Survey of Service Description Languages and Their Issues in Cloud Computing

Sun, Le; Dong, Hai; Ashraf, Jamshaid

doi:10.1109/skg.2012.49

Cited by 35 publications

(20 citation statements)

References 23 publications

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“…Most importantly, our approach is different from the work of Papazoglou and Vaquero [46] and Sun et al [50] since we give insights into the representational capabilities of recent cloud modeling approaches by demonstrating them according to a concrete cloud-based migration scenario.…”

Section: Related Surveys On Cloud Modeli11g Languagesmentioning

confidence: 93%

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Cloud Modeling Languages by Example

Bergmayr

Wimmer

Kappel

et al. 2014

2014 IEEE 7th International Conference on Service-Oriented Computing and Applications

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Abstract-Rece ntly, several proposals towards a cloud model· ing language have erneq;;cd. As they ad dress the diversity of cloud envimnments, il is not surprising that these modeling languages support different S(. 'Cfllll·ios. Using a by-example approach based on the sce nario of softw:uc migration, we demonstrate their represe ntational Cllpabilitics :md review them according to d!aracteristics common lo all modeling languages as weiJ as specific to the cloud computing donmin. We repor t on our findings an d present research guidelines for future efforts towards a better alignment of the different cloud modeling languages. I. I NTRODUCTIONCloud computing bas recently emerged as a new possibility how software can be made available to clients as a service. Hereby, software is hosted on a cloud environment [5] and consumable over the network by different clients. For software vendors, th. is is appealing because of low upfroot costs compared to a traditional on-premise solution as well as operational costs that scale with the provisioning and releasing of cloud resources, which are in turn offered as a service by cloud providers. Cloud service offerings range from low-level infrastructure elements, such as raw computing nodes, over higher level platforms, such as a Java execution environment on top of a cloud infrastructure, to ready-to-use software deployed on a cloud platform. Furthermore, software vendors are no longer forced to plan far ahead for resource provisioning [3] because the large-scale datacenters of today's cloud providers ensure that requested resources are available through their cloud environments. The risk of under-and over-provisioning of such resources is reduced by cloud environments due to their capabilities for elastic scalability on demand. Therefore, resources are not only provisioned as their demand increases but also released once their demand decreases (e.g. , number of user requests exceeds or falls below a defined threshold).Since cloud environments offer novel optimization opportunities (e.g., advanced scalable data persistence solutions), taking full advantage of the cloud requires that the software is prepared for such an environment However, in order to leverage the possibilities of a cloud environment, the software often needs to comply with certain restrictions that might hinder its functioning (e.g., statefuJ components in a highly scalable cloud environment). Several previous and ongoing European projects, notably REMICS [41] For each selected approach, we provide a brief description and then categorize it according to both modeling language characteristics and cloud computing characteristics. We do not claim that this set of cloud modeling language is complete and leave a systematic li terature review for future work. Rather, we foc us on the ex tensional perspective of the modeling languages by demonstrating the approaches in the setting of a cloud migration scenario. We have selected this scenario since it covers the wide spectrum of the software lifecycle. Finally, we c...

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Section: Related Surveys On Cloud Modeli11g Languagesmentioning

confidence: 93%

“…Sun et al [50] present a survey of service description languages that examines seven different aspects. By analyzing common modeling language characteristics and their capabilities witll respect to cloud computing, we cover all of these aspects in this article.…”

Section: Related Surveys On Cloud Modeli11g Languagesmentioning

confidence: 99%

Cloud Modeling Languages by Example

Bergmayr

Wimmer

Kappel

et al. 2014

2014 IEEE 7th International Conference on Service-Oriented Computing and Applications

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show abstract

“…Each approach either it uses a specific taxonomy or it considers the general aspect of the composition problem, without details on the focused service model (SaaS, IaaS, or PaaS) or the composition dimension. In addition, existing multicloud solutions ignore the semantic aspect, and most approaches on semantic cloud service composition have been proposed in traditional single‐cloud environments …”

Section: Related Workmentioning

confidence: 99%

Bringing semantics to multicloud service compositions

2020

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Summary Over the last decade, cloud computing has emerged as a new paradigm for delivering various on‐demand virtualized resources as services. Cloud services have inherited not only the major characteristics of web services but also their classical issues, in particular, the interoperability issues and the heterogeneous nature of their hosting environments. This latter problem must be taken into account when composing various cloud services, in order to answer users' complex requirements. Moreover, leading cloud providers started to offer their services across multiple clouds. This adds a new factor of heterogeneity, as composition engines must take into consideration the heterogeneity not only at the service level (eg, service descriptions) but also at the cloud level (eg, pricing models, security policies). In this context, the semantics of multicloud actors must be incorporated into the multicloud service composition (MCSC) process. However, most existing approaches have treated the semantic service composition in traditional single‐cloud environments. The few works in multicloud settings have ignored the semantics of cloud zones and resources. Moreover, they often focus on the general aspect of MCSC (eg, horizontal or vertical compositions). Even the few researchers who have addressed both vertical and horizontal service compositions, conducted their research studies in the context of single‐ cloud environments, which were proven to be unrealistic and offer limited quality of service (QoS) and security support. To ensure a high interoperability when composing services from multiple heterogeneous clouds and to enable a horizontal/vertical semantic service compositions, we take advantage of a standardized and semantically enriched generic service description, including all aspects (technical, operational, business, semantic, contextual) and supporting different cloud service models (SaaS, PaaS, IaaS, etc). We also incorporate Semantic Web Rule Language into the MCSC process to enable not only rule‐based reasoning about various composition constraints (eg, QoS constraints, cloud zones constraints) but also to provide accurate semantic matching of cloud services' capabilities. Conducted experiments have proven the ability of our approach to combine high‐quality services from the optimal number of clouds.

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“…For service discovery, a study by Sun et al [25] compares various service description languages in terms of what features they provide, and suitability for a cloud environment. In the area of sematic discovery, works such as [26] and [27] use an extension to OWL, the Web Ontology Language, called OWL-S, to describe web services semantically.…”

Section: Related Workmentioning

confidence: 99%

Delivering Mobile Cloud Services to the User: Description, Discovery, and Consumption

O’Sullivan

Grigoraş

2015

2015 IEEE International Conference on Mobile Services

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Access to the full text of the published version may require a subscription. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request. Rights

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Survey of Service Description Languages and Their Issues in Cloud Computing

Cited by 35 publications

References 23 publications

Cloud Modeling Languages by Example

Cloud Modeling Languages by Example

Bringing semantics to multicloud service compositions

Delivering Mobile Cloud Services to the User: Description, Discovery, and Consumption

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