We investigate several strategies to constrain the amount of resources the scheduler can allocate to each application and evaluate them over a wide range of scenarios.
Data mining in distributed systems has been facilitated by using high-support association rules. Less attention has been paid to distributed low-support/high-correlation data mining. This has proved useful in several ...
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ISBN:
(纸本)9781424437511
Data mining in distributed systems has been facilitated by using high-support association rules. Less attention has been paid to distributed low-support/high-correlation data mining. This has proved useful in several fields such as computational biology, wireless networks, web mining, security and rare events analysis in industrial plants. In this paper we present distributed versions of efficient algorithms for low-support/high-correlation data mining such as Min-Hashing, K-Min-Hashing and Locality-Sensitive-Hashing. Experimental results on real data concerning scalability, speed-up and network traffic are reported.
We describe the design of a lightweight library using MPI to support stream-processing on acyclic process structures. The design can be used to connect together arbitrary modules where each module can be its own paral...
ISBN:
(纸本)9781424437511
We describe the design of a lightweight library using MPI to support stream-processing on acyclic process structures. The design can be used to connect together arbitrary modules where each module can be its own parallel MPI program. We make extensive use of MPI groups and communicators to increase the flexibility of the library, and to make the library easier and safer to use. The notion of a communication context in MPI ensures that libraries do not conflict where a message from one library is mistakenly received by another. The library is not required to be part of any larger workflow environment and is compatible with existing MPI execution environments. The library is part of MarketMiner, a system for executing financial workflows.
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