A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments

The scheduling problem in distributed data-intensive computing environments has become an active research topic due to the tremendous growth in grid and cloud computing environments. As an innovative distributed intelligent paradigm, swarm intelligence provides a novel approach to solving these potentially intractable problems. In this paper, we formulate the scheduling problem for work-flow applications with security constraints in distributed data-intensive computing environments and present a novel security constraint model. Several meta-heuristic adaptations to the particle swarm optimization algorithm are introduced to deal with the formulation of efficient schedules. A variable neighborhood particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experimental results illustrate that population based meta-heuristics approaches usually provide a good balance between global exploration and local exploitation and their feasibility and effectiveness for scheduling work-flow applications. © 2010 Elsevier Inc. All rights reserved.

A Dynamic Buffer Management Scheme for End-to-End QoS Enhancement of Multi-flow Services in HSDPA

End-user multi-flow services support is a crucial aspect of current and next generation mobile networks. This paper presents a dynamic buffer management strategy for HSDPA end-user multi-flow traffic with aggregated real-time and non-real-time flows. The scheme incorporates dynamic priority switching between the flows for transmission on the HSDPA radio channel. The end-to-end performance of the proposed strategy is investigated with an end-user multi-flow session of simultaneous VoIP and TCP-based downlink traffic using detailed HSDPA system-level simulations. Compared to an equivalent static buffer management scheme, the results show that end-to-end throughput performance gains in the non-real-time flow and better HSDPA channel utilization is attainable without compromising the real-time VoIP flow QoS constraints

Situated regional university incubation: A multi-level stakeholder perspective

From a macro perspective, it is widely acknowledged that University incubation models within a region are important stimulants of economic development through innovation and job creation. With the emergence of quadruple helix innovation ecosystems, universities have had re-evaluate their University incubation activity and models to engage more fully with industry and end users. However, within a given region, the type of University may influence their ability to engage with quadruple helix stakeholders and consequently impact their incubation activity. To date there is a scarcity of research which explores this 'meso' environment and its subsequent impact on University incubation models. Therefore, the aim of this paper is to use a stakeholder lens to explore University Incubation models within unique regional and organisational characteristics and constraints. The research methodology employed was based on a comparative case analysis of incubation of two different Universities within a UK peripheral region. It was found that variances existed in relation to the two universities incubation models which were found to result from both regional (macro environment) and organisational (meso environment) influences (i.e. university type). This research contributes to both regional and national agendas by empirically illustrating the need for appropriate design and tailoring of university incubation models (via acknowledgement of quadruple helix stakeholder influence) to incorporate contextual influences rather than adopting a best practise approach.