Continuous advancement of cloud technologies, alongside their ever increasing stability, adoption, and ease of use, has led to a rise in native cloud applications, possibly over a larger pool of heterogeneous resources or in multi-cloud approaches. This, in turn, brought unprecedented levels of complexity in the context of cloud computing. Such complexity may cause a series of events and incidents that are difficult to be intercepted or managed on time, in a manner that also ensures the overall Quality of Services and existing Service-Level Agreements.Our special issue presents advances in several key areas that are highly relevant for automated cloud incident management: a continuous approach for reliable cloud native applications, novel approaches for Metal-as-a-Service, centered around an advanced reservation system, or development of a framework based on the concept of secure SLA, in order to deal with specific cloud security issues.

Model-driven engineering (MDE) often features quality assurance (QA) techniques to help developers creating software that meets reliability, efficiency, and safety requirements. In this paper, we consider the question of how quality-aware MDE should support data-intensive software systems. This is a difficult challenge, since existing models and QA techniques largely ignore properties of data such as volumes, velocities, or data location. Furthermore, QA requires the ability to characterize the behavior of technologies such as Hadoop/MapReduce, NoSQL, and stream-based processing, which are poorly understood from a modeling standpoint. To foster a community response to these challenges, we present the research agenda of DICE, a quality-aware MDE methodology for data-intensive cloud applications. DICE aims at developing a quality engineering tool chain offering simulation, verification, and architectural optimization for Big Data applications. We overview some key challenges involved in developing these tools and the underpinning models.

The astonishing expansion of Internet of Things has opened a lot of opportunities for related domains to employ strategies that were successfully used for the “things” governance. Furthermore, because of the technology blending in the most common household devices and wearable items, it becomes very easy for the computers to sense the surrounding environment and to collect information about the inhabitants, therefore transforming the intelligent house in a Home Care System (HCS). For medical conditions like dementia and its associated diseases, it is very convenient to monitor the patients in their living space because the patient will benefit from their home comfort. In addition, the costs for in hospital monitoring will decrease. This chapter proposes an Internet of Things Governance Architecture that can be used to sustain and monitor a complex e-health system, with application especially for patients with dementia and its associated diseases.

With the massive adoption of cloud computing, a growing number of small and medium enterprises SMEs now have the opportunity, together with the immediate goal of reducing cost, to effectively expose their services or group themselves into clusters or virtual enterprises in order to build and expose customised offerings. Different requirements, ranging from interoperability and portability, provisioning and reconfiguration, security and privacy, or monitoring and auditing, can all be controlled through the lifecycle support of cloud services and grouped under the umbrella of different virtual enterprises. A semantic support is essential to enable such an entity, and to foster collaboration among different virtual enterprises by providing specific mechanisms by which the construction of complex services from elementary building blocks can be made possible. At the same time it actively supports tasks like automation of cloud resource usage, efficient monitoring and scaling of cloud services, either individually or based on specific dependencies, or replacement and termination of services when the initial offers are no longer available.

The Semantic data is regarded as one of the best methods to describe relations between different data sources. The Semantic annotation of data improves the data aggregation methods because the relations are already defined. We have implemented a software prototype using Scala, Apache Jena and the Fuseki database server for handling a context modeled using a dynamic created ontology. For use cases we have used a hospital setting because it is the one of the most dynamic environments.

With the dawn of Cloud computing, competitors all over the world rush to take advantage of this new paradigm and the economic models it brings. Unfortunately for the earnest, cloud computing is not short of problems, most of which are being addressed through different platform-as-a-service, cloud management or governance solutions. Coming to support a natural integration between cloud management and cloud governance, CloudML and other specifications like Tosca enable a model-based approach which addresses inconsistencies found at infrastructure or platform levels thus contributing to the automation of the cloud service lifecycle.

As modern games become more and more sophisticated graphically, so does the level of artificial intelligence that animates them thus, the larger the game budget, the more work is put into improving the AI. Unfortunately many of these games feature AIs that are standalone and do not communicate with each other, they do not try to negotiate in order to improve their individual standing. The current work focuses on analysing existing game types in order to establish types of negotiation that can be achieved between AI entities. Moreover, an evolutionary approach which focuses on achieving negotiation between these entities and tackles the problem of having multiple negotiation items with discrete values is presented.

Modern video games have become an important part of AI research in the past years, largely thanks to the characteristics of their environment and the challenges they pose to AI researchers. This paper is a a survey of the current game AI state of the art and highlights important achievements in this field. An adaptive multi-agent system that can be deployed on a cloud infrastructure to solve computational constraints of advanced machine learning methods is also presented.

Cloud service abstractions are currently used to hide the underlying complexity given by existing technologies and services, in hope of facilitating the enacting of Cloud Federations and Marketplaces. In particular, resource management systems dealing with multiple Cloud providers need to expose an uniform interface for various services and to build wrappers for the Cloud service APIs. In this paper we discuss the solution adopted by a recent developed open-source and vendor agnostic platform-as-a-service for Multi-Cloud application deployment. The middleware includes a multi-agent system for automatic Cloud resource management. With a modular design, the solution provides a flexible approach to encompass new Cloud service offers as well as new resource types. This paper focuses on the modules which enable resource abstraction and automatized management.

With the ongoing advancements in Cloud Computing, new technologies and standards are being developed, ever increasing the complexity of this environment along with the associated risks, thus making it difficult for companies to take advantage of the benefits Cloud Computing brings without being exposed to them. Due to this increased complexity, there is an increased chance of a deviation from the normal operation which can lead to a reduction or interruption of quality of service, to service shortages, to an incident. Cloud Incident Management is thus an important research direction that is being addressed in this paper, which focuses on providing a complete support for the cloud incident lifecycle from its detection to its resolution. The paper covers existing solutions and standards, challenges and open issues as well as proposes an architecture for Cloud Incident Management.

The Internet of Things (IoT) is the enabler of major societal changes. As an integrated part of the Future Internet, it is based on a series of enabling technologies. In the context of the IoT, "smart things/objects" are becoming more important, active players with capabilities to communicate and interact one with the other and with IoT-enabled ecosystems. Semantic annotations, things/objects search and discovery, semantic interoperability, ontology-based semantic standards, are some of the important issues to be addressed in the context of the IoT. The focus of this paper is on analyzing existing semantic approaches in the context of the Internet of Things in order to identify potential shortcomings for the issues they address.

The video game industry is a multibillion-dollar industry in which, due to general short deadlines, game visuals as well as gameplay elements are worked in parallel up until the very last minute. This means that even if the AI system has been designed in parallel with the other game elements, once a change has been made in the late stages of the game development, the AI may prove to be inadequate to the given job.Our article covers some of the existing frameworks for game AI and proposes a multi-agent system which serves as a framework for scalable learning game AI through integration of existing machine learning techniques.

The Internet, since its inception, has been continuously evolving, creating both problems and solutions. Recent trends show that an increasing number of uniquely identifiable things (objects, sensors and devices) are making their way to this medium. A unique opportunity for integration within the already existing landscape of cloud services has been created. Further challenges related to the management and governance of these devices have been identified. The work presented in this paper addresses the issue of storing information received from different things in cloud databases in order to facilitate further exploitation in the context of cloud services.

Recent migration towards the cloud has lead to boom of initiatives that address key issues related to it. One of these issues, integrating various available cloud services while providing automated lifecycle management, was addressed by cloud governance. However, while mostly until now computers and phones were linked to the internet, new devices like tablets, sensors, smart devices (embedded devices) are now making their way into this environment. This, in turn, raises questions related to managing, governing these devices. Similarly to how cloud governance extends SOA governance, this article will detail the way in which IoT governance extends cloud governance.

As the number of existing cloud vendors rises, resource count and types are ever increasing leading to a need of cloud management solutions which facilitate easy cloud adoption. While providing several services, cloud management's primary role is resource provisioning. In order to meet application needs in terms of resources, cloud developers must carefully choose among the existing offers in order to deploy their applications. The research presented in this paper enables developers to automate the process of resource provisioning by specifying their preference towards resources and resource attributes based on which the system can propose solutions for their requirements.

With the current prevalence of Ubiquitous Computing, more specifically Internet of Things, almost every other appliance aims to exhibit smart behavior and expose it through internet connectivity, resulting in a plethora of sensors that expose environmental information online. As a direct result, large quantities of data are pouring online waiting to be processed. Our paper addresses this problem through an architecture which handles storage, analysis and processing of large amounts of data and can scale accordingly, providing a corner stone for Internet of Things.

Adaptive Game AI has been one of the key topics being researched in the field of academic game AI research. In this paper we present a comparison of several domain independent machine learning methods with the aid of which we extract expert knowledge from game logs. Each game log is represented as a feature vector that encodes cardinality and timing for player actions. We compare a wide variety of classification methods and highlight which ones are best for deployment for an adaptive game AI systems.

The adoption of Cloud Computing paradigm by the Small and Medium Enterprises allows them to associate and to create virtualized forms of enterprises or clusters that better sustain the competition with large enterprises sharing the same markets. In the same time the lack of security standards in Cloud Computing generates reluctance from the Small and Medium Enterprises in fully move their activities in the Cloud. We have proposed a Cloud Governance architecture which relies on mOSAIC project's cloud management solution called Cloud Agency, implemented as a multi-agent system. The Cloud Governance solution is based on various datastores that manage the data produced and consumed during the services lifecycle. This paper focuses on determining the requirements that must be met by the various databases that compound the most complex datastore from the proposed architecture, called Service Datastore, together with emphasizing the threats and security risks that the individual database entities must face.

The maturation of Cloud Computing technologies, cumulated with the opportunities for Small and Medium Enterprises to migrate their activities into the Cloud, have led to new marketplaces where the competition with the large enterprises can be sustained through Platform-as-a-Service solutions combined with cloud Governance. Having a Cloud Governance solution that offers support for the services lifecycle, it creates the framework for a reliable and complex persistence layer that will store the data produced and consumed during the cloud governance processes. Choosing the appropriate databases for the data stores implementations is a very important step in designing a reliable cloud governance system. This paper focuses on establishing the requirements for the subsystems composing the data stores and determining the performances of two representative database types that are candidates to be used in various data stores.