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Load pattern categorization plays a significant role in enhancing the understanding of demand characteristics of different cohorts of energy customers on a distribution network. For a distribution network with embedded photovoltaic (PV) systems, it is also desirable to enumerate these premises since unreported PV installations can have a detrimental impact on voltage control, frequency regulation, and incur reverse power flows. In this paper, a holistic smart meter data analytics approach is proposed for classifying and characterizing the intra-day load curves of PV and non-PV households. Unlike existing studies based on raw time-series data, a series of interpretable and discriminating global and peak-period features are first developed to extract the physical information of load patterns. A two-step feature-based clustering is then applied to classify the load profiles of PV and non-PV households. A post-clustering quantitative compositional analysis is developed to characterize the energy use variability and compositional changes for each household. The proposed PV household identification method allows network owners to identify the existence of PV installations in a particular area for voltage control and ancillary service provision. In the longer term, effective load pattern categorization and post-clustering characterization can help better understand demand characteristics of different cohorts of customers, network utilization, and network-level changes in load growth.

With the deployments of fifth generation mobile networks (5G), rapid development of mobile internet, continued growth in mobile traffic and increased adoption of the internet of things, Multi-access Edge Computing (MEC) remains an inevitable critical future radio access network artefact for an optimized network. Network operators as part of their optimization exercise are also adopting Cloud Radio Access Network (C-RAN) to further improve network operation performance. Combining MEC and C-RAN makes the economics (OPEX + CAPEX) of both technologies more attractive and also enables network operators to support key 5G applications. This research investigates a major challenge of combining MEC + C-RAN: how to efficiently orchestrate cloud resources as a user moves within the edge environment without compromising the overall quality of service. This ongoing research adopts a machine learning technique in orchestrating resources efficiently in order for applications and services to adhere to stringent performance requirements even at geographically dispersed "edge" locations.

Predicting the electricity consumption of proposed new supermarkets is helpful to design and plan future energy management. Instead of creating complex site-specific thermal engineering models, data-driven energy prediction models can be useful to energy managers. We have designed and implemented a data-driven method to predict the future ’electricity daily load profile’ (EDLP) of new supermarkets using historical EDLPs of existing supermarkets of the same type. The supermarket features used for the prediction are 10 types of floor areas divided by usage ( m 2 ) and its location. Four data-driven regression models are used and compared to predict EDLPs: Artificial Neural Networks, Support Vector Machines, k-Nearest Neighbours and OLS. Prediction computational experiments were performed over 1-h electricity readings of 213 UK supermarkets gathered during six years. Prediction error mainly varies between 12 and 20% depending on method, year, supermarket type, and division of the data (season or temperature intervals). EDLPs computed over warm periods are better predicted than over cold periods and supermarkets only with electricity are better predicted than supermarkets with electricity and gas. The three features with more weight in the prediction are Food, Chilled produce and Cafeteria areas. The limitations of machine learning methods to solve this problem are discussed.

This paper provides a brief introduction into emerging user requirements and expectations in the context of large cloud-based infrastructures. Observed cloud usage patterns are used to demonstrate the need for large federated cloud infrastructures, in particular cloud federations bridging the gap between private cloud infrastructures and large commercial public cloud providers. The paper proposes the use of open standards as the best possible way to achieve a working user-friendly large-scale cloud federation and discusses the challenges in assembling and managing such a federation whilst focusing on the differences between private-private and public-private cloud federations in key areas of virtual machine management and authentication, authorization, and identity management.

As the Industrial Internet of Things (IIoT) grows, systems are increasingly being monitored by arrays of sensors returning time-series data at ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An obvious use for these data is real-time systems condition monitoring and prognostic time to failure analysis (remaining useful life, RUL). (e.g. See white papers by Senseye.io, and output of the NASA Prognostics Center of Excellence (PCoE).) However, as noted by Agrawal and Choudhary 'Our ability to collect "big data" has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery.' In order to fully utilize the potential of Industrial Big Data we need data-driven techniques that operate at scales that process models cannot. Here we present a prototype technique for data-driven anomaly detection to operate at industrial scale. The method generalizes to application with almost any multivariate dataset based on independent ordinations of repeated (bootstrapped) partitions of the dataset and inspection of the joint distribution of ordinal distances., Comment: 9 pages; 11 figures

Effectively establishing trust in Cloud Computing is a critical requirement for achieving wider adoption of hybrid and public cloud. Although a number of Trusted Cloud concepts have been proposed, they suffer from limitations in resilience, scalability and dynamism. We tackle these limitations with the creation of a distributed attestation service, Porridge. Porridge achieves resiliency, as multiple attestation workers are employed and redundant workers assigned for attesting each Virtual Machine (VM); scalability, as the attestation load and responsibility is automatically distributed evenly among workers; adaptivity to cloud dynamism, as each VM’s virtual Trusted Platform Module (vTPM) is mapped to a stable set of physical Trusted Platform Modules (TPM) in the host and then the workers TPMs. Overall the attestation scheme enables flexible vTPM-TPM bindings while hiding details of cloud infrastructure, with the root-of-trust for the VM not bound to its underlying host’s TPM, but to its managing workers. This concept can be extended to support more advanced cloud security through the introduction of Trusted Service Providers providing Cloud Attestation as a Service (CAaaS).

Climateprediction.net has traditionally been an activity that requires a large amount of computing resources from its volunteer network, whilst allowing a time-frame of weeks to months for simulations to be returned for each project. However, there is an increasing trend of projects requiring results in shorter and shorter timescales. Under no project is this clearer than in the World Weather Attribution (WWA) initiative, where we are aiming to provide in near to real-time an answer to how anthropogenic climate change has altered the frequency of occurrence of a particular type of extreme weather event, either as it happens or as soon after as is practical. As such we need the ability to run simulations on alternate resources when volunteer resources will not provide results within the necessary timeframe. This paper describes a workflow to distribute ensembles of climateprediction.net simulations in the Amazon Elastic Compute Cloud, to provide urgent compute capability for projects such as WWA. We propose a method of optimizing the use of cloud resources to minimize cost while maximising throughput. A case study is presented to provide a proof of concept of this methodology. As such, this is a clear example of beneficial utilisation of cloud resources to supplement those available through our volunteer community.

Grid computing enables the access of heterogeneous computational resources across a dynamic set of physical organisations seamlessly. A campus grid is an example of the grid solution which is located within a single administrative domain. In the majority of installations these enable connection of all institutionally owned computational resources, making them transparently available to appropriately registered members of staff. In this paper we introduce the design and implementation of a lightweight campus grid framework. It manages a set of heterogeneous computational resources, using virtual organisation based policies for each individual grid resource and enables user's to transparently access resources through a unified user-friendly interface. The proposed framework can discover, aggregate and broker all of these heterogeneous resources with low management costs that matches users’ requirements. With this framework, a campus grid enables university-wide, regional-wide, national and international grid resources become transparently available to university members through their university account without knowing how to access each individual resource. Such a framework allows scalable grid resource sharing, virtual organisation based resource management, dynamical and autonomous resource brokering with improved usability and manageability in a campus grid environment.

Cloud computing has been increasingly adopted by users and providers to promote a flexible, scalable and tailored access to computing resources. Nonetheless, the consolidation of this paradigm has uncovered some of its limitations. Initially devised by corporations with direct control over large amounts of computational resources, cloud computing is now being endorsed by organizations with limited resources or with a more articulated, less direct control over these resources. The challenge for these organizations is to leverage the benefits of cloud computing while dealing with limited and often widely distributed computing resources. This study focuses on the adoption of cloud computing by higher education institutions and addresses two main issues: flexible and on-demand access to a large amount of storage resources, and scalability across a heterogeneous set of cloud infrastructures. The proposed solutions leverage a federated approach to cloud resources in which users access multiple and largely independent cloud infrastructures through a highly customizable broker layer. This approach allows for a uniform authentication and authorization infrastructure, a fine-grained policy specification and the aggregation of accounting and monitoring. Within a loosely coupled federation of cloud infrastructures, users can access vast amount of data without copying them across cloud infrastructures and can scale their resource provisions when the local cloud resources become insufficient.

The cloud computing ecosystem is in urgent need of effective and practical trust establishment schemes. Cloud customers currently lack approaches to effectively verify the genuine behaviours of cloud services. They can only blindly believe that the Cloud Service Providers (CSPs) are honest enough to not tamper with their data, while many others have avoided using the cloud entirely. Trust establishment schemes, such as cloud auditing and cloud attestation systems, lack controls and transparency over their trust building processes, which only blur the effectiveness of the proclaimed trustworthiness. We argue that these problems ultimately result from the CSPs' autocratic governance over all the activities inside the cloud. In this paper, we present a Separation-of-Powers (SoP) model by referencing the similar concepts from the discipline of the political philosophy. We define three independent roles to separate the powers of definition, enforcement, and inspection from the CSPs. These roles form the collaborative-restrictive relationship to facilitate trustworthy cloud services and achieve the balance-of-powers. We believe a model of this kind will open new opportunities for achieving trustworthy and open cloud ecosystem governance.

Within the Environmental 'omics community Bio-Linux is a widely used tool. This has the advantage of providing in a single deliverable package all necessary software and tools to support common analyses. With the growth in data volumes within the community and increasing constraints on user access and control over their own desktops an alternative delivery method of Bio-Linux and, in future, the Docker container environment is necessary. Within the EOS Cloud project we have constructed a Desktop as a Service system to centrally host virtual machines with these tools preconfigured and maintained. To enable efficient use of the resources we have enabled user controlled resource scaling so that users are able to utilise small scale VMs for task configuration and data manipulation and boost to a larger scale to run analysis applications all the while maintaining the user environment in a consistent manner. Alongside this within the project we have been developed tools to simplify the increasingly popular Docker software usage model. This includes ensure uniformity of behaviour between the host system and the running Docker container. Within the invitation only trial user community we identify two different exemplars groups and explain their usage and how the products and services developed within the project are useful for them. We conclude discussing the useful nature of Desktop as a Service, how it is of great benefit to the bioinformatics community but could also be of great use elsewhere, where the need for a stable user environment with applications already available that do not rely on local ICT support.

This article has been made available through the Brunel Open Access Publishing Fund. The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called 'Chinese restaurant process' method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships.

Cloud Computing provides an infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model. It is useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is especially important due to the time and financial constraints of many user communities. The typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this poster, a use case is presented illustrating how the integration of Trusted Computing technologies into an available Infrastructure as a Service (IaaS) platform -- Eucalyptus -- allows the security-critical energy industry to exploit the flexibility and potential economic benefits of Cloud Computing for their business-critical applications.

Copyright @ 2012 IEEE Cloud Computing provides an optimal infrastructure to utilise and share both computational and data resources whilst allowing a pay-per-use model, useful to cost-effectively manage hardware investment or to maximise its utilisation. Cloud Computing also offers transitory access to scalable amounts of computational resources, something that is particularly important due to the time and financial constraints of many user communities. The growing number of communities that are adopting large public cloud resources such as Amazon Web Services [1] or Microsoft Azure [2] proves the success and hence usefulness of the Cloud Computing paradigm. Nonetheless, the typical use cases for public clouds involve non-business critical applications, particularly where issues around security of utilization of applications or deposited data within shared public services are binding requisites. In this paper, a use case is presented illustrating how the integration of Trusted Computing technologies into an available cloud infrastructure - Eucalyptus - allows the security-critical energy industry to exploit the flexibility and potential economical benefits of the Cloud Computing paradigm for their business-critical applications.

Linking up two projects that are dedicated to facilitate the work of documentary scholars, this paper presents image processing algorithms tailored to the study of ancient documents and how they have been made available to the users through a portal that calls upon a web-service exploiting grid computational power. To that end, image processing algorithms were wrapped to fit into the National Grid Service (NGS) Uniform Execution Environment; the data model of an existing Virtual Research Environment (VRE-SDM) was extended; JSR-168 compliant portlets were developed to facilitate secure and seamless distributed image analysis; and a GridSAM interface between the portal and the NGS-installed algorithms was developed. The outcomes of the project include: a web-based application, a proof of concept for the usability of the VRE-SDM platform, an opportunity for wider dissemination for the image processing algorithms, and a proof of feasibility for the use of the NGS for Humanities applications.

The National Grid Service (NGS) provides access to compute and data resources for UK academics. Currently users are required to have an X.509 certificate from the UK e-Science Certification Authority (CA) or one of its international peers to access the NGS. The CA must satisfy the requirements for internationally agreed assurance levels and some users find the processes of obtaining and managing certificates difficult. Shibboleth, an implementation of federation identity-based authentication, has been widely deployed in academic environments in the UK. The SARoNGS project, was proposed to integrate the Shibboleth and X.509 based infrastructures, to deliver a production level service for accessing the NGS in a user-friendly way. This paper describes an architecture by which users are authenticated by the UK Access Management Federation to acquire low assurance credentials to access Grid resources on the NGS. Users can login to NGS resources via NGS Portal, using their local institution’s authentication system.

The Condor matchmaker provides a powerful mechanism for matching together both users job requirements and resource providers requirements in such a manner that not only is a paring selected which satisfies both requirements but is optimal for both. This has made the Condor system a good choice for use as a meta-scheduler within the Grid. Integrating Condor within a wider Grid context is possible but only through modification to the Condor source code. Here we describe how the standards for job submission and resource descriptions can be integrated into the Condor system to allow arbitrary Grid resources which support these standards to be brokered through Condor.

With the increasing cost of providing computing and storage facilities for research capability, it has become important that universities make increasingly efficient use of their existing resources. The strategy developed at the University of Bristol has involved the design and construction of a campus grid. This has enabled better utilization of spare capacity on available systems connected to the grid and also the separation, for the first time, of those applications that require parallel compute resources (i.e. closely coupled systems) and those that require serial resources. The overall implementation of this strategy was driven by two prime considerations. Firstly, the contributing cluster systems are, in the main, very heavily used and connecting them onto the grid must be done in a non-invasive way to ensure currently executing applications don't have to be interrupted or any other cluster operations compromised. Secondly, the grid has been considered by many scientific users to be over hyped. This implied that we needed to get a system up and running quickly however 'simple' it was to prove the concept was viable. It was hoped that involving real users at an early stage of the grid's construction would lead to them evangelising about the system and thus, encourage more users to use the system.