Recent advances in smart connected vehicles and Intelligent Transportation Systems (ITS) are based upon the capture and processing of large amounts of sensor data. Modern vehicles contain many internal sensors to monitor a wide range of mechanical and electrical systems and the move to semi-autonomous vehicles adds outward looking sensors such as cameras, lidar, and radar. ITS is starting to connect existing sensors such as road cameras, traffic density sensors, traffic speed sensors, emergency vehicle, and public transport transponders. This disparate range of data is then processed to produce a fused situation awareness of the road network and used to provide real-time management, with much of the decision making automated. Road networks have quiet periods followed by peak traffic periods and cloud computing can provide a good solution for dealing with peaks by providing offloading of processing and scaling-up as required, but in some situations latency to traditional cloud data centres is too high or bandwidth is too constrained. Cloud computing at the edge of the network, close to the vehicle and ITS sensor, can provide a solution for latency and bandwidth constraints but the high mobility of vehicles and heterogeneity of infrastructure still needs to be addressed. This paper surveys the literature for cloud computing use with ITS and connected vehicles and provides taxonomies for that plus their use cases. We finish by identifying where further research is needed in order to enable vehicles and ITS to use edge cloud computing in a fully managed and automated way. We surveyed 496 papers covering a seven-year timespan with the first paper appearing in 2013 and ending at the conclusion of 2019.

Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud, due to long distances. Fog/edge computing is a new model for analysing and acting on time-sensitive data, adjacent to where it is produced. Further, cloud services provided by large companies such as Google, can also be localised to improve response time and service agility. This is accomplished through deploying small-scale datacentres in various locations, where needed in proximity of users; and connected to a centralised cloud that establish a multi-access edge computing (MEC). The MEC setup involves three parties, i.e. service-providers (IaaS), application-providers (SaaS), network-providers (NaaS); which might have different goals, therefore, making resource management difficult. Unlike existing literature, we consider resource management with-respect-to all parties; and suggest game-theoretic resource management techniques to minimise infrastructure energy consumption and costs while ensuring applications' performance. Our empirical evaluation, using Google's workload traces, suggests that our approach could reduce up to 11.95% energy consumption, and ~17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up-to 20.27% energy bills and NaaS can increase their costs-savings up-to 18.52% as compared to other methods.

Multiaccess edge computing systems (MECs) bring the capabilities of cloud computing closer to the radio access network (RAN), in the context of 4G and 5G telecommunication systems, and converge with existing radio access technologies like satellite or WiFi. An MEC is a cloud server that runs at the mobile network’s edge and is installed and executed using virtual machines (VMs), containers, and/or functions. A cloudlet is similar to an MEC that consists of many servers which provide real-time, low-latency, computing services to connected users in close proximity. In connected vehicles, services may be provisioned from the cloud or edge that will be running users’ applications. As a result, when users travel across many MECs, it will be necessary to transfer their applications in a transparent manner so that performance and connectivity are not negatively affected. In this article, we propose an effective strategy for migrating connected users’ services from one edge to another or, more likely, to a remote cloud in an MEC. A mathematical model is presented to estimate the expected times to allocate and migrate services. Our evaluations, based on real workload traces and mobility patterns, suggest that the proposed strategy “ApMove” migrates connected services while ensuring their performance (~0.004%–2.99% loss), reduced runtimes, therefore, users’ costs (~4.3%–11.63%), and minimizing the response time (~7.45%–9.04%). Furthermore, approximately 17.39% migrations are avoided. We also study the impacts of variations in the car’s speed and network transfer rates on service migration durations, latencies, and service execution times.

The performance of Cloud systems is a key concern, but has typically been assessed by the comparison of relatively few Cloud systems, and often on the basis of just one or two features of performance. In this paper, we discuss the evaluation of four different Infrastructure as a Service (IaaS) Cloud systems -- from Amazon, Rackspace, and IBM -- alongside a private Cloud installation of OpenStack, using a set of five so-called micro-benchmarks to address key aspects of such systems. The results from our evaluation are offered on a web portal with dynamic data visualization. We find that there is not only variability in performance by provider, but also variability, which can be substantial, in the performance of virtual resources that are apparently of the same specification. On this basis, we can suggest that performance-based pricing schemes would seem to be more appropriate than fixed-price schemes, and this would offer much greater potential for the Cloud Economy.

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ∼9.61% more energy and ∼20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs.