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JournalISSN: 2192-113X

Journal of Cloud Computing 

SpringerOpen
About: Journal of Cloud Computing is an academic journal published by SpringerOpen. The journal publishes majorly in the area(s): Cloud computing & Edge computing. It has an ISSN identifier of 2192-113X. It is also open access. Over the lifetime, 235 publications have been published receiving 3460 citations.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: A framework for integrating various trust mechanisms together to reveal chains of trust in the cloud is suggested by suggesting more rigorous mechanisms based on evidence, attribute certification, and validation.
Abstract: Trust is a critical factor in cloud computing; in present practice it depends largely on perception of reputation, and self assessment by providers of cloud services. We begin this paper with a survey of existing mechanisms for establishing trust, and comment on their limitations. We then address those limitations by proposing more rigorous mechanisms based on evidence, attribute certification, and validation, and conclude by suggesting a framework for integrating various trust mechanisms together to reveal chains of trust in the cloud.

212 citations

Journal ArticleDOI
TL;DR: A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis and the service analyses the Bristol Open data by identifying correlations between selected urban environment indicators.
Abstract: A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends.

184 citations

Journal ArticleDOI
TL;DR: An extension of an approach first proposed in a theoretical study by Wu, Zhang, & Huberman which is implemented in an agent-based simulation, using asset classes and price-levels directly modelled on currently available real-world data from markets relevant to cloud computing, for both service-providers provisioning and customers' demand patterns.
Abstract: One of the major benefits of cloud computing is the ability for users to access resources on a pay-as-you go basis, thereby potentially reducing their costs and enabling them to scale applications rapidly. However, this approach does not necessarily benefit the provider. Providers have the responsibility of ensuring that they have the physical infrastructure to meet their users' demand and that their performance meets agreed service level agreements. Without an accurate view of future demand, planning for variable costs such as staff, replacement servers or coolers, and electricity supplies, can all be very difficult, and optimising the distribution of virtual machines presents a major challenge.

174 citations

Journal ArticleDOI
TL;DR: This work contributes to understanding why trust establishment is important in the Cloud computing landscape, how trust can act as a facilitator in this context and what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.
Abstract: Cloud computing offers massively scalable, elastic resources (e.g., data, computing power, and services) over the internet from remote data centres to the consumers. The growing market penetration, with an evermore diverse provider and service landscape, turns Cloud computing marketplaces a highly competitive one. In this highly competitive and distributed service environment, the assurances are insufficient for the consumers to identify the dependable and trustworthy Cloud providers. This paper provides a landscape and discusses incentives and hindrances to adopt Cloud computing from Cloud consumers’ perspective. Due to these hindrances, potential consumers are not sure whether they can trust the Cloud providers in offering dependable services. Trust-aided unified evaluation framework by leveraging trust and reputation systems can be used to assess trustworthiness (or dependability) of Cloud providers. Hence, cloud-related specific parameters (QoS + ) are required for the trust and reputation systems in Cloud environments. We identify the essential properties and corresponding research challenges to integrate the QoS + parameters into trust and reputation systems. Finally, we survey and analyse the existing trust and reputation systems in various application domains, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) why trust establishment is important in the Cloud computing landscape, 2) how trust can act as a facilitator in this context and 3) what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.

159 citations

Journal ArticleDOI
TL;DR: The divide-and-conquer approach improves the proposed system, as is proven experimentally through comparison with the existing BATS and improved differential evolution algorithm (IDEA) frameworks when turnaround time and response time are used as performance metrics.
Abstract: Cloud computing is required by modern technology. Task scheduling and resource allocation are important aspects of cloud computing. This paper proposes a heuristic approach that combines the modified analytic hierarchy process (MAHP), bandwidth aware divisible scheduling (BATS) + BAR optimization, longest expected processing time preemption (LEPT), and divide-and-conquer methods to perform task scheduling and resource allocation. In this approach, each task is processed before its actual allocation to cloud resources using a MAHP process. The resources are allocated using the combined BATS + BAR optimization method, which considers the bandwidth and load of the cloud resources as constraints. In addition, the proposed system preempts resource intensive tasks using LEPT preemption. The divide-and-conquer approach improves the proposed system, as is proven experimentally through comparison with the existing BATS and improved differential evolution algorithm (IDEA) frameworks when turnaround time and response time are used as performance metrics.

156 citations

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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202153
202062
201922
201816
20178
20163