5 resultados para Cloud service broker

em Aston University Research Archive


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Technological advancements enable new sourcing models in software development such as cloud computing, software-as-a-service, and crowdsourcing. While the first two are perceived as a re-emergence of older models (e.g., ASP), crowdsourcing is a new model that creates an opportunity for a global workforce to compete with established service providers. Organizations engaging in crowdsourcing need to develop the capabilities to successfully utilize this sourcing model in delivering services to their clients. To explore these capabilities we collected qualitative data from focus groups with crowdsourcing leaders at a large technology organization. New capabilities we identified stem from the need of the traditional service provider to assume a "client" role in the crowdsourcing context, while still acting as a "vendor" in providing services to the end client. This paper expands the research on vendor capabilities and IS outsourcing as well as offers important insights to organizations that are experimenting with, or considering, crowdsourcing.

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Crowdsourcing platforms that attract a large pool of potential workforce allow organizations to reduce permanent staff levels. However managing this "human cloud" requires new management models and skills. Therefore, Information Technology (IT) service providers engaging in crowdsourcing need to develop new capabilities to successfully utilize crowdsourcing in delivering services to their clients. To explore these capabilities we collected qualitative data from focus groups with crowdsourcing leaders at a large multinational technology organization. New capabilities we identified stem from the need of the traditional service provider to assume a "client" role in the crowdsourcing context, while still acting as a "vendor" in providing services to the end-client. This paper expands the research on vendor capabilities and IT outsourcing as well as offers important insights to organizations that are experimenting with, or considering, crowdsourcing. © 2014 Elsevier B.V. All rights reserved.

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Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. The composition aims at satisfying some objectives (e.g. Utilizing storage and eliminating waste, sharing space and optimizing for energy, reducing computational cost etc.). In cases when a single volunteered service does not satisfy a request, VSC will be required. In this paper, we contribute to three approaches for composing volunteered services: these are exhaustive, naïve and utility-based search approach to VSC. The proposed new utility-based approach, for instance, is based on measuring the utility that each volunteered service can provide to each request and systematically selects the one with the highest utility. We found that the utility-based approach tend to be more effective and efficient when selecting services, while minimizing resource waste when compared to the other two approaches.

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The enormous potential of cloud computing for improved and cost-effective service has generated unprecedented interest in its adoption. However, a potential cloud user faces numerous risks regarding service requirements, cost implications of failure and uncertainty about cloud providers' ability to meet service level agreements. These risks hinder the adoption of cloud. We extend the work on goal-oriented requirements engineering (GORE) and obstacles for informing the adoption process. We argue that obstacles prioritisation and their resolution is core to mitigating risks in the adoption process. We propose a novel systematic method for prioritising obstacles and their resolution tactics using Analytical Hierarchy Process (AHP). We provide an example to demonstrate the applicability and effectiveness of the approach. To assess the AHP choice of the resolution tactics we support the method by stability and sensitivity analysis. Copyright 2014 ACM.

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Cloud computing is a new technological paradigm offering computing infrastructure, software and platforms as a pay-as-you-go, subscription-based service. Many potential customers of cloud services require essential cost assessments to be undertaken before transitioning to the cloud. Current assessment techniques are imprecise as they rely on simplified specifications of resource requirements that fail to account for probabilistic variations in usage. In this paper, we address these problems and propose a new probabilistic pattern modelling (PPM) approach to cloud costing and resource usage verification. Our approach is based on a concise expression of probabilistic resource usage patterns translated to Markov decision processes (MDPs). Key costing and usage queries are identified and expressed in a probabilistic variant of temporal logic and calculated to a high degree of precision using quantitative verification techniques. The PPM cost assessment approach has been implemented as a Java library and validated with a case study and scalability experiments. © 2012 Springer-Verlag Berlin Heidelberg.