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Currently, the data to be explored and exploited by computing systems increases at an exponential rate. The massive amount of data or so-called “Big Data” put pressure on existing technologies for providing scalable, fast and efficient support. Recent applications and the current user support from multi-domain computing, assisted in migrating from data-centric to knowledge-centric computing. However, it remains a challenge to optimally store and place or migrate such huge data sets across data centers (DCs). In particular, due to the frequent change of application and DC behaviour (i.e., resources or latencies), data access or usage patterns need to be analyzed as well. Primarily, the main objective is to find a better data storage location that improves the overall data placement cost as well as the application performance (such as throughput). In this survey paper, we are providing a state of the art overview of Cloud-centric Big Data placement together with the data storage methodologies. It is an attempt to highlight the actual correlation between these two in terms of better supporting Big Data management. Our focus is on management aspects which are seen under the prism of non-functional properties. In the end, the readers can appreciate the deep analysis of respective technologies related to the management of Big Data and be guided towards their selection in the context of satisfying their non-functional application requirements. Furthermore, challenges are supplied highlighting the current gaps in Big Data management marking down the way it needs to evolve in the near future.

Background As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient’s health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party. Methods A contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient’s recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient’s health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient’s sensitive information in the blockchain network. Results The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient’s sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals. Conclusions The proposed mechanism informs proactively the emergency team of professional clinicians about patients’ critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users’ trust to the access control mechanism.

Cloud computing and relevant emerging technologies have presented ordinary methods for processing edge-produced data in a centralized manner. Presently, there is a tendency to offload processing tasks as close to the edge as possible to reduce the costs and network bandwidth used. In this direction, we find efforts that materialize this paradigm by introducing distributed deep learning methods and the so-called Federated Learning (FL). Such distributed architectures are valuable assets in terms of efficiently managing resources and eliciting predictions that can be used for proactive adaptation of distributed applications. In this work, we focus on deep learning local loss functions in multi-cloud environments. We introduce the MulticloudFL system that enhances the forecasting accuracy, in dynamic settings, by applying two new methods that enhance the prediction accuracy in applications and resources monitoring metrics. The proposed algorithm’s performance is evaluated via various experiments that confirm the quality and benefits of the MulticloudFL system, as it improves the prediction accuracy on time-series data while reducing the bandwidth requirements and privacy risks during the training process.

While a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The feasibility of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.

The rapid growth of new computing models that exploit the cloud continuum has a big impact on the adoption of microservices, especially in dynamic environments where the amount of workload varies over time or when Internet of Things (IoT) devices dynamically change their geographic location. In order to exploit the true potential of cloud continuum computing applications, it is essential to use a comprehensive set of various intricate technologies together. This complex blend of technologies currently raises data interoperability problems in such modern computing frameworks. Therefore, a semantic model is required to unambiguously specify notions of various concepts employed in cloud applications. The goal of the present paper is therefore twofold: (i) offering a new model, which allows an easier understanding of microservices within adaptive fog computing frameworks, and (ii) presenting the latest open standards and tools which are now widely used to implement each class defined in our proposed model.

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.

The emergence of fog and edge computing has complemented cloud computing in the design of pervasive, computing-intensive applications. The proximity of fog resources to data sources has contributed to minimizing network operating expenditure and has permitted latency-aware processing. Furthermore, novel approaches such as serverless computing change the structure of applications and challenge the monopoly of traditional Virtual Machine (VM)-based applications. However, the efforts directed to the modeling of cloud applications have not yet evolved to exploit these breakthroughs and handle the whole application lifecycle efficiently. In this work, we present a set of Topology and Orchestration Specification for Cloud Applications (TOSCA) extensions to model applications relying on any combination of the aforementioned technologies. Our approach features a design-time “type-level” flavor and a run time “instance-level” flavor. The introduction of semantic enhancements and the use of two TOSCA flavors enables the optimization of a candidate topology before its deployment. The optimization modeling is achieved using a set of constraints, requirements, and criteria independent from the underlying hosting infrastructure (i.e., clouds, multi-clouds, edge devices). Furthermore, we discuss the advantages of such an approach in comparison to other notable cloud application deployment approaches and provide directions for future research.

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

Enterprises increasingly recognize the compelling economic and operational benefits from virtualizing and pooling IT resources in the cloud. Nevertheless, the significant and valuable transformation of organizations that adopt cloud computing is 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 framework that aspires to alleviate these challenges. Specifically, the proposed framework involves a context-aware security model, the necessary policies enforcement mechanism along with a physical distribution, encryption and query middleware.