Smart cities generate huge volumes of data to be processed by applications with different criticality and requirements. For example, a healthcare application needs lower latency when requested from an ambulance travelling to a hospital during an emergency compared to applications in less-critical domains. Cities can use Multi-access Edge Computing to reduce latency by placing applications’ services closer to users. A service placement process selects the set of servers to run the services for deployment. Smart cities challenge this selection as a large number of servers and services generate a large number of potential solutions with different QoS properties. Additionally, placement approaches must consider applications’ criticality and users’ mobility to offer an appropriate overall latency. Current approaches have considered servers’ utilisation and users’ location to place services. However, they do not consider applications’ criticality and mobile users’ paths. This paper presents MAACO, a Mobility-Aware, priority-driven, ACO-based service placement model that prioritises applications according to their criticality and minimises critical applications’ latency, while considering predicted paths for mobile users. Evaluation results show that MAACO achieves lower latency and waiting time compared against baselines at the cost of reduced load balance between the network servers.

Automatic planning, with dynamic binding and adaptive composition recovery, has been used to tackle complex service provisioning in mobile environments, but given frequent network topology changes, and services with time-dependent QoS, finding composites that can functionally and non-functionally satisfy a user's request remains difficult. Many service composition mechanisms either require a centralised perspective of the environment, or use optimisation mechanisms that trade off computational efficiency for optimality. Stigmergy-based approaches have been used to model decentralised service interactions between service providers, using a community of mobile software agents that share the same goal to approximate the set of QoS-optimal service compositions. Inspired by this model, this article addresses computational efficiency concerns using a collaborative approach to engage multiple communities of agents for provisioning QoS-optimal service compositions in mobile environments. New compositions can emerge from local decisions and interactions with agents from diverse communities. We assess whether having multiple communities improves the diversity and optimality of solutions. We also measure the proposed approach’ efficiency in dealing with incomplete information. The results show that the proposed approach trades optimality for a more diverse set of solutions, at a cost of higher overhead.

Pervasive applications in smart cities rely on a large number of IoT devices, which are deployed in large geographic areas. Smart cities can manage these devices using Service-Oriented Architectures (e.g., micro-services) by encapsulating devices capabilities as IoT services. Distributed service discovery architectures reduce search spaces and perform discovery processes closer to consumers on edge devices. However, request management, a key task in distributed service discovery, is still challenging because requests must be forwarded through large networks where nodes have partial knowledge about other participants. Previous research has shown that social-based and bio-inspired methods can be used to manage requests in small-scale environments, but such approaches do not scale to large environments. This paper adds urban context to a social-based and bio-inspired mechanism to forward requests where they are most likely to be solved. Results show that our model has the best rate of solved requests, and intermediate latency.

This document introduces the artifacts that implement the request manager proposed in the paper "An Urban-driven Service Request Management Model". The artifacts can be found in the public TCD GitLab project percom2020-srmm 1 .

Multiaccess edge computing (MEC) will strengthen forthcoming 5G networks by improving the Quality of Service (QoS), in particular, reducing latency, increasing data processing rates, and providing real-time information to develop high-value Internet-of-Things (IoT) services. To enable data-intensive network services and support advanced analytics, many network operators have proposed to integrate MEC systems with network function virtualization (NFV) consolidating virtual network functions (VNFs) and edge capabilities on a shared infrastructure. As of yet, this integration is not fully established, with various architectural issues currently open, even at standardization level. For instance, any update to VNFs deployed in a MEC system requires a time-consuming manual effort, which affects the overall infrastructure operations. To address these pitfalls, VNFs can be decomposed into microservices, which maintain their own states and exhibit different resource consumption requirements. This article presents an approach to integration that leverages serverless computing to merge MEC and NFV at the system level and to deploy VNFs on demand, by combining MEC functional blocks with an NFV orchestrator using a Kubernetes cluster. We further investigate whether the resource utilization of a MEC system can be improved by leveraging networked FPGA-enabled MEC servers, through an extension of the edge layer that takes advantage of available programmable hardware. We quantitatively evaluate and demonstrate the improvement of 75% end-to-end latency, 99.96% VNF execution time, 26.9% resource utilization, and 15.8% energy consumption in comparison with traditional baselines of cloud, edge, and serverless-edge test cases for a high-definition real-time video streaming application.

In service-oriented architectures, collaborative filtering is a key technique for service recommendation based on QoS prediction. Matrix factorisation has emerged as one of the main approaches for collaborative filtering as it can handle sparse matrices and produces good prediction accuracy. However, this process is resource-intensive and training must take place in the cloud, which can lead to a number of issues for user privacy and being able to update the model with new QoS information. Due to the time-varying nature of QoS it is essential to update the QoS prediction model to ensure that it is using the most recent values to maintain prediction accuracy. The request time, which is the time for a middleware to submit a user's information and receive QoS metrics for a candidate services is also important due to the limited time during dynamic service adaptations to choose suitable replacement services. In this paper we propose a stacked autoencoder with dropout on a deep edge architecture and show how this can be used to reduce training and request time compared to traditional matrix factorisation algorithms, while maintaining predictive accuracy. To evaluate the accuracy of the algorithms we compare the actual and predicted QoS values using standard error metrics such as MAE and RMSE. In addition, we propose an alternative evaluation technique using the predictions as part of a service composition and measuring the impact that the predictions have on the response time and throughput of the final composition. This more clearly shows the direct impact that these algorithms will have in practice.

The proliferation of Internet of Things (IoT) and the success of resource-rich cloud services have pushed the data processing horizon towards the edge of the network. This has the potential to address bandwidth costs, and latency, availability and data privacy concerns. Serverless computing, a cloud computing model for stateless and event-driven applications, promises to further improve Quality of Service (QoS) by eliminating the burden of always-on infrastructure through ephemeral containers. Open source serverless frameworks have been introduced to avoid the vendor lock-in and computation restrictions of public cloud platforms and to bring the power of serverless computing to on-premises deployments. In an IoT environment, these frameworks can leverage the computational capabilities of devices in the local network to further improve QoS of applications delivered to the user. However, these frameworks have not been evaluated in a resource-constrained, edge computing environment. In this work we evaluate four open source serverless frameworks, namely, Kubeless, Apache OpenWhisk, OpenFaaS, Knative. Each framework is installed on a bare-metal, single master, Kubernetes cluster. We use the JMeter framework to evaluate the response time, throughput and success rate of functions deployed using these frameworks under different workloads. The evaluation results are presented and open research opportunities are discussed.

Many modern software systems and applications are built using heterogeneous services provided by a range of devices, from high-power devices located in the Cloud to potentially resource-constrained and/or mobile services from IoT devices at the edge of the network. The large growth in the number of these services has led to some functionally similar services. When selecting services, a critical criterion is Quality of Service (QoS), which includes factors such as response time, location and cost. As the value of dynamic QoS attributes vary with time, there is a need to accurately forecast future QoS values to identify if a service may be about to fail. In this paper, we propose using an LSTM-based neural network to forecast future QoS values. We evaluate the use of an LSTM network against the existing state of the art in experiments using an established web service dataset and a new dataset collected by deploying services on low power IoT devices, which we publicly release. This mixture of datasets covers the heterogeneity that would be expected in a typical IoT environment.

Smart cities provide software services to citizens that are likely to be deployed in large, dynamic, heterogeneous, and distributed environments. The discovery of these services needs to be efficient and pervasive, based on the specific context of the city, and the integration of diverse providers. We identify a trade-off between accuracy and performance in the discovery of services in this scenario. Existing research has proposed solutions that focus either on semantic methods to improve accuracy with performance negatively affected, or vice versa. Additionally, the composition of services from different sources has not been explored in smart cities and large scenarios. We propose to address the trade-off by extending both how service information is organised, and the service discovery process. Service organisation uses urban context to spread service descriptions to the right urban-places; the service discovery process uses this model to forward requests where they are more likely to be solved. We simulate our model as a network of gateways that covers Dublin city center and manages services information. Results show that our model solves more requests than previous work in a smart city environment. In addition, response time keeps acceptable even when there are 100 thousand services.

Internet of Things (IoT) applications can be built from a number of heterogeneous services provided by a range of devices, which are potentially resource constrained and/or mobile. As these services and applications continue to be more widespread, a key research question is how to predict user-side quality of service (QoS), to ensure the optimal selection, composition and adaptation of IoT services. The exponential growth in the number of these services means that it is not practical to invoke all candidate services to test their QoS, especially during runtime service adaptation. QoS can vary by time and location, which makes it difficult for service providers to give accurate estimates of how the service will perform for users located in changing network topologies. We propose IoTPredict, a novel neighbourhood-based prediction approach for the IoT, which uses an alternative similarity computation mechanism. Our collaborative approach requires no additional invocation of services, which is a key requirement for resource constrained devices in the IoT. We evaluate our algorithm on a QoS dataset and show that it achieves higher QoS prediction accuracy than other state of the art approaches.

Mobile users can form a service-sharing community within a geographic area by using their mobile devices. Finding Quality of Service (QoS) optimal service compositions in such mobile environments is challenging because of the inherent dynamism in services deployed on mobile devices. Existing service composition proposals for mobile environments either use template-matching composition or require a-priori knowledge about the QoS objectives' weights, which limits the composition flexibility in such environments. This paper introduces a QoS optimisation mechanism for planning-based service composition in mobile environments, where mobile software agents use stigmergic coordination to iteratively explore parts of the distributed service composition space to approximate a set of QoS optimal configurations. We present a mechanism that minimises the exploration of previously identified non-optimal solutions to encourage exploration of different parts of the service space. We evaluate the performance of the proposed approach and compare the results with a baseline variant, a Dijkstra-based, a Greedy and a Random approach. The results show that the proposed approach can achieve higher utility compared to the evaluated proposals at the cost of increased overhead.

This paper discusses the challenges in developing an IoT platform for registering, discovering and composing heterogeneous services from multiple provider types, viz., Wireless Sensor Networks (WSNs), Web Service Providers (WSPs), and Autonomous Service Providers (ASPs), without human intervention. The platform executes a service composition in a decentralised fashion, with a mechanism to detect service provider failure and fallback to previously discovered services to complete a service composition flow. We comment on technical and scientific challenges involved in managing these heterogeneous, autonomous, and resilient IoT services.

Recently, there have been a large number of proposals for IoT middleware solutions. In addition, a few recent studies have surveyed and qualitatively evaluated these IoT middleware proposals against functional and non-functional features. A quantitative evaluation is also needed to complement these existing qualitative studies and provide a more in-depth perspective of the state of the art. This paper presents a quantitative evaluation of 4 representative proposals: OpenIoT, CHOReOS, LinkSmart and UBIWARE. The evaluation results, based on a small real-life scenario, show that research is needed in the area of autonomous and scalable service registration, discovery and composition, heterogeneity, and interoperability of IoT middlewares.

The Internet of Things (IoT) envisages a future in which digital and physical things or objects (e.g., smartphones, TVs, cars) can be connected by means of suitable information and communication technologies, to enable a range of applications and services. The IoT's characteristics, including an ultra-large-scale network of things, device and network level heterogeneity, and large numbers of events generated spontaneously by these things, will make development of the diverse applications and services a very challenging task. In general, middleware can ease a development process by integrating heterogeneous computing and communications devices, and supporting interoperability within the diverse applications and services. Recently, there have been a number of proposals for IoT middleware. These proposals mostly addressed wireless sensor networks (WSNs), a key component of IoT, but do not consider RF identification (RFID), machine-to-machine (M2M) communications, and supervisory control and data acquisition (SCADA), other three core elements in the IoT vision. In this paper, we outline a set of requirements for IoT middleware, and present a comprehensive review of the existing middleware solutions against those requirements. In addition, open research issues, challenges, and future research directions are highlighted.