Cloudbus Toolkit for Market-Oriented Cloud Computing

This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.Comment: 21 pages, 6 figures, 2 tables, Conference pape

Investigating co-innovation in exploratory partnerships: An analytical framework based on design theory

Intensive innovation contexts push organizations to search for new partnerships in order to explore value creation opportunities and to access external resources. Recent literature shows that more and more partnerships are established before the object and the terms of the partnership has been determined. In such exploratory partnerships (Segrestin 2006), motivated by the prospect of joint value creation and co-innovation, partners explore and progressively construct a common project and an agreement on the sharing of tasks and outputs. In this work we investigate co-innovation dynamics of exploratory partnerships within the context of MINATEC IDEAs Laboratory® (MIL). MIL comprises several industrial partners from different sectors and a major scientific partner specialized in micro-nanotechnologies. Partners of MIL share resources to explore new project ideas and co-innovation opportunities. A particularity of MIL is that all its industrial partners come from different business sectors. The diversity of agendas, competencies and design strategies exhibited at MIL allow the examination of different dimensions of exploratory partnerships: Are there different configurations of exploratory partnerships? What are the dynamics of exploration? How does the exploratory process converge? An analytical framework based on CK design theory is used in order to examine the dynamics of exploratory partnerships within MIL.co-innovation; exploratory partnership;design theory; design oriented organizations;collective action

PORTFOLIO MANAGEMENT OF INNOVATION FIELDS : APPLYING CK DESIGN THEORY IN CROSS INDUSTRY EXPLORATORY PARTNERSHIP

Our paper refers to an industrial practice based on an integrated theoretical framework of design, CK design theory (Hatchuel and Weil, 2002, Hatchuel and Weil, 2003, Hatchuel and Weil, 2008), to support people in management of innovation fields. This study is based on an empirical case in a new form of R&D partnerships, the Cross Industry Exploratory Partnerships. MINATEC IDEAs Laboratory® is composed of a broad scope of partners 2 which aims to co-explore opportunities of micronanotechnologies. The paper deals with a strategic design tool, OPERA, which has been experimented since 2007 and involved participation of design team work and powerholders. During two years, creative insights and projects of the two laboratory's major innovation fields have been collected and structured within CK theory. This tool permits power-holders to drive innovation projects by giving an overview of explored concepts (and still not explored), activation and production of competencies and knowledge.CK theory; innovative design; innovation partnership; OPERA; design theory; management of innovation

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page