51,551 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
OpenPING: A Reflective Middleware for the Construction of Adaptive Networked Game Applications
The emergence of distributed Virtual Reality (VR) applications
that run over the Internet has presented networked game
application designers with new challenges. In an environment
where the public internet streams multimedia data and is
constantly under pressure to deliver over widely heterogeneous
user-platforms, there has been a growing need that distributed VR
applications be aware of and adapt to frequent variations in their
context of execution. In this paper, we argue that in contrast to
research efforts targeted at improvement of scalability, persistence
and responsiveness capabilities, much less attempts have been
aimed at addressing the flexibility, maintainability and
extensibility requirements in contemporary distributed VR
platforms. We propose the use of structural reflection as an
approach that not only addresses these requirements but also
offers added value in the form of providing a framework for
scalability, persistence and responsiveness that is itself flexible,
maintainable and extensible. We also present an adaptive
middleware platform implementation called OpenPING1 that
supports our proposal in addressing these requirements
The "MIND" Scalable PIM Architecture
MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a
Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on
each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND
architecture
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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