Containers are an increasingly used mechanism for providing low-cost, lightweight, portable, standalone application deployments, particularly for service orchestration. Docker provides container technology that enables a single host to isolate several applications and deploy them rapidly in different environments. The increasing demand for container applications and the growing popularity of Docker has motivated extensive research into evaluating the performance, energy consumption, and running cost of Docker-based computation. This paper investigates the energy footprint of Docker containers and workloads. To motivate research in energy-efficient container development, this paper takes a practical approach to measure the energy consumption in common Docker containers under various workloads.

Resilience in context-aware applications is especially important within challenged network and physical environments. This paper discusses the current methods for maintaining resilience in context management architectures. These methods largely focus on resolving contextual information loss, rather than maintaining the standard operation of the deployed system. The paper contends that this approach leads to loss of functionality and unwanted modifications to contextual information. Existing approaches for mitigating the effects of network instability have a high resource requirement and do not maintain the standard functionality of the system in real time. The paper proposes and discusses the Lightweight Context Management Architecture (LCMA) which addresses the lack of lightweight solutions for resilient context management systems. The functional requirements of the LCMA components are proposed and detailed. The proposed LCMA will be validated in mission-critical applications with potential adversarial actions.

Typical Internet of Things (IoT) deployments are resource-constrained, with limited computation and storage, high network latency, and low bandwidth. The introduction of Edge and Cloud computing provides a method of mitigating these shortfalls. This paper proposes a framework for structuring IoT applications to allow for seamless offloading (based on CPU load) of work from IoT nodes to Edge and Cloud computing resources. The proposed flexible framework utilises software to orchestrate multiple containerised IoT applications for optimal performance within available computational resources. Edge and Cloud servers co-operate autonomously to determine the appropriate resource allocation based on the requirements of running IoT applications in real-time. The result is a framework that is suited to perform with heterogeneous IoT hardware while improving overall computational performance, latency and bandwidth relative to IoT architectures that do not auto-scale. This framework is evaluated using an experimental setup with multiple IoT nodes, Edge nodes and Cloud computing resources. It demonstrates the approach is viable and results in a flexible and scalable IoT solution.

In this paper we introduce Jarvis, a context-as-a-service mobile tool, which enables context-aware data collection, service discovery, and computer-aided situational awareness through a conversational User Interface (UI). At the core of Jarvis are two main components: (i) a voice-based UI to translate speech to Context Definition and Query Language (Speech-to-CDQL), and (ii) an operational component called Context-as-a-Service (CoaaS), which enables smart things and IoT silos to discover, validate and share relevant and dependable context. The UI is based on two machine learning models: a Speech-to-Text model and a Text-to-CDQL model based on an encoder-decoder architecture. Jarvis is developed as a mobile application that allows people with different backgrounds to interact with various IoT devices. Our demo shows how easy Jarvis can be used for context-aware data collection and to interact with diverse objects in a smart home environment through voice.

The pervasiveness of social media and user-generated content has triggered an exponential increase in global data. However, due to collection and extraction challenges, data in embedded comments, reviews and testimonials are largely inaccessible to a knowledge management system. This article describes a KM framework for the end-to-end knowledge management and value extraction from such content. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source. Three contributions are described in this article. First, a method for automatically navigating webpages to expose UGC for collection is presented. This is evaluated using browser emulation integrated with automated collection. Second, a method for collecting data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing techniques, providing evidence of the increased amount of UGC data extracted.

Cloud of Things (CoT) is increasingly viewed as a paradigm that can satisfy the diverse requirements of emerging Internet of Things (IoT) applications. The potential of CoT is not yet realized due to challenges in sharing and reusing IoT physical resources across multiple applications. The existing approaches provide small-scale and hardware-dependent shared access to IoT resources. This article considers using market mechanisms to commoditize CoT resources as the approach to enable shared access to CoT resources and to improve their reusability. In order to achieve this, the requirements for trading CoT resources are discussed to conceptualize the proposed approach. A generic description model for CoT resource is introduced to quantify the value of CoT resources. In this article, a marketplace architecture for trading CoT resources referred to as AMACoT is proposed. By formulating the trading of CoT resources as an optimization problem, the proposed approach is experimentally validated. The evaluation measures the system performance and verifies the optimization problem using three evolutionary algorithms. The evaluation of the optimization algorithms demonstrates the optimality of trading CoT resources solutions in terms of resource cost, resource utilization, provider lock-in, and provider profit.

The emerging Cloud of Things (CoT) paradigm promises to meet the diverse requirements of many real-world applications, which previously could not be fulfilled by either cloud computing or Internet of Things (IoT). Trading CoT resources is a challenging aspect, particularly when managing quality of service (QoS) as resource providers and application developers have different priorities. This article focuses on the challenge of supporting QoS when trading CoT resources and performing resource allocation. The contributions of this article are: 1) the problem of managing QoS while trading CoT resources is investigated as an optimization problem; 2) a QoS model is proposed to solve the problem by optimizing five different QoS objectives; and 3) experimental evaluation of the proposed model using three optimization algorithms. The evaluation results show the efficiency and dynamism of the proposed model in optimizing CoT resource allocation based on diverse QoS objectives, including resource cost, energy consumption, response time, fault tolerance, and resource coverage.

Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers—support vector machines (SVMs), K -nearest neighbour and random forest—is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset—the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.

Summary An increasing number of enterprise applications are intensive in their consumption of IT but are infrequently used. Consequently, either organizations host an oversized IT infrastructure or they are incapable of realizing the benefits of new applications. A solution to the challenge is provided by the large‐scale computing infrastructures of clouds and grids, which allow resources to be shared. A major challenge is the development of mechanisms that allow efficient sharing of IT resources. Market mechanisms are promising, but there is a lack of research in scalable market mechanisms. We extend the multi‐attribute combinatorial exchange mechanism with greedy heuristics to address the scalability challenge. The evaluation shows a trade‐off between efficiency and scalability. There is no statistical evidence for an influence on the incentive properties of the market mechanism. This is an encouraging result as theory predicts heuristics to ruin the mechanism's incentive properties. Copyright © 2015 John Wiley & Sons, Ltd.

Large scale distributed e-infrastructures are emerging as commodity resource platforms. The next generation of commodity e-infrastructures will encapsulate the physical or tangible world by integrating ubiquitous sensors. Cheap environmental and physiological sensors are being increasingly deployed by many commercial organisations. The process of discovering and accessing commercially available resources requires a market for providers and consumers to trade these resources. This paper argues that developing a market will encourage the commoditisation of environmental sensor networks. It presents an overall architecture and adopts algorithms to support the trading of commodity environmental sensor networks.

Ethernet data rates have increased many orders of magnitudes since standardisation in 1982. Despite these continual data rates increases, the 1500 byte Maximum Transmission Unit (MTU) of Ethernet remains unchanged. Experiments with varying latencies, loss rates and transaction lengths are performed to investigate the potential benefits of Jumboframes on the Internet. This study reveals that large MTUs offer throughputs much larger than a simplistic overhead analysis might suggest. The reasons for these higher throughputs are explored and discussed.

Wireless Sensor Networks (WSNs) can have high demands for real-time data transmission and processing, but this is often constrained by limited resources. Cloud Computing can act as the backend for WSNs to provide processing and storage on demand. This paper proposes a generic architecture to support the integration of sensors with the Cloud. It uses a lightweight component model and dynamic proxy-based approach to connect sensors to the Cloud. The feasibility of this approach is evaluated experimentally.

Physiological monitoring has been used in a wide range of scenarios to assist in disease diagnosis, athlete monitoring and other activities. There are also many opportunities in analysing aggregate data from groups of people rather than individuals such as public event monitoring or athletic team performance optimisation. Numerous difficulties exist pertaining to this, particularly concerning how to process and transform the resulting physiological data in real-time when many devices are producing data. This paper proposes a system that is designed to monitor, analyse and report physiological data in real-time by leveraging mobile devices as distributed processors.

Abstract Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has the responsibility of establishing how best to map tasks within a workflow to the available resources. As workflows are typically run over shared resources, and thus face unpredictable and changing resource capabilities, there may be benefit to be derived from adapting the task‐to‐resource mapping while a workflow is executing. This paper describes the use of utility functions to express the relative merits of alternative mappings; in essence, a utility function can be used to give a score to a candidate mapping, and the exploration of alternative mappings can be cast as an optimization problem. In this approach, changing the utility function allows adaptations to be carried out with a view to meeting different objectives. The contributions of this paper include: (i) a description of how adaptive workflow execution can be expressed as an optimization problem where the objective of the adaptation is to maximize a utility function; (ii) a description of how the approach has been applied to support adaptive workflow execution in execution environments consisting of multiple resources, such as grids or clouds, in which adaptations are coordinated across multiple workflows; and (iii) an experimental evaluation of the approach with utility measures based on response time and profit using the Pegasus workflow system. Copyright © 2010 John Wiley & Sons, Ltd.

Smart space (SS) communication has rapidly emerged as an exciting new paradigm that includes ubiquitous, grid and pervasive computing to provide intelligence, insight and vision for the emerging world of intelligent environments, products, services and human interaction. Dependable networking of a SS environment can be ensured through reliable routing, efficient selection of error-free links, rapid recovery from broken links and the avoidance of congested gateways. Since link failure and packet loss are inevitable in SS wireless sensor networks (WSNs), the authors have developed an efficient scheme to achieve a reliable data collection for SSs composed of low capacity wireless sensor nodes. WSNs must tolerate a certain lack of reliability without a significant effect on packet delivery performance, data aggregation accuracy or energy consumption. An effective hybrid scheme is presented that adaptively reduces control traffic with a metric that measures the reception success ratio of representative data packets. Based on this approach, the proposed routing scheme can achieve reduced energy consumption while ensuring minimal packet loss in environments featuring high link failure rates. The performance of the proposed routing scheme is experimentally investigated using both simulations and a test bed of TelosB motes. It is shown to be more robust and energy efficient than the network layer provided by TinyOS2.x. The results show that the scheme is able to maintain better than 95% connectivity in an interference-prone medium while achieving a 35% energy saving.

As Grid infrastructures become more widely used by the academic and commercial world, the problem of resource allocation increases in complexity. Resource trading markets are one mechanism that allows many resource owners and resource consumers to trade. To perform efficiently trading markets for grids require approaches to match consumers and producers. Solutions for optimal and non-optimal resource trading exist, but fail to scale effectively to meet the challenges of large numbers of traders. This paper first defines the problem of scalable resource trading in grids before describing and evaluating greedy approaches for scalability.

Designing a reliable and trusted routing scheme for resource-constrained Wireless Sensor Networks (WSNs) is a challenging task due to the lack of infrastructure and the highly dynamic network topology. To ensure trustworthy end-to-end communications between wirelessly connected sensor nodes, a considerable amount of bidirectional traffic must be relayed either between neighboring sensor nodes or between source sensor nodes and the base station. Such scenarios may lead to an added routing overhead, higher energy depletion rate and network life time minimization. The existing trusted routing protocols focus on trusted data dissemination while lacking the consideration of the restricted resources of sensor nodes and low-power radio link failures. To solve this problem, we propose a reliability-oriented routing scheme that takes into account the link reliability and residual energy of sensor nodes, thus allowing for better trustworthy data exchange, traffic balancing and network lifetime extension. Based on real test bed experiments and large-scale simulations, the attained results show the benefits stemming from the adoption of our scheme to be a reliable and energy efficient data delivery platform for potential trusted data exchange models. Our results show that the scheme is able to reduce energy consumption without affecting the connectivity of the network.

Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has responsibility for establishing how best to map tasks within a workflow to the available resources. As workflows are typically run over shared resources, and thus face unpredictable and changing resource capabilties, there may be benefit to be derived from adapting the task-to-resource mapping while a workflow is executing. This paper describes the use of utility functions to express the relative merits of alternative mappings; in essence, a utility function can be used to give a score to a candidate mapping, and the exploration of alternative mappings can be cast as an optimization problem. In this approach, changing the utility function allows adaptations to be carried out with a view to meeting different objectives. The contributions of this paper include: (i) a description of how adaptive workflow execution can be expressed as an optimization problem where the objective of the adaptation is to maximize some property expressed as a utility function; (ii) a description of how the approach has been applied to support adaptive workflow execution in grids; and (iii) an experimental evaluation of the resulting approach for alternative utility measures based on response time and profit.

Abstract Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has the responsibility of establishing how best to execute a workflow given the available resources. The Pegasus workflow management system compiles abstract workflows into concrete execution plans, and has been widely used in large‐scale e‐Science applications. This paper describes an extension to Pegasus whereby resource allocation decisions are revised during workflow evaluation, in the light of feedback on the performance of jobs at runtime. The contributions of this paper include: (i) a description of how adaptive processing has been retrofitted to an existing workflow management system; (ii) a scheduling algorithm that allocates resources based on runtime performance; and (iii) an experimental evaluation of the resulting infrastructure using grid middleware over clusters. Copyright © 2009 John Wiley & Sons, Ltd.

Component-based software structuring principles are now commonplace at the application level; but componentization is far less established when it comes to building low-level systems software. Although there have been pioneering efforts in applying componentization to systems-building, these efforts have tended to target specific application domains (e.g., embedded systems, operating systems, communications systems, programmable networking environments, or middleware platforms). They also tend to be targeted at specific deployment environments (e.g., standard personal computer (PC) environments, network processors, or microcontrollers). The disadvantage of this narrow targeting is that it fails to maximize the genericity and abstraction potential of the component approach. In this article, we argue for the benefits and feasibility of a generic yet tailorable approach to component-based systems-building that offers a uniform programming model that is applicable in a wide range of systems-oriented target domains and deployment environments. The component model, called OpenCom , is supported by a reflective runtime architecture that is itself built from components. After describing OpenCom and evaluating its performance and overhead characteristics, we present and evaluate two case studies of systems we have built using OpenCom technology, thus illustrating its benefits and its general applicability.

This paper describes the open P2P tracing system which aims to improve the research community's understanding of P2P file sharing systems by providing continuous and up-to-date traffic data which is anonymized and made freely accessible to all interested parties. It is our hope that this open data set will grow over time into a resource capable of exposing trends in P2P network usage and promote research into the socio-technical factors that drive user behaviour on P2P file sharing systems.

Network processors (NPs) are set to play a key role in the next generation of networking technology. They have the performance of ASIC-based routers whilst offering a high degree of programmability. However, the programmability potential of NPs can only be realized with appropriate software. In this paper, we argue that specialized software to support runtime reconfiguration is needed to fully exploit the potential of NPs. We first justify supporting runtime reconfiguration on NPs by offering real-world scenarios and discussing the issues associated with these. We then demonstrate how runtime reconfiguration can be achieved in practice through a case study of our component-based programming approach on the Intel IXP2400 NP