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Cloud Computing, Grid Computing, Green Computing 1.
Scheduling and Load Balancing in Large Scale Distributed Computing
Environments Large scale distributed
systems (e.g. Grid Computing, Cloud Computing) are quite prevalent today. These
systems provide high performance capabilities to a wide range of
applications. These applications normally have different, and sometimes
conflicting, requirements. This will necessitate the development of more
flexible scheduling techniques. Another factor which is detrimental to the
performance of such these systems is the dynamic nature of such combination
of heterogeneous resources that are, for most of the time, located in
disparate locations. In addition, the availability of resources (e.g. computational,
storage, etc) for some of the time does not mean that such resources will be
available all the time. Such conditions will add more complexity to the
design of these schedulers. This also suggests the need to suites of
schedulers that can be used in different operating scenarios. This project
deals with the study and development of a variety of scheduling scenarios and
algorithms that can help in achieving the ultimate goal of furthering our
understanding of scheduling in large scale distributed systems. 2. Quality of Service in Distributed Computing Systems
There is a need to
develop a comprehensive framework to determine what QoS
means in the context of the distributed systems and the services that will be
provided through such infrastructure. What complicates the scenario is that
the fact the distributed systems will provide a whole range of services and
not only high performance computing. There is a great need for the
development of different QoS metrics for distributed
systems that could capture all the complexity and provide meaningful measures
for a wide range of applications. This will possibly mean that new classes of
algorithms and simulation models need to be developed. These should be able
to characterize the variety of workloads and applications that can be used to
better understand the behaviour of distributed computing systems under
different operating conditions.
As
the complexity of distributed systems increases time there will be a need
to endow such systems with capabilities that make them capable of operating
in disaster scenarios. What makes this problem very complex is the
heterogeneous nature of today’s distributed computing environments that
could be made up of hundreds or thousands of components (computers,
databases, etc). In addition, a user in one location might not be able to
have control over other parts of the system. So it is rather logical that
there is a need for “smart” algorithms (protocols) that can
achieve such an acceptable level of fault-tolerance and account for a variety
of disaster recovery scenarios. 4. Application
Isolation Techniques in Cloud Computing Platforms The cloud computing model
allows people to use CPU, storage and even network bandwidth from remote
resource providers. These resource providers often host lots of third-party
applications in tens of thousands machines in their data centres. As many
third-party applications share physical CPUs, storage and networks, how to
isolate these applications becomes an issue. The project will review the
technologies, such as virtual machines, used by existing “cloud computing”
infrastructure providers for achieving application level isolation and
examine the effectiveness of these technologies. It will also investigate how
to make a “cloud computing” platform trustworthy. 5. Accountability
in Distributed Systems for Bioinformatics Data Management As a growing number of
scientific activities involve computers, there are increasing needs to make
important activities accountable in computer systems, e.g., people may be
interested in how a conclusion is drawn, what data support a claim and what
tools are used for processing the data. Existing efforts related to
this problem include data provenance management in database area and
scientific workflow management in Grid computing area. However, these works
do not explicitly addresses accountability problem. The project
intends to produce a few mechanisms to manage distributed workflow and data
for attesting complex bioinformatics computing processes. 6. Application-Specific
Service Level Agreement and Energy-Efficiency Improvement in Cloud Computing
Platforms Cloud computing
environments are gaining popularity as the de facto platforms for many
applications. These systems bring a range of heterogeneous resources that
should be able to function continuously and autonomously. However, these
systems expend a lot of energy. Thus, this project aims to develop new
algorithms and tools for energy-aware resource management allocation for
large-scale distributed systems enabling these systems to become
environmentally friendly. The proposed framework will be
‘holistic’ in nature seamlessly integrating a set of both
site–level and system–level/service–level
energy–aware resource allocation schemes addressing a range of complex
scenarios and different operating conditions.
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