Data contracts for cloud-based data marketplaces

Truong, Hong-Linh; Comerio, Marco; Paoli, Flavio De; Gangadharan, G. R.; Dustdar, Schahram

doi:10.1504/ijcse.2012.049749

Cited by 23 publications

(11 citation statements)

References 28 publications

(31 reference statements)

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“…Indeed, we find several works [19], [20], [21] [22] [23], [24] enabling an application to negotiate its Data Management Contract (DMC), often referred to as data agreement or data license, with various clouds and to bind to the specific DBMSs according to its DMC. Truong et al [19], [20], [21] propose to model and specify data concerns in data contracts to support concern-aware data selection and utilization. For this purpose, they define an abstract model to specify a data contract and the main data contract terms.…”

Section: Related Workmentioning

confidence: 99%

Supporting Multi Data Stores Applications in Cloud Environments

Sellami

Bhiri

Defude

2016

IEEE Trans. Serv. Comput.

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International audienceThe production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data store applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of cloud discovery, and application deployment and execution. In this paper we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unifying data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. Based on that, they express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant cloud environment and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and is currently used to implement use cases from the OpenPaaS projec

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Section: Related Workmentioning

confidence: 99%

Supporting Multi Data Stores Applications in Cloud Environments

Sellami

Bhiri

Defude

2016

IEEE Trans. Serv. Comput.

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“…Patel et al (2009) describe how a Web service legal agreement framework, developed in the context of service oriented architecture, can be applied to cloud SLAs. Truong et al (2012) describe how to design contracts and exchanges for sharing and using data among cloud providers and consumers. Alhamad et al (2010) describe nonfunctional requirements that cloud consumers need to consider when negotiating SLA with a cloud provider.…”

Section: Standardization Of Slasmentioning

confidence: 99%

Cloud Service Level Agreement

Baset¹

2016

Encyclopedia of Cloud Computing

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Cloud-based services are becoming commonplace. Examples of these services include on-demand creation of virtual machines (VMs), backup of user data, and rapid deployment and automatic scaling of applications. These services are typically offered under three different service models: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) (Mell and Grance, 2011). Cloud providers, large and small, offer these services to individuals, small and medium businesses, and large enterprises. Each service is typically accompanied by a service-level agreement (SLA), which defines the service guarantees that a provider offers to its users.While some cloud service deployment models are better understood than others (e.g., IaaS is arguably the most understood service model at the time of writing of this chapter), by and large cloud services are rapidly evolving and hence not standardized. As a consequence, the services offered by providers vary. Even for services deemed similar across providers, the SLAs can vary considerably. Service-level agreements also serve as a marketing tactic to attract new customers, either by bundling together different services, or by offering attractive guarantees with small print that may not ultimately benefit a cloud consumer. Such groupings of SLAs make comparison across cloud providers even more difficult. Moreover, a cloud provider may offer a canned SLA to all its consumers, irrespective of their requirements or size. An entity running mission-critical applications on a cloud would likely require a different SLA from an entity that is just beginning to evaluate the use of a cloud-based service.In this chapter, we briefly explain the entities involved in cloud-based services (section 36.1.1). We describe elements of an SLA of a cloud-based service with detailed examples, which can help a cloud consumer in comparing SLAs of cloud-based services. We then give an overview of SLAs of two well known public cloud providers, namely, Amazon and Rackspace (section 36.3). Finally, we discuss future directions in cloud SLAs (section 36.4).

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“…Data in big data analytics in our view will be provided, managed and shared by different providers but based on different data concerns (e.g, different quality of data, privacy, data retention, licensing, and data contract) [10]. The data concerns are crucial that must be designed together with data collection, summarization, exchange and analytics.…”

Section: Data Resource Elasticitymentioning

confidence: 99%

Principles of Software-Defined Elastic Systems for Big Data Analytics

Truong

Dustdar

2014

2014 IEEE International Conference on Cloud Engineering

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Techniques for big data analytics should support principles of elasticity that are inherent in types of data and data resources being analyzed, computational models and computing units used for analyzing data, and the quality of results expected from the consumer. In this paper, we analyze and present these principles and their consequences for software-defined environments to support data analytics. We will conceptualize software-defined elastic systems for data analytics and present a case study in smart city management, urban mobility and energy systems with our elasticity supports.

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Data contracts for cloud-based data marketplaces

Cited by 23 publications

References 28 publications

Supporting Multi Data Stores Applications in Cloud Environments

Supporting Multi Data Stores Applications in Cloud Environments

Cloud Service Level Agreement

Principles of Software-Defined Elastic Systems for Big Data Analytics

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