Over the time, the type of applications has evolved from batch, compute or memory intensive applications to streaming or even interactive applications. As a result, applications are getting more complex and become long-running. Such applications might require frequent-access to multiple distributed data sources. During application deployment and provisioning, the user can face various issues such as (i) where to effectively place both the data and the computation; (ii) how to achieve required objectives while reducing the overall application running cost. Data could be generated from various sources, including a multitude of devices over IoT environments that can generate a huge amount of data, while the applications are running. An application can further produce a large amount of data. In general, data of such size is usually referred to as Big Data. In general, Big Data is characterised by five properties [1, 2]. These are volume, velocity (means rapid update and propagation of data), variety

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Part 18: Optimization in Collaborative NetworksInternational audienceWith the rise of cloud computing, enterprises increasingly rely for their daily operations on heterogeneous externally-sourced cloud services that span different levels of capability. Their IT environment is thus progressively transformed into an ecosystem of intertwined infrastructure, platform, and application services. To effectively manage the ensuing complexity, enterprises are anticipated to increasingly rely on cloud service brokerage (CSB). This work presents a conceptual architecture for a framework which provides solutions with respect to the quality assurance and optimisation dimensions of CSB in the context of virtual enterprises. The framework revolves around three general themes, namely governance and quality control, failure prevention and recovery, and optimisation

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Over the last few years, the vast increase of cloud service offerings that are available from heterogeneous cloud vendors, has made the evaluation and selection of desired cloud services, a cumbersome task for service consumers. In that respect, there is an increasing need for user guidance and intermediation during the service selection process but also during the cloud service consumption that should always refer to the best possible choice based on user preferences. In this paper, we discuss the Preference-based cLoud Service Recommender (PuLSaR) that uses a holistic multi-criteria decision making (MCDM) approach for offering optimisation as a brokerage service. The specification and implementation details of this proposed software mechanism are thoroughly discussed while the background method used is summarised. Both method and brokerage service allow for the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics and dealing with their fuzziness. We cope with the fuzziness of imprecise metrics in the sense that this approach deals with linguistically expressed preferences and cloud service characteristics that lack a fixed or precise value and entail a level of vagueness which can only be captured using the Zadeh's Fuzzy Set Theory. Furthermore, this paper reports on a number of experiments that were conducted in order to measure PuLSaR's performance and scalability.

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Abstract:The valuable transformation of organizations that adopt cloud computing is indisputably accompanied by a number of security threats that should be considered. In this paper, we outline significant security challenges presented when migrating to a cloud environment and propose PaaSword -a novel holistic, data privacy and security by design, framework that aspires to alleviate them. The envisaged framework intends to maximize and fortify the trust of individual, professional and corporate users to cloud services. Specifically, PaaSword involves a context-aware security model, the necessary policies enforcement and governance mechanisms along with a physical distribution, encryption and query middleware, aimed at facilitating the implementation of secure and transparent cloud-based applications.

In emergency situations, different actors involved in first aid services should be authorized to retrieve information from the patient’s Electronic Health Records (EHRs). The research objectives of this work involve the development and implementation of methods to characterise emergency situations requiring extraordinary access to healthcare data. The aim is to implement such methods based on contextual information pertaining to specific patients and emergency situations and also leveraging personalisation aspects which enable the efficient access control on sensitive data during emergencies. The Attribute Based Access Control paradigm is used in order to grant access to EHRs based on contextual information. We introduce an ABAC approach using personalized context handlers, in which raw contextual information can be uplifted in order to recognize critical situations and grant access to healthcare data. Results indicate that context-aware ABAC is a very effective method for detecting critical situations that require emergency access to personal health records. In comparison to RBAC implementations of emergency access control to EHRs, the proposed ABAC implementation leverages contextual information pertaining to the specific patient and emergency situations. Contextual information increases the capability of ABAC to recognize critical situations and grant access to healthcare data.