6 research outputs found
A Social Cloud for Public eResearch
Abstract—Scientific researchers faced with extremely large computations or the requirement of storing vast quantities of data have come to rely on distributed computational models like cloud computing. However, distributed computation is typically complex and expensive. The Social Cloud for Public eResearch aims to provide researchers with a platform to exploit social networks to reach out to users who would otherwise be unlikely to donate computational time for scientific and other research oriented projects. In this paper we explore the motivations of users to contribute computational time and examine the various ways these motivations can be catered to through established social networks. We specifically look at integrating Facebook and BOINC, and discuss the architecture of the functional system and the novel social engineering algorithms that power it. I
Designing a Resource Broker for Heterogeneous Grids
Grids provide uniform access to aggregations of heterogeneous resources and
services such as computers, networks and storage owned by multiple
organizations. However, such a dynamic environment poses many challenges for
application composition and deployment. In this paper, we present the design of
the Gridbus Grid resource broker that allows users to create applications and
specify different objectives through different interfaces without having to
deal with the complexity of Grid infrastructure. We present the unique
requirements that motivated our design and discuss how these provide
flexibility in extending the functionality of the broker to support different
low-level middlewares and user interfaces. We evaluate the broker with
different job profiles and Grid middleware and conclude with the lessons learnt
from our development experience.Comment: 26 pages, 15 figure
The Social Cloud for Public eResearch
Scientific researchers faced with extremely large computations or the requirement
of storing vast quantities of data have come to rely on distributed
computational models like grid and cloud computing. However,
distributed computation is typically complex and expensive. The Social
Cloud for Public eResearch aims to provide researchers with a platform
to exploit social networks to reach out to users who would otherwise be
unlikely to donate computational time for scientific and other research oriented
projects. This thesis explores the motivations of users to contribute
computational time and examines the various ways these motivations can
be catered to through established social networks. We specifically look
at integrating Facebook and BOINC, and discuss the architecture of the
functional system and the novel social engineering algorithms that power it
Master/worker parallel discrete event simulation
The execution of parallel discrete event simulation across metacomputing infrastructures is examined. A master/worker architecture for parallel discrete event simulation is proposed providing robust executions under a dynamic set of services with system-level support for fault tolerance, semi-automated client-directed load balancing, portability across heterogeneous machines, and the ability to run codes on idle or time-sharing clients without significant interaction by users. Research questions and challenges associated with issues and limitations with the work distribution paradigm, targeted computational domain, performance metrics, and the intended class of applications to be used in this context are analyzed and discussed. A portable web services approach to master/worker parallel discrete event simulation is proposed and evaluated with subsequent optimizations to increase the efficiency of large-scale simulation execution through distributed master service design and intrinsic overhead reduction. New techniques for addressing challenges associated with optimistic parallel discrete event simulation across metacomputing such as rollbacks and message unsending with an inherently different computation paradigm utilizing master services and time windows are proposed and examined. Results indicate that a master/worker approach utilizing loosely coupled resources is a viable means for high throughput parallel discrete event simulation by enhancing existing computational capacity or providing alternate execution capability for less time-critical codes.Ph.D.Committee Chair: Fujimoto, Richard; Committee Member: Bader, David; Committee Member: Perumalla, Kalyan; Committee Member: Riley, George; Committee Member: Vuduc, Richar
Digital ecosystems
We view Digital Ecosystems to be the digital counterparts of biological ecosystems, which
are considered to be robust, self-organising and scalable architectures that can automatically
solve complex, dynamic problems. So, this work is concerned with the creation, investigation,
and optimisation of Digital Ecosystems, exploiting the self-organising properties of biological
ecosystems. First, we created the Digital Ecosystem, a novel optimisation technique inspired
by biological ecosystems, where the optimisation works at two levels: a first optimisation,
migration of agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on evolutionary computing
that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant
constraints. We then investigated its self-organising aspects, starting with an extension
to the definition of Physical Complexity to include the evolving agent populations of our
Digital Ecosystem. Next, we established stability of evolving agent populations over time,
by extending the Chli-DeWilde definition of agent stability to include evolutionary dynamics.
Further, we evaluated the diversity of the software agents within evolving agent populations,
relative to the environment provided by the user base. To conclude, we considered alternative
augmentations to optimise and accelerate our Digital Ecosystem, by studying the accelerating
effect of a clustering catalyst on the evolutionary dynamics of our Digital Ecosystem, through
the direct acceleration of the evolutionary processes. We also studied the optimising effect of
targeted migration on the ecological dynamics of our Digital Ecosystem, through the indirect
and emergent optimisation of the agent migration patterns. Overall, we have advanced the
understanding of creating Digital Ecosystems, the self-organisation that occurs within them,
and the optimisation of their Ecosystem-Oriented Architecture