9,910 research outputs found
Design and Implementation of a Distributed Middleware for Parallel Execution of Legacy Enterprise Applications
A typical enterprise uses a local area network of computers to perform its
business. During the off-working hours, the computational capacities of these
networked computers are underused or unused. In order to utilize this
computational capacity an application has to be recoded to exploit concurrency
inherent in a computation which is clearly not possible for legacy applications
without any source code. This thesis presents the design an implementation of a
distributed middleware which can automatically execute a legacy application on
multiple networked computers by parallelizing it. This middleware runs multiple
copies of the binary executable code in parallel on different hosts in the
network. It wraps up the binary executable code of the legacy application in
order to capture the kernel level data access system calls and perform them
distributively over multiple computers in a safe and conflict free manner. The
middleware also incorporates a dynamic scheduling technique to execute the
target application in minimum time by scavenging the available CPU cycles of
the hosts in the network. This dynamic scheduling also supports the CPU
availability of the hosts to change over time and properly reschedule the
replicas performing the computation to minimize the execution time. A prototype
implementation of this middleware has been developed as a proof of concept of
the design. This implementation has been evaluated with a few typical case
studies and the test results confirm that the middleware works as expected
Distributed Management of Massive Data: an Efficient Fine-Grain Data Access Scheme
This paper addresses the problem of efficiently storing and accessing massive
data blocks in a large-scale distributed environment, while providing efficient
fine-grain access to data subsets. This issue is crucial in the context of
applications in the field of databases, data mining and multimedia. We propose
a data sharing service based on distributed, RAM-based storage of data, while
leveraging a DHT-based, natively parallel metadata management scheme. As
opposed to the most commonly used grid storage infrastructures that provide
mechanisms for explicit data localization and transfer, we provide a
transparent access model, where data are accessed through global identifiers.
Our proposal has been validated through a prototype implementation whose
preliminary evaluation provides promising results
pony - The occam-pi Network Environment
Although concurrency is generally perceived to be a `hard' subject, it can in fact be very simple --- provided that the underlying model is simple. The occam-pi parallel processing language provides such a simple yet powerful concurrency model that is based on CSP and the pi-calculus. This paper presents pony, the occam-pi Network Environment. occam-pi and pony provide a new, unified, concurrency model that bridges inter- and intra-processor concurrency. This enables the development of distributed applications in a transparent, dynamic and highly scalable way. The first part of this paper discusses the philosophy behind pony, explains how it is used, and gives a brief overview of its implementation. The second part evaluates pony's performance by presenting a number of benchmarks
Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications
Many scientific problems require multiple distinct computational tasks to be
executed in order to achieve a desired solution. We introduce the Ensemble
Toolkit (EnTK) to address the challenges of scale, diversity and reliability
they pose. We describe the design and implementation of EnTK, characterize its
performance and integrate it with two distinct exemplar use cases: seismic
inversion and adaptive analog ensembles. We perform nine experiments,
characterizing EnTK overheads, strong and weak scalability, and the performance
of two use case implementations, at scale and on production infrastructures. We
show how EnTK meets the following general requirements: (i) implementing
dedicated abstractions to support the description and execution of ensemble
applications; (ii) support for execution on heterogeneous computing
infrastructures; (iii) efficient scalability up to O(10^4) tasks; and (iv)
fault tolerance. We discuss novel computational capabilities that EnTK enables
and the scientific advantages arising thereof. We propose EnTK as an important
addition to the suite of tools in support of production scientific computing
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