Observing the clouds : a survey and taxonomy of cloud monitoring

This research was supported by a Royal Society Industry Fellowship and an Amazon Web Services (AWS) grant. Date of Acceptance: 10/12/2014Monitoring is an important aspect of designing and maintaining large-scale systems. Cloud computing presents a unique set of challenges to monitoring including: on-demand infrastructure, unprecedented scalability, rapid elasticity and performance uncertainty. There are a wide range of monitoring tools originating from cluster and high-performance computing, grid computing and enterprise computing, as well as a series of newer bespoke tools, which have been designed exclusively for cloud monitoring. These tools express a number of common elements and designs, which address the demands of cloud monitoring to various degrees. This paper performs an exhaustive survey of contemporary monitoring tools from which we derive a taxonomy, which examines how effectively existing tools and designs meet the challenges of cloud monitoring. We conclude by examining the socio-technical aspects of monitoring, and investigate the engineering challenges and practices behind implementing monitoring strategies for cloud computing.Publisher PDFPeer reviewe

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Business-driven resource allocation and management for data centres in cloud computing markets

Cloud Computing markets arise as an efficient way to allocate resources for the execution of tasks and services within a set of geographically dispersed providers from different organisations. Client applications and service providers meet in a market and negotiate for the sales of services by means of the signature of a Service Level Agreement that contains the Quality of Service terms that the Cloud provider has to guarantee by managing properly its resources. Current implementations of Cloud markets suffer from a lack of information flow between the negotiating agents, which sell the resources, and the resource managers that allocate the resources to fulfil the agreed Quality of Service. This thesis establishes an intermediate layer between the market agents and the resource managers. In consequence, agents can perform accurate negotiations by considering the status of the resources in their negotiation models, and providers can manage their resources considering both the performance and the business objectives. This thesis defines a set of policies for the negotiation and enforcement of Service Level Agreements. Such policies deal with different Business-Level Objectives: maximisation of the revenue, classification of clients, trust and reputation maximisation, and risk minimisation. This thesis demonstrates the effectiveness of such policies by means of fine-grained simulations. A pricing model may be influenced by many parameters. The weight of such parameters within the final model is not always known, or it can change as the market environment evolves. This thesis models and evaluates how the providers can self-adapt to changing environments by means of genetic algorithms. Providers that rapidly adapt to changes in the environment achieve higher revenues than providers that do not. Policies are usually conceived for the short term: they model the behaviour of the system by considering the current status and the expected immediate after their application. This thesis defines and evaluates a trust and reputation system that enforces providers to consider the impact of their decisions in the long term. The trust and reputation system expels providers and clients with dishonest behaviour, and providers that consider the impact of their reputation in their actions improve on the achievement of their Business-Level Objectives. Finally, this thesis studies the risk as the effects of the uncertainty over the expected outcomes of cloud providers. The particularities of cloud appliances as a set of interconnected resources are studied, as well as how the risk is propagated through the linked nodes. Incorporating risk models helps providers differentiate Service Level Agreements according to their risk, take preventive actions in the focus of the risk, and pricing accordingly. Applying risk management raises the fulfilment rate of the Service-Level Agreements and increases the profit of the providerPostprint (published version

ID2.1 Initial Requirements Report

This internal deliverable contains two main parts: 1. Functional Requirements for the TENCompetence integrated system (which includes the descriptions of the high level use cases and the main components of the system) 2. Non Functional Requirements for the TENCompetence inte-grated systemThe work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

A novel platform incorporating multiple forms of communication to support applications in a mobile environment

This thesis discusses the creation of a novel platform that incorporates multiple communication methods, including SMS, email and web-based technologies, for interacting with users of mobile communication devices. The platform utilises people in a mobile environment to solve a range of different application problems, where each problem is a separate and distinct scenario type with unique objectives. There are existing applications available that interact with users of mobile communication devices to provide a service, such as regular weather updates to the users. Other applications have been designed to manage and coordinate the users to perform tasks within a mobile environment, such as performing field studies for scientific purposes. However, the existing applications are designed for only one specific scenario, with the design and implementation solely focused on solving the objectives of that scenario. Each component of these applications needs to be developed from scratch in order to cater for the application s requirements. There is currently no integrated communications platform that offers a framework for supporting a range of different scenario types. The new platform, entitled the Connected-Mobile Platform, aims to support the rapid development and implementation of new scenarios. This platform is composed of a framework of generic components that enable the active running of multiple scenarios concurrently, with the ability to tailor to the requirements of new scenarios as they arise via a structured process. The platform facilitates a means to coordinate its users in order to tackle the objectives of a scenario. The thesis investigates several system architectures to determine an appropriate architectural design for constructing the proposed platform. The platform has a generic framework, based on a client-server architecture, to facilitate the inclusion of a multitude of scenarios. A scenario represents a problem or an event, whereby the platform can utilise and interact with users of mobile communication devices to attempt to solve the objectives of the scenario. Three mobile communication methods are supported; the Short Message Service, electronic mail and web-forms via the mobile internet. Users are able to select and switch between the different methods. The thesis describes the platform s tailored communication structure for scenarios and autonomous analysis of messages. The thesis discusses case studies of two different scenarios to evaluate the platform s facilities for rapid scenario development. The Diet Diary scenario, which is for individual users, aims to manage a user s daily calorie intake to help them reach their desired weight goal. The focus is on the platform s functionality for analysing and responding to messages autonomously. The Missing Persons scenario, which utilises multiple users, involves tracking and locating people who have been reported missing. The focus is on the platform s functionality for coordinating the multiple users, through the creation of assignments, in order to distribute the scenario objectives. The thesis concludes by highlighting the novel features of the platform and identifying opportunities for future work

Economics of Human Resources Development under Globalisation

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