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Collaboration with University of Toronto, University of Alberta, University of Victoria and several industrial partners (2011)
The NSERC Strategic Network for Smart Applications on Virtual
Infrastructure (SAVI) is a partnership of Canadian industry, academia,
research and education networks, and high performance computing centres
to investigate key elements of future application platforms. The main
research goal of the SAVI Network is to address the design of future
applications platforms built on flexible, versatile and evolvable
infrastructure that can readily deploy, maintain, and retire the
large-scale, possibly short-lived, distributed applications that will be
typical in the future applications marketplace.
SAVI will design a national distributed application platform testbed for
creating and delivering Future Internet applications. The SAVI testbed
will provide flexible, virtualized converged infrastructure to support
experimental research in application-oriented networking, cloud
computing, integrated wireless/optical access networks, and Future
Internet architectures. The testbed will also support experimentation in
applications built on advanced services that provide intelligence
through analytics and advanced media processing.
The SAVI Network will help advance Canada’s Digital Economy Strategy by
strengthening the industrial base in information and communications
technology (ICT) through the active participation of its partners in the
research program as well as the preparation of highly qualified
manpower with expertise in the design and operation of globally
competitive ICT infrastructure and the creation of innovative and
disruptive products, services, and applications.
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Collaboration with Dr. Alex Thomo, University of Victoria (2011)
Efficient algorithms for effective web services selection
becomes an important issue to enhance the traditional web
search discovery. With the development of Service-Oriented Architecture(SOA),
the Quality of Service(QoS) was more critical for a QoS driven
service selection. We investigated the effective algorithms that can be reused and applied for the retrieval of
web services, with multiple parameters, under different criteria, and compromises between conflicting criteria.
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Collaboration with Dr. Ernesto Damiani, University of Milan (2007–2011)
Current security certification schemes do not provide, from
an end-user perspective, a reliable way to assess the trustworthiness of
a composite applications in the context where it will be actually
executed. ASSERT4SOA will fill this gap by producing novel techniques
and tools integrated within the SOA lifecycle – for expressing,
assessing and certifying security properties for complex
service-oriented applications, composed of distributed software services
that may dynamically be selected, assembled and replaced, and running
within complex and continuously evolving software ecosystems.
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Collaboration with Dr. Hausi A. Müller, University of Victoria (2007–2012)
Our work aims to track and consider dynamic attributes, such as availability,
in web service discovery mechanisms. Our algorithm is
based similar to those that benefit autonomic
computing. The goal is to increase the quality of
and ultimately consumer's satisfaction with the returned
results, by returning fewer extraneous results.
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Collaboration with Dr. Hausi A. Müller, University of Victoria (2007–2012)
Our approach to high quality
service discovery aims to overcome the limitations of existing methods, which assume the world is static, by
considering dynamic attributes, which are currently not supported by service discovery mechanisms,
and employing context-aware information retrieval techniques.
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Collaboration with Dr. Andrew J. Malton and Dr. Richard C. Holt, University of Waterloo (2003–2005)
Research on University of Waterloo graph visualization tool—LSEdit—that was designed to
explore and edit software “landscapes” produced by tools such as CPPX, QLDX, JAVEX and ASX.
Because LSEdit only visualized static information, my task was to visualize the dynamic landscape of software, and to research
which information should be visualized and how. We provided a
semi-automated technique of lightweight dynamic
analysis to simplify the dynamic analysis techniques for
understanding a software system's behaviour when a software project evolves and its
complexity increases. Our technique limits this surplus information and describes the most important interaction
through lightweight dynamic analysis.