Abstract Fast machine learning (FastML) has strong potential to enhance energy optimization and operational efficiency in heating, ventilation, and air conditioning (HVAC) systems within building management systems (BMS). Traditional HVAC control approaches frequently depend on static schedules and computationally intensive, CPU-based optimization techniques, which often lack the responsiveness and scalability required for real-time embedded applications. To address these limitations, we propose a fast machine learning framework that integrates a random forest surrogate model implemented as a hardware accelerator on the programmable logic (PL) with a lightweight and adaptive genetic algorithm (GA) executed on the processing system (PS), thereby forming a hybrid PS–PL deployment. This combination of fast machine learning and evolutionary algorithms optimization delivers substantial computational efficiency, achieving over 1.67 million predictions per second on a PYNQ-Z1 FPGA and significantly outperforming recent FPGA-based approaches. By using a case study, we demonstrate how FastML can employ a GA multi-objective fitness function to dynamically optimize hourly airflow rates and supply air temperatures in response to occupancy and seasonal environmental patterns, thereby reducing electricity and thermal energy consumption while maintaining occupant comfort within standard predicted mean vote (PMV) thresholds. Empirical evaluation conducted over 72 days across four distinct seasons reveals consistent electricity savings exceeding 50%, alongside thermal energy reductions of up to 150 kWh per day during heating periods. A comprehensive three-dimensional Pareto front analysis further substantiates the system’s capability to effectively balance energy efficiency and occupant comfort. These results highlight the practicality, scalability, and substantial promise of FPGA-based multi-objective optimization as a robust, real-time solution for intelligent and sustainable building energy management at the edge.

This research demonstrates the benefits of integrating digital twin (DT) technology with advanced artificial intelligence techniques for energy management and occupant comfort optimization in large-scale sports facilities. By implementing a high-fidelity DT framework at the Qatar University Sports and Events Complex, our approach achieves a 14.52% reduction in heating, ventilation, and air conditioning power consumption and an 85.03% improvement in thermal comfort, resulting in substantial cost savings and a notable reduction in carbon emissions. We unify an EnergyPlus-driven DT, artificial neural network surrogates, and multiobjective optimization (Nondominated sorting genetic algorithms II/III, Unified NSGA-III, and S-metric selection evolutionary multiobjective optimization algorithm) and validate the framework on a live facility, demonstrating real-world deployability with existing building management system infrastructures.

The Internet of Things (IoT) and Edge-Cloud Computing have been trending technologies over the past few years. In this work, we introduce the Enhanced Optimized-Greedy Nominator Heuristic (EO-GNH), a framework designed to optimize machine learning (ML) and artificial intelligence (AI) application placement in edge environments, aiming to improve Quality of Service (QoS). Developed specifically for sectors such as smart agriculture, industry, and healthcare, EO-GNH integrates asynchronous MapReduce and parallel meta-heuristics to effectively manage AI applications, focusing on execution performance, resource utilization, and infrastructure resilience. The framework carefully addresses the distribution challenges of AI applications, especially Service Function Chains (SFCs), in edge-cloud infrastructures. It contains Data Flow Management, which covers aspects of data storage and data privacy, and also considers factors like regional adaptations, mobile access, and AI model refinement. EO-GNH ensures high availability for forecasting, prediction, and training AI models, operating efficiently within a geo-distributed infrastructure. The proposed strategies within EO-GNH emphasize concurrent multi-node execution, enhancing AI application placement by improving execution time, dependability, and cost-effectiveness. The efficiency of EO-GNH is demonstrated through its impact on QoS in real-time resource management across three application domains, highlighting its adaptability and potential in diverse cross-domain IoT-based environments. • EO-GNH optimizes AI application placement in edge computing environments for improved QoS performance • EO-GNH leverages Parsl for parallel meta-heuristic execution and optimized workload deployment • EO-GNH is faster than distributed NSGA-II in finding solutions to multi-objective optimization problems • EO-GNH delivers edge AI capabilities from federated learning to real-time inference • EO-GNH transforms IoT operations across healthcare, industry, and agriculture applications

With increasing complexity of global construction supply chains and the substantial contribution such supply chains make to the environment, there is a need to understand how components of these supply chains can be reused and repurposed. Blockchains provide an important basis for recording transactions carried out in supply chains to aid potential reusability.A recent example of circularity in the built environment includes the tracking of material reuse across the building lifecycle (from construction to demolition and then reuse). The use of decentralisation and immutability in Blockchains is used to demonstrate how material passports may be supported and used as a basis to create a circular economy in construction.

Abstract Cloud federation enables inter‐layer resource exchanges among multiple, heterogeneous cloud service providers. This article proposes a Quality of Service (QoS) aware trust model for effective resource allocation in response to the various user requests within the Clouds4Coordination (C4C) federation system. This QoS mainly comprises of nine parameters combined into three categories: (i) node profile, (ii) reliability, and (iii) competence. Numerical values for these parameters are computed every ‘t’ seconds for each cloud provider. All values measured over an interval Δt are further processed by the proposed model to evaluate the utility associated with a provider (referred to as a discipline in the presented case study). The decision about interacting with a discipline in a collaborative project is based on this utility value. The systems architecture, evaluation methodology, proposed model, and experimental evaluation on a practical test bed is outlined. The proposed QoS‐aware trust evaluation mechanism allows selection of the most useful (based on a utility value) providers. The proposed approach can be used to support federation of cloud services across a number of different application domains.

Abstract Predicting energy consumption in buildings plays an important part in the process of digital transformation of the built environment, and for understanding the potential for energy savings. This also contributes to reducing the impact of climate change, where buildings need to increase their adaptability and resilience while reducing energy consumption and maintain user comfort. The use of Internet of Things devices for monitoring and control of energy consumption in buildings can take into account user preferences, event monitoring and building optimization. Detecting peak energy demand from historical building data can enable users to manage their energy use more efficiently, while also enabling real-time response strategies (including control and actuation) to known or future scenarios. Several statistical, time series, and machine learning techniques are proposed in this work to predict electricity consumption for five different building types, by using peak demand forecasting to achieve energy efficiency. We have used several indigenous and exogenous variables with a view to test different energy forecasting scenarios. The suggested techniques are evaluated for creating predictive models, including linear Regression, dynamic regression, ARIMA time series, exponential smoothing time series, artificial neural network, and deep neural network. We conduct the analysis on an energy consumption dataset of five buildings from 2014 until 2019. Our results show that for a day ahead prediction, the ARIMA model outperforms the other approaches with an accuracy of 98.91% when executed over a 168 h (1 week) of uninterrupted data for five government buildings.

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.

As devices existing at the edge of the network improve in their processing and data storage capacity, there is increasing potential to host and enact services on such devices. A workflow that was traditionally enacted on a data centre can be fragmented across both edge and data centre hosted resources. The following aspects are investigated in this work: (i) mechanisms for dividing a workflow across edge and cloud/data centre resources; (ii) service hosting environments that can be shared across edge and data centre resources; (iii) performance metrics that can influence service placement and selection. An "edge orchestrator" is a resource manager that makes such decisions on the behalf of a user application, and which may be centralised or distributed. An industry scenarios is used to illustrate decision points that influence such choices within an edge orchestrator. The overall objective considered is the completion of the workflow within some deadline constraint by the edge orchestrator.

Regulation procedures and water management that incorporate projected hydrological changes with related uncertainties become extremely important in order to prevent degradation of water ecosystems. Ensuring real time water management and optimization becomes mandatory for resolving the constraints of water supply/demand and to comply with biodiversity requirements. We focus our research on water optimization and catchment regulation and present our solution that has been developed as part of the Innovate UK Radical project.11Developing a Real Time Abstraction and Discharge Permitting Process for Catchment Regulation and Optimized Water Management. In our study, we use the Usk reservoir in South Wales with rich biodiversity and nationally significant fishery to optimize catchment flow and to conserve water with real-time catchment management information to support the decision makers. Our developed solution uses artificial intelligence techniques to deliver real-time decision support for water management and catchment regulation with reflection to biodiversity protection and reservation. We present an intelligent analytics system that uses real-time data from river stations enabling informed decisions and a more dynamic approach for managing water resources. The system utilizes a neuronal network engine to support river level prediction based on which a dependency modeling is developed for assessing the probability of risk in the Usk reservoir.

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.

As multimedia traffic provides the dominating data flows on Internet channels nowadays, on-demand video streaming has also grown in popularity. Users watching on-demand streams are interested in receiving their video streams without interruption and at low cost. To achieve this, video providers need to build computational infrastructure that can adapt to changes in demand. This paper presents an architecture to build synergy between Peer-to-Peer and Cloud Computing systems to achieve the necessary throughput for video-on-demand providers with reduced costs. We propose heuristics that overcome lack of stability in P2P systems by the inclusion of cloud servers into the pool of video streaming servers, avoiding disruptions (stalls) even when the total number of servers are reduced. Simulation results show that the proposed heuristic strategies can help reduce costs while maintaining quality of service.

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.

Summary Social Clouds provide the capability to share resources among participants within a social network—leveraging on the trust relationships already existing between such participants. In such a system, users are able to trade resources between each other rather than make use of capability offered at a (centralized) data center. Although such an environment has significant potential for improving resource utilization and making available additional capacity that remains dormant, incentives for sharing remain an important hurdle limiting its effective. In this paper, we utilize the socioeconomic model proposed by Silvio Gesell to demonstrate how a ‘virtual currency’ can be used to incentivise sharing of resources within a ‘community’. We subsequently demonstrate, through simulations, the benefit provided to participants within such a community using a variety of economic (such as overall credits gained) and technical (number of successfully completed transactions) metrics. Further, we describe our implementation of such a Social Cloud using CometCloud. CometCloud is an autonomic computing engine for cloud and grid environments. It supports highly heterogeneous and dynamic federated cloud/Grid infrastructures, integration of public/private clouds and autonomic cloudbursts. We demonstrate the implementation of two designs on the basis of the master/worker approach: (i) one tuple space per cluster and (ii) one coordination tuple space and multiple transient spaces—one per each cluster. Finally, we discuss an extended version of our Social Cloud model where intermediary relay nodes take on more active roles as traders in a transaction. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Distributed systems in which users consume and supply different types of services and resources are becoming ever more prevalent. For the matching of consumers and providers, preference-based two-sided matching algorithms can be used to improve the efficiency of the overall match outcome. In such systems, requesting (providing) users rank others based on preferences derived from feedback. Trust can be inferred using trust networks through direct or indirect feedback based on previous actions, and influences the preference ranking that users submit to the matching algorithm. As feedback influences the preference ranking and thus the matching, in this paper we propose a novel methodology which makes use of feedback in the decision making of preference rankings, in order to avoid being matched to untrustworthy users (or provides not likely to deliver their service). We use a simulation based validation approach to determine the effects of dynamic/continuous feedback and its influence on preference ranking of providers.

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.

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.

Cloud computing generally involves the use of data storage and computational resources from external providers. Although a number of commercial providers are currently on the market, it is often beneficial for a user to consider capability from a number of different ones. This would prevent vendor lock-in and more economic choice for a user. Based on this observation, work on "Social Clouds" has involved using social relationships formed between individuals and institutions to establish Peer-2-Peer resource sharing networks, enabling market forces to determine how demand for resources can be met by a number of different (often individually owned) providers. In this paper we identify how trading within such a network could be enhanced by the dynamic emergence (or identification) of brokers -- based on their social position in the network (based on connectivity metrics within a social network). We investigate how offering financial incentives to such brokers, once discovered, could help improve the number of trades that could take place with a network. A social score algorithm is described and simulated with PeerSim to validate our approach. We also compare the approach to a distributed dominating set algorithm - the closest approximation to our approach.

The increasing availability of Internet services has stimulated the development of peer-to-peer markets. These electronic markets have the potential for improving the efficiency of trading by reducing search and transaction costs. They also allow buyers to choose the best possible service for every