Application and data level interoperability on virtual machine in cloud computing environment

Zahara, Soffa; Pratomo, Istas; Rahardjo, Djoko Suprajitno

doi:10.1109/icwt.2015.7449238

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…From what we observed in the literature, cloud interoperability solutions are not restricted to achieve the interoperability between services only, but also to address other aspects such as cloud portability, composition, design, deployment, discovery, migration, configuration, provisioning, adaptability monitoring, management, description, and integration …”

Section: Cloud Service Interoperability: Definition and Environmentsmentioning

confidence: 97%

“…The work in Reference is the further development of testing interoperability on operating system and application (TIOSA). The authors proposed a reference test method for system migration between cloud providers to determine the interoperability level of the applications and the data running on VM systems.…”

Section: Classification Of the Cloud Service Interoperability Approachesmentioning

confidence: 99%

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A survey on the service interoperability in cloud computing: Client‐centric and provider‐centric perspectives

2020

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The vendor lock-in is a prominent issue in cloud computing. It is caused by cloud providers who offer proprietary services, which hinders the cloud interoperability. Client-centric interoperability enables the migration of the data and applications across clouds; it gives the clients control over their workloads and a wider range of service choices. Whereas, provider-centric interoperability allows the providers to collaborate. Thus, providers, who have spare resources, can lend them to other providers who lack computational or storage capabilities to overcome the limitations of their local resources. In this article, we conduct a survey to differentiate between client-and provider-centric interoperability solutions. We aim to provide an up-to-date analysis of the current tendencies and the neglected areas of the cloud interoperability field. Thus, we study the cloud service interoperability evolution through the years. Furthermore, we propose definitions for the intra-cloud and inter-cloud interoperability. Moreover, we propose a taxonomy to classify the cloud interoperability approaches into client-centric and provider-centric categories. Then, for each category, we classify the approaches based on their interoperability environment into single cloud or interconnected clouds. Finally, we analyze and compare the approaches based on multiple criteria. The study reveals the focus on the client-centric solutions and the interoperability in interconnected clouds. We notice more interest in the data and application levels interoperability, mainly, in infrastructure as a service model. We also find that client-centric solutions are, mostly, semantic technologies and brokers. However, provider-centric solutions are middleware, protocols, and standards. We conclude that a generic cloud service interoperability model is needed.

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Section: Cloud Service Interoperability: Definition and Environmentsmentioning

confidence: 97%

Section: Classification Of the Cloud Service Interoperability Approachesmentioning

confidence: 99%

A survey on the service interoperability in cloud computing: Client‐centric and provider‐centric perspectives

2020

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“…With the emergence of several cloud and fog service providers, it is expected that system performance and run time estimates will be used for resource selection, which make organization to move from one service provider to another to meet their needs. Predictions are a very important step towards automatic resource management [1] [2]. As the capabilities of IoT devices are limited, more attention is required when allocating tasks on these devices to assign the workload optimally on available resources.…”

Section: Related Workmentioning

confidence: 99%

Resource Selection Service Based on Neural Network in Fog Environment

Mostafa¹

2020

Adv. sci. technol. eng. syst. j.

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As an emergent technology in Internet of Things (IoT), the ultimate target of fog computing is to provide a widely distributed computational resources and data repository closer to the network edge providing heterogeneous systems both in terms of software and hardware. The fog system must have the capability to deal with huge number of resources and users at the same time, such increased size sometimes presents the issue of performance degradation. Therefore, the fog should be able to support adaptability, scalability, and extensibility to avoid such degradation by adopting efficiently and effectively an optimal resource selection and allocation model. As many fog users have limited interest in, or knowledge of fog middleware issues, it follows that in a large fog environment the best approach would be to have an automated system for resource selection and allocation. Such an approach eliminates the need for user intervention. This paper proposes a fog resource selection service based on neural network to perform resource selection tasks by coordinating with the metascheduler. Five different selection algorithm were used to evaluate the prediction model for resource selection. In addition to introducing a history update and management algorithm to manage and control the storage of the history log records.

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Application and data level interoperability on virtual machine in cloud computing environment

Cited by 2 publications

References 5 publications

A survey on the service interoperability in cloud computing: Client‐centric and provider‐centric perspectives

A survey on the service interoperability in cloud computing: Client‐centric and provider‐centric perspectives

Resource Selection Service Based on Neural Network in Fog Environment

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