Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.

Cloud computing is emerging as a viable platform for scientific exploration. Elastic and on-demand access to resources (and other services), the abstraction of “unlimited” resources, and attractive pricing models provide incentives for scientists to move their workflows into clouds. Generalizing these concepts beyond a single virtualized datacenter, it is possible to create federated marketplaces where different types of resources (e.g., clouds, HPC grids, supercomputers) that may be geographically distributed, are collectively exposed as a single elastic infrastructure. This presents opportunities for optimizing the execution of application workflows with heterogeneous and dynamic requirements, and tackling larger scale problems. In this paper, we introduce a framework to manage the end-to-end execution of data-intensive application workflows in dynamic software-defined resource federation. This framework enables the autonomic execution of workflows by elastically provisioning an appropriate set of resources that meet application requirements, and by adapting this set of resources at runtime as the requirements change. It also allows users to customize scheduling policies that drive the way resources federated and used. To demonstrate the benefits of our approach, we study the execution of two different data-intensive scientific workflows in a multi-cloud federation using different policies and objective functions.

Service-based access models coupled with recent advances in application deployment technologies can support emerging dynamic and data-driven applications. However, due to evolving application requirements and the dynamicity of the underlying resources, it is necessary to support flexible and opportunistic composition of services in order to satisfy application needs. The goal of this work is to provide a programmable and dynamic framework that can support these applications. The framework uses software-defined environment concepts to drive the process of dynamically composing infrastructure services from multiple providers. The resulting distributed software-defined environment autonomously evolves over the application life cycle while meeting objectives and constraints set by the users, applications, and/or resource providers. We present two different approaches for programming resources and controlling the composition process, one that is based on a rule engine and another that leverages constraint programming. Preliminary results demonstrate the framework operation and performance using simulations and real experiments running Docker containers across multiple clouds.

Service-based access models coupled with emerging application deployment technologies are enabling opportunities for realizing highly customized software-defined environments, which can support dynamic and data-driven applications. However, this requires rethinking traditional resource federation models to support dynamic resource compositions, which can adapt to evolving application needs and the dynamic state of underlying resources. In this paper, we present a programmable approach that leverages software-defined techniques to create a dynamic space-time infrastructure service composition. We propose the use of Constraint Programming as a formal language to allow users, applications, and service providers to define the desired state of the execution environment. The resulting distributed software-defined environment continually adapts to meet objectives/constraints set by the users, applications, and/or resource providers. We present the design and prototype implementation of such distributed software-defined environment. We use a cancer informatics workflow to demonstrate the operation of our framework using resources from five different cloud providers, which are aggregated on-demand based on dynamic user and resource provider constraints.

The exponential growth of digital data sources has the potential to transform all aspects of society and our lives. However, to achieve this impact, the data has to be processed promptly to extract insights that can drive decision making. Further, traditional approaches that rely on moving data to remote data centers for processing are no longer feasible. Instead, new approaches that effectively leverage distributed computational infrastructure and services are necessary. Specifically, these approaches must seamlessly combine resources and services at the edge, in the core, and along the data path as needed. This paper presents our vision for enabling an approach for computing in the continuum, i.e., realizing a fluid ecosystem where distributed resources and services are programmatically aggregated on-demand to support emerging data-driven application workflows. This vision calls for novel solutions for federating infrastructure, programming applications and services, and composing dynamic workflows, which are capable of reacting in real-time to unpredictable data sizes, availabilities, locations, and rates.

The popularity and proliferation of the Internet of Things (IoT) paradigm is resulting in a growing number of devices connected to the Internet. These devices are generating and consuming unprecedented amounts of data at the edges of the infrastructure, and are enabling new classes of data-drivenapplications, however, current approaches typically rely on cloud platforms located at the core of the infrastructure to process data. As the number of devices and the amount of data they generate and consume increases, such core-centric approaches are becoming increasingly inefficient as they need to transfer data back and forth between the edge and the core. Furthermore, the latencies associated with such data transfer may not be able to support applications involving time-critical data-driven decision making. In this paper, we propose an edge-based programming framework that allows users to define how data streams are processed based on the content and the location of the data. This enables the definition of data-driven reactive behaviors that can effectively exploit data patterns to dynamically drive stream processing, leveraging resources located at the edges of the infrastructure. We have implemented a prototype of the proposed approach and performed several experiments to evaluate its scalability and efficiency against a more typical single-cloud approach. Using a smart-city application usecase, we illustrate that the presented programming system can support data-driven stream processing using edge resources. In terms of scalability, our experiments show that the system can scale to hundreds of nodes while keeping overheads low. Our experiments also show that our approach can perform up to 56% better than a single cloud approach that does not consider data and user locality.

Data staging has been shown to be very effective for supporting data intensive in-situ workflows and coupling of applications. Experimental sciences are increasingly becoming collaborative among geographically distributed teams, and include experimental instruments and HPC facilities. This new way of doing science poses new challenges due to data sizes, complexity of computation, and the use of wide area networks between couplings. In this paper, we explore how the staging abstraction can be extended to support such workflows. Specifically, we develop a NUMA-like abstraction that orchestrates multiple distributed local-area staging abstractions, and provides asynchronous data put/get semantics to enable data sharing across them. To mask data movement overhead and provide in-time data access, we propose the use of predictive prefetching approaches that leverage the iterative nature of the coupling. We evaluate our prototype implementation using a fusion workflow and show that our design can effectively and transparently support widearea coupled workflows. Additionally, results show that the use of prefetching techniques leads to significant gains in data access times of data that needs to be moved over the wide area network.

Summary Recent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at predefined time intervals or triggered by user‐defined events. Understanding how such streaming content (the raw data or events) can be processed within a time threshold remains an important research challenge. We investigate how a cloud‐based computational infrastructure can autonomically respond to such streaming content, offering quality of service guarantees. In particular, we contextualize our approach using an electric vehicles (EVs) charging scenario, where such vehicles need to connect to the electrical grid to charge their batteries. There has been an emerging interest in EV aggregators (primarily intermediate brokers able to estimate aggregate charging demand for a collection of EVs) to coordinate the charging process. We consider predicting EV charging demand as a potential workload with execution time constraints. We assume that an EV aggregator manages a number of geographic areas and a pool of computational resources of a cloud computing cluster to support scheduling of EV charging. The objective is to ensure that there is enough computational capacity to satisfy the requirements for managing EV battery charging requests within specific time constraints.

Recent advances in sensor technologies and instrumentation have led to an extraordinary growth of data sources and streaming applications. A wide variety of devices, from smart phones to dedicated sensors, have the capability of collecting and streaming large amounts of data at unprecedented rates. A number of distinct streaming data models have been proposed. Typical applications for this include smart cites & built environments for instance, where sensor-based infrastructures continue to increase in scale and variety. Understanding how such streaming content can be processed within some time threshold remains a non-trivial and important research topic. We investigate how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering Quality of Service guarantees. We propose an autonomic controller (based on feedback control and queueing theory) to elastically provision virtual machines to meet performance targets associated with a particular data stream. Evaluation is carried out using a federated Cloud-based infrastructure (implemented using CometCloud)-where the allocation of new resources can be based on: (i) differences between sites, i.e., types of resources supported (e.g., GPU versus CPU only), (ii) cost of execution; (iii) failure rate and likely resilience, etc. In particular, we demonstrate how Little's Law-a widely used result in queuing theory-can be adapted to support dynamic control in the context of such resource provisioning.

As the number of people who interact on social networks increases, and coupled with the greater capability made available within our computational devices, there is the potential to establish “Social Clouds”-a resource sharing infrastructure that enable people who have trust relationships to come together to share computational/ data services within a community. Social clouds can also provide the means to enhance multi-user collaboration and greatly stimulate the exchange of resources among participants. Recent research in the establishment and use of Social Clouds has raised significant interest by proposing an environment where users are able to trade resources mediated by a social networking mechanism. In such a cloud environment the incentives for sharing can represent a solution for improving resource utilisation and for making available additional capacity to friends and collaborators. In this paper we demonstrate how revenue can be earned within a social cloud community, by executing internal (intra community) and external (inter community) tasks. A number of different scenarios are first investigated through simulation, using the PeerSim simulator, in order to validate our approach. We use two key metrics: revenue and reputation, to evaluate how the system dynamics change as new tasks are added to one or more communities for execution, along with additional behaviours, such as nodes migrating from one community to another, or selectively reporting on the outcome of task execution. Subsequently, we develop a practical deployment using a federated cloud scenario using the CometCloud system-deployed over three sites: Cardiff (UK), Rutgers and Indiana. We show how approaches that have been simulated in PeerSim can be implemented in practice.

Data-driven application workflows that leverage compute capabilities and hosted services near the network edge can support latency-sensitive and critical applications in emerging areas such as Internet of Things (IoT) and smart infrastructure. However, distributed instantiation and execution of these workflows using resources across service providers and datacenters can be challenging. In this paper, we present the formulation of a decentralized workflow management approach for the autonomous instantiation and execution of dynamic data-driven workflows based on the opportunistic discovery and composition of services on-demand. Given a workflow template specification, this approach allows us to decouple workflow stages, allowing the execution of different stages to be performed by individual services, which are discovered and instantiated dynamically, and can be independently scaled as needed. These services may be geographically distributed and may be offered by different service providers using various QoS levels and cost models. The design, implementation and experimental evaluation of a decentralized workflow management framework using a live media stream application in a multi-cloud infrastructure is presented. Evaluations using a sample topology shows up to 2.5 times increase in QoS-meeting throughput when using our dynamic multi-cloud approach instead of using a fixed centralized cloud of identical capacity.

Pervasive computational ecosystems that combine data sources and computing/communication resources in self-managed environments, such as the ones powered by Internet of Things (IoT) devices, have the potential to automate and facilitate many aspects of our lives, and impact a variety of applications, from the management of extreme events to the optimization of everyday processes. However, this vision remains mostly unrealized despite the fact that the technology to achieve it exists, largely because of the gap between our ability to collect data and our ability to gain insight from it. In this paper, we discuss the challenges associated with providing a pervasive computational ecosystem. We then present our vision of how to best support data-driven computational ecosystems and propose a conceptual architecture that leverages ideas from software-defined environments in order to combine data, computing, and communication resources. In addition, we show how this proposed architecture enables the execution of data-driven workflows on top of these resources.

Since the social media hype started in the early 2000s, the Internet has bloomed with user-generated data. The content generated by users in social media varies from blogs, forums, social network platforms, and video sharing communities. This data has a special emphasis on the relationships among users of the community. As a consequence, social media data contains significant information about their creators and people around them. For this reason, law enforcement and insurance companies, among others, are starting to explore ways of using this data to identify unlawful or fraudulent activities. In this work, we present a solution to extract and analyze social media data in pursuit of identifying insurance fraud. We describe and evaluate an initial prototype of our solution that has been implemented on top of the CometCloud framework. We show how our solution is driven by the insights obtained from the data and it is able to extract data relevant to the investigators.

Cloud computing has generally involved the use of specialist data centres to support computation and data storage at a central site (or a limited number of sites). The motivation for this has come from the need to provide economies of scale (and subsequent reduction in cost) for supporting large scale computation for multiple user applications over (generally) a shared, multi-tenancy infrastructure. The use of such infrastructures requires moving data to a central location (data may be pre-staged to such a location prior to processing using terrestrial delivery channels and does not always require the use of a network-based transfer), undertaking processing on the data, and subsequently enabling users to download results of analysis. We extend this model using software defined networks (SDNs), whereby capability within the network can be used to support in-transit processing while data is in movement from source to destination. Using a smart building infrastructure scenario, consisting of sensors and actuators embedded within a built environment, we describe how an SDN-based architecture can be used to support real time data processing. This significantly influences the processing times to support energy optimisation of the building and reduces costs. We describe an architecture for such a distributed, multi-layered Cloud system and discuss a prototype that has been implemented using the CometCloud system, deployed across three sites in the UK and the US. Wevalidate the prototype using data from sensors within a Sports facility and making use of EnergyPlus.

Multi-cloud systems have enabled resource and service providers to co-exist in a market where the relationship between clients and services depends on the nature of an application and can be subject to a variety of different Quality of Service (QoS) constraints. Deciding whether a cloud provider should host (or finds it profitable to host) a service in the long-term would be influenced by parameters such as the service price, the QoS guarantees required by customers, the deployment cost (taking into account both cost of resource provisioning and operational expenditure, e.g. energy costs) and the constraints over which these guarantees should be met. In a federated cloud system users can combine specialist capabilities offered by a limited number of providers, at particular cost bands-such as availability of specialist co-processors and software libraries. In addition, federation also enables applications to be scaled on-demand and restricts lock in to the capabilities of a particular provider. We devise a market model to support federated clouds and investigate its efficiency in two real application scenarios:(i) energy optimisation in built environments and (ii) cancer image processing both requiring significant computational resources to execute simulations. We describe and evaluate the establishment of such an application based federation and identify a cost-decision based mechanism to determine when tasks should be outsourced to external sites in the federation. The following contributions are provided: (i) understanding the criteria for accessing sites within a federated cloud dynamically, taking into account factors such as performance, cost, user perceived value, and specific application requirements; (ii) developing and deploying a cost based federated cloud framework for supporting real applications over three federated sites at Cardiff (UK), Rutgers and Indiana (USA), (iii) a performance analysis of the application scenarios to determine how task submission could be supported across these three sites, subject to particular revenue targets.

Cloud computing is emerging as a viable platform for scientific exploration. The ideas of on-demand access to resources, "unlimited" resources as well as interesting pricing models are making scientist to move their workflows into cloud computing. However, the amount of services and different pricing models offered by the providers often overwhelm users when deciding which option is best for them. Moreover, interoperability across providers remains an open topic that forces users to develop specific solutions for each provider. In this paper, we present a service framework that enables the autonomic execution of dynamic workflows in multi-cloud environments. It also allows users to customize scheduling policies to use those resources that best fit their needs. To demonstrate the benefits of this framework, we study the execution of a real scientific workflow, with data dependencies across stages, in a multi-cloud federation using different policies and objective functions.

With increasing availability of instrumented infrastructures in built environments, it is necessary to understand how such data will be stored, processed and analysed in a timely manner. Many "smart cities" applications, for instance, identify how data from building sensors can be combined together to support applications such as emergency response, energy management, etc. Enabling sensor data to be transmitted to a Cloud environment for processing provides a number of benefits, such as scalability and elastic provisioning of computational resources - as the total data size may not be known apriori. In this application-based case study, we describe the integration of an in-building sensor network (both for sensing and actuation) with a distributed Cloud environment. Energy optimisation in buildings represents a class of problems that requires significant computational resources and generally is a time consuming process. We describe the use of Cloud computing for efficiently running and deploying Energy Plus simulations with sensor data in order to fulfil a number of energy related objectives for buildings. We describe and evaluate the establishment of such a sensor based application using a Comet Cloud implementation with data collection from a real building pilot. Although our focus is on a single application, the general architecture and analysis carried out can be generalised to other similar scenarios.

The use of in-silico simulations in experimental science can greatly increase laboratory efficiency and provide additional insights into interactions not easily described by traditional methods. Such simulations require significant amounts of computational resources, accessible only via supercom

One of the key benefits of Cloud systems is their ability to provide elastic, on-demand (seemingly infinite) computing capability and performance for supporting service delivery. With the resource availability in single data centres proving to be limited, the option of obtaining extra-resources from a collection of Cloud providers has appeared as an efficacious solution. The ability to utilize resources from multiple Cloud providers is also often mentioned as a means to: (i) prevent vendor lock in, (ii) to enable in house capacity to be combined with an external Cloud provider, (iii) combine specialist capability from multiple Cloud vendors (especially when one vendor does not offer such capability or where such capability may come at a higher price). Such federation of Cloud systems can therefore overcome a limit in capacity and enable providers to dynamically increase the availability of resources to serve requests. We describe and evaluate the establishment of such a federation using a CometCloud based implementation, and consider a number of federation policies with associated scenarios and determine the impact of such policies on the overall status of our system. CometCloud provides an overlay that enables multiple types of Cloud systems (both public and private) to be federated through the use of specialist gateways. We describe how two physical sites, in the UK and the US, can be federated in a seamless way using this system.

BACKGROUND: The development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance. RESULTS: The CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers. CONCLUSIONS: In this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.