Reinforcement learning is playing a crucial role in sustainable developments and enabling wide applications in our daily lives ranging from self-driving cars, robotics, automation, personalised product suggestions, user notifications and many more. Kernel-based reinforcement learning holds theoretical guarantees of convergence to a unique solution for decision function approximation. This has motivated the development of implementations that exploit the advantage of kernel methods whilst overcoming the ‘curse of dimensionality’. The theoretical properties of kernel-based methods also support the combination of learning agents in loosely coupled systems into a single combined model for composable learning. Learning speedup is achievable by introducing expert knowledge to the algorithm through the kernel, which reduces variance by increasing bias and thus learning speed. Despite the theoretical promise of kernel based reinforcement learning methods, and continued evolution of more efficient algorithms, practical implementation are not prevalent. Research in quantum machine learning has successfully applied quantum algorithms to achieve computational speed-ups for various machine-learning tasks, and has also identified potential quantum advantage through the identification of patterns not accessible to classical data mapping. To date, this has not been applied to kernel based reinforcement learning. Recent work in the field of quantum learning suggests an important duality between quantum machine learning and kernel machine learning methods. Therefore, this paper proposes a framework to complement kernel-based reinforcement learning with quantum generated feature maps for function approximation to improve learning speed. This has positive consequences for optimisation of reinforcement learning techniques already in use to support sustainability targets in applications such as industry innovation and infrastructure, power generation management, data centre cooling and self-driving cars. Our established framework enables the adoption of emerging quantum kernel methods for reinforcement learning applications. The achieved results show over 20 % improvement and positive impacts of quantum map selection on model performance for different problems.

In line with cloud computing emergence as the dominant enterprise computing paradigm, our conceptualization of the cloud computing reference architecture and service construction has also evolved. For example, to address the need for cost reduction and rapid provisioning, virtualization has moved beyond hardware to containers. More recently, serverless computing or Function-as-a-Service has been presented as a means to introduce further cost-efficiencies, reduce configuration and management overheads, and rapidly increase an application's ability to speed up, scale up and scale down in the cloud. The potential of this new computation model is reflected in the introduction of serverless computing platforms by the main hyperscale cloud service providers. This paper provides an overview and multi-level feature analysis of seven enterprise serverless computing platforms. It reviews extant research on these platforms and identifies the emergence of AWS Lambda as a de facto base platform for research on enterprise serverless cloud computing. The paper concludes with a summary of avenues for further research.

Cloud computing is rapidly changing the digital service landscape. A proliferation of Cloud providers has emerged, increasing the difficulty of consumer decisions. Trust issues have been identified as a factor holding back Cloud adoption. The risks and challenges inherent in the adoption of Cloud services are well recognised in the computing literature. In conjunction with these risks, the relative novelty of the online environment as a context for the provision of business services can increase consumer perceptions of uncertainty. This uncertainty is worsened in a Cloud context due to the lack of transparency, from the consumer perspective, into the service types, operational conditions and the quality of service offered by the diverse providers. Previous approaches failed to provide an appropriate medium for communicating trust and trustworthiness in Clouds. A new strategy is required to improve consumer confidence and trust in Cloud providers. This paper presents the operationalisation of a trust label system designed to communicate trust and trustworthiness in Cloud services. We describe the technical details and implementation of the trust label components. Based on a use case scenario, an initial evaluation was carried out to test its operations and its usefulness for increasing consumer trust in Cloud services.

Anomaly detection in Cloud service provisioning platforms is of significant importance, as the presence of anomalies indicates a deviation from normal behaviour, and in turn places the reliability of the distributed Cloud network into question. Existing solutions lack a multi-level approach to anomaly detection in Clouds. This paper presents a wavelet-inspired anomaly detection framework for detecting anomalous behaviours across Cloud layers. It records the evolution of multiple metrics and extracts a two-dimensional spectrogram representing a monitored system’s behaviour. Over two weeks of historical monitoring data were used to train the system to identify healthy behaviour. Anomalies are then characterised as deviations from this expected behaviour. The training technique as well as the pre-processing techniques are highly configurable. Based on a Cloud service deployment use case scenario, the effectiveness of the framework was evaluated by randomly injecting anomalies into the recorded metric data and performing comparison using the resulting spectrograms.

A data location control model for Cloud services is presented that uses an authorization system as its core control element. The model is intended for use by enterprises that collect personal data from end users that can potentially be stored and processed at multiple geographic locations. By adhering to the model's authorization decisions, the enterprise can address end users' concerns about the location of their data by incorporating the their preferences about the location of their personal data into authorization policy. The model also ensures that the end users have visibility into the location of their data and are informed when the location of their data changes. A prototype of the model has been implemented that provides the data owner with an interface that allows their location preferences to be expressed. These preferences are stored internally as XACML policy documents. Thereafter, movements or remote duplications of the data must be authorized by submitting requests to an ISO/IEC 10181-3:1996 compliant Policy Enforcement Point. End users can, at any time, view up-to-date information on the locations where their data is stored via a web interface. Furthermore, XACML obligations are used to ensure that end users are informed whenever the location of their data changes.

SUMMARY Cloud computing represents a novel on‐demand computing technology where resources are provisioned in compliance to a set of predefined non‐functional properties specified and negotiated by means of service level agreements (SLAs). Currently, cloud providers strive to achieve efficient SLA enforcement strategies to avoid costly SLA violations during application provisioning and to timely react to failures and environmental changes. These strategies include advanced application deployment mechanisms and appropriate resource monitoring concepts. In terms of cloud resource monitoring, providers tend to adopt existing monitoring tools, such as those from grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low‐level resource metrics (e.g., system uptime and downtime) to high‐level application‐specific SLA parameters (e.g., system availability). In this paper, we present a novel low‐level metrics to high‐level SLA (LoM2HiS) framework for managing the monitoring of low‐level resource metrics and mapping them to high‐level SLAs and an application deployment mechanism for scheduling and provisioning applications in clouds. The LoM2HiS framework provides the application deployment mechanism with monitored information and SLA violation prevention techniques, thereby ensuring the performance of the applications and thus increasing the revenue of the cloud provider by avoiding SLA violation penalty cost. This framework is the building block of the Foundations of Self‐governing ICT Infrastructures project, which intends to facilitate autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the knowledge component to act so as to avert the threats. We discuss in detail the conceptual design of the LoM2HiS framework and the application deployment mechanism including their implementations. Finally, we present our evaluation results based on a use‐case scenario demonstrating the usage of the LoM2HiS framework in a real cloud environment. Copyright © 2012 John Wiley & Sons, Ltd.

Cloud resources and services are offered based on Service Level Agreements (SLAs) that state usage terms and penalties in case of violations. Although, there is a large body of work in the area of SLA provisioning and monitoring at infrastructure and platform layers, SLAs are usually assumed to be guaranteed at the application layer. However, application monitoring is a challenging task due to monitored metrics of the platform or infrastructure layer that cannot be easily mapped to the required metrics at the application layer. Sophisticated SLA monitoring among those layers to avoid costly SLA penalties and maximize the provider profit is still an open research challenge. This paper proposes an application monitoring architecture named CASViD, which stands for Cloud Application SLA Violation Detection architecture. CASViD architecture monitors and detects SLA violations at the application layer, and includes tools for resource allocation, scheduling, and deployment. Different from most of the existing monitoring architectures, CASViD focuses on application level monitoring, which is relevant when multiple customers share the same resources in a Cloud environment. We evaluate our architecture in a real Cloud testbed using applications that exhibit heterogeneous behaviors in order to investigate the effective measurement intervals for efficient monitoring of different application types. The achieved results show that our architecture, with low intrusion level, is able to monitor, detect SLA violations, and suggest effective measurement intervals for various workloads.

Cloud computing is the result of the convergence of several concepts, ranging from virtualization, distributed application design, Grid computing, and enterprise IT management. Efficient management of Cloud computing infrastructures faces with the contradicting goals like unlimited scalability, provision of Service Level Agreements (SLAs), extensive use of virtualization, energy efficiency and minimization of the administration overhead by humans. Thus, autonomic computing seems to be one of the promising paradigms for the implementation of the management infrastructures for Clouds. However, currently available autonomic systems do not consider the characteristics of Clouds, e.g., virtualization layer, and thus are not easily applicable to Cloud infrastructures. In this paper we discuss first steps towards revealing the current MAPE (Monitoring, Analysis, Planning, Execution) loops for the application to Cloud infrastructures. We present novel techniques for the adequate monitoring of Clouds, discuss the approach for the knowledge management and present our solutions for facilitating SLA generation and management.

Cloud computing represents a promising computing paradigm where resources have to be dynamically allocated to software that needs to be executed. Self-manageable Cloud infrastructures are required to achieve that level of flexibility on the one hand, and to comply to users' requirements specified by means of Service Level Agreements (SLAs) on the other. Such infrastructures should automatically respond to changing component, workload, and environmental conditions minimizing user interactions with the system and preventing violations of SLAs. However, identification of system states where reactive actions are necessary for the prevention of SLA violations is far from trivial. In this paper we investigate how current knowledge management systems can be used for the prevention of SLA violations in Clouds. First, we define a typical SLA use case and formulate the expected behavior of the knowledge management system in order to prevent possible SLA violations. Second, we investigate different methods for knowledge management, e.g., situation calculus and case based reasoning (CBR). We discuss how these methods match the expected behavior for SLA violation prevention. In particular we examine the CBR method and devise several approaches for knowledge management in Clouds based on CBR. Finally, we evaluate our approach based on the presented use case.

Admission control mechanisms are crucial for an efficient resource management and fulfillment of service level agreements. Common existing techniques perform their admission decision function for new requests, without taking into consideration the priority or guarantee commitments of particular service classes. Other multi-class based approaches are usually designed for packet-oriented differentiated service networks. This paper introduces a framework for multi-class and dynamic flow-aware admission control (MAC), which performs its admission decision by considering the class priority to which the flow requests belong and the particular resource capacity proportion allocated per-class. The main characteristic of the framework is that it performs measurements required for the load estimation and applies an admission criteria on a per-class basis, with regard to the available class-based capacity, for which a dynamic resource allocation mechanism is implemented as well.