Cloud computing and datacentres provided by companies such as Google, Alibaba, Tencent, and Amazon Web Services still dominate datacentres industry from a business revenue perspective. However, imminent technologies and a variety of digital devices that form part of the Multi-access Edge Clouds (MECs) and Internet of Things (IoT), along with modular applications, are starting to take the central stage. In the MEC scenario, there are multiple providers such as networks, processing resources, and applications that are involved in service offerings and often have conflicting optimisation objectives. For example, the profit of the infrastructure providers would need more users, which can degrade the network performance. Therefore, to ensure service quality, all providers' goals should be kept in mind, in particular, when deciding placements. Game theory, particularly the Stackelberg framework, can be used to deal with such optimisation problems because it can model the conflicting objectives typical in MEC environments-such as communication between service providers (leaders) and users (followers); therefore, enabling efficient service placement decisions. In this paper, we model the optimisation problem as a Stackelberg game and propose a bidding strategy that ensures the objectives of all providers are met. Our evaluations and results, based on real workload traces, suggest that the proposed strategy runs services while ensuring their expected levels of performance (∼0.018%-3.27% loss), energy efficiency (∼14.43%-34.79%), reduced runtimes (or at least comparable to the no migration strategy) therefore, users' costs (∼7.88%-15.89%), and minimizing the response time (∼6.37%-8.18%). Furthermore, approximately 16.93% migrations are avoided.

Continuous-discrete state space models (CDSSMs) enable modelling of irregular time series by learning the underlying continuous-time dynamics with noisy measurements obtained at discrete timestamps. Recent studies have shown remarkable performance involving CDSSMs with neural networks. However, challenges still remain in the application of general non-linear non-Gaussian CDSSMs to irregular time series. To address these challenges, we propose a new method, named continuous-discrete differentiable particle filters (CD-DPFs), to model probabilistic irregular time series. Representing the latent state probability density function by a Gaussian mixture model (GMM), an adaptive Gaussian sum particle filter is applied into CDSSMs, and the weights of the GMM are optimised adaptively through solving a convex optimisation problem by direct matching the Fokker-Planck-Kolmogorov equation. Performance is evaluated on a stochastic Lorenz 63 model, a highly non-linear chaotic system. Compared with the state-of-the-art, the proposed method demonstrates significant improvement for forecasting whilst maintaining competitive performance on imputation.

Backfilling refers to the practice of allowing small jobs to be completed ahead of schedule as long as they do not cause the first job in the line to wait. Users are expected to offer estimates of how long jobs will take to complete in order to make these decisions possible, and these projections are often based on historical data. However, predictions are very hard and may not be accurate, particularly in cloud computing scenarios where jobs or applications run on Virtual Machines (VMs). In addition, scheduling and consolidation techniques can improve the energy efficiency and performance of applications. Consolidation involves VM migrations that can have a negative impact on workload performance and users’ costs. Backfilling can be used as an alternative technique for consolidation (short-term) and/or can be used along with consolidation (long-term). Backfilling methods are well-utilised in single computing systems, but are relatively unexplored in cloud resource allocation. A backfilling-based resource allocation and consolidation technique is proposed. Using real workloads from the Google cluster traces, we investigate the impact of backfilling on infrastructure energy efficiency and performance. For 12583 heterogeneous servers and approximately three million jobs that belong to three different applications, we observed that approximately 19% energy savings and 6% workload performance improvements are achievable using the backfilling approach. Furthermore, our evaluation suggests that using VM runtime as a criterion for the backfilling approach is approximately 3.56%–7.78% more energy and 1.91%–3.38% more performance efficient than using priority as a backfilling criterion.

Multi-access 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 paper, 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.

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$\sim$9.61% more energy and$\sim$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.

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.

This paper proposes a discrete time distributed state feedback controller design strategy for a homogenous vehicle platoon system with undirected network topology which is resilient to both external disturbances and random consecutive network packet drop. The system incorporates a distributed state feedback controller design by satisfying bounded$H_{\infty }$norm using Lyapunov-Krasovskii based linear matrix inequality (LMI) approach that ensures internal stability and performance. The effect of packet drops on internal stability in terms of stability margin are studied for a homogenous vehicle platoon system with undirected network topology and external disturbance. The variation of stability margin, representing absolute value of least stable close-loop pole, is also studied for two common undirected network topologies for vehicle platooning, i.e., bidirectional predecessor following (BPF) and bidirectional predecessor leader following (BPLF) topologies by varying platoon members, packet drop rates with number of contiguous packets dropped. Results demonstrate that the control strategy best satisfies the requirement of maintaining a desired inter-vehicular distance with constant spacing policy and leader trajectory using two network topologies: BPF and BPLF. We show how these topologies are robust in terms of ensuring internal stability and performance to maintain cooperative motion of vehicle platoon system with different number of followers, random multiple consecutive packet drops and external disturbance.

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 percent energy consumption, and$\sim$17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27 percent energy bills and NaaS can increase their costs-savings up to 18.52 percent as compared to other methods.

Infrastructure Clouds offer large scale resources for rent, which are typically shared with other users—unless you are willing to pay a premium for single tenancy (if available). There is no guarantee that your instances will run on separate hosts, and this can cause a range of issues when your instances are co-locating on the same host including: mutual performance degradation, exposure to underlying host failures, and increased threat surface area for host compromise. Determining when your instances are co-located is useful then, as a user can implement policies for host separation. Co-location methods to date have typically focused on identifying co-location with another user’s instance, as this is a prerequisite for targeted attacks on the Cloud. However, as providers update their environments these methods either no longer work, or have yet to be proven on the Public Cloud. Further, they are not suitable to the task of simply and quickly detecting co-location amongst a large number of instances. We propose a method suitable for Xen based Clouds which addresses this problem and demonstrate it on EC2—the largest Public Cloud Infrastructure.

Most current Infrastructure Clouds are built on shared tenancy architectures, with resources shared amongst \nlarge numbers of customers. However, multi tenancy can lead to performance issues (so-called “noisy \nneighbours”) and also brings potential for serious security breaches such as hypervisor breakouts. \nConsequently, there has been a focus in the literature on identifying co-locating instances that are being \naffected by noisy neighbours or suggesting that such instances are vulnerable to attack. However, there is \nlimited evidence of any such attacks in the wild. More beneficially, knowing that there is co-location \namongst your own Virtual Machine instances (siblings) can help to avoid being your own worst enemy: \navoiding your instances acting as your own noisy neighbours, building resilience through ensuring hostbased \nredundancy, and/or reducing exposure to a single compromised host. In this paper, we propose and \ndemonstrate a test to detect co-locating sibling instances on Xen-based Clouds, as could help address such \nneeds, and evaluate its efficacy on Amazon’s EC2.

Various papers have reported on the differential performance of virtual machine instances of the same type, and same supposed performance rating, in Public Infrastructure Clouds. It has been established that instance performance is determined in large part by the underlying hardware, and performance variation is due to the heterogeneous nature of large and growing Clouds. Currently, customers have limited ability to request performance levels, and can only identify the physical CPU backing an instance, and so associate CPU models with expected performance levels, once resources have been obtained. Little progress has been made to predict likely performance for instances on such Public Clouds. In this paper, we demonstrate how such performance predictions could be provided for, predicated on knowledge derived empirically from one common Public Infrastructure Cloud.

The increasing number of Public Clouds, the large and varied range of VMs they offer, and the provider specific terminology used for describing performance characteristics, makes price/performance comparisons difficult. Large performance variation can lead to Clouds being described as 'unreliable' and 'unpredictable'. The aim of this paper is to offer a basis for making probability-based performance predictions in Public (Infrastructure) Clouds, with Amazon's EC2 as our focus. We demonstrate how CPU model determines instance performance, show associations between instance classes and sets of CPU models, and determine class-to-model performance characteristics. We suggest that by knowing the proportion of CPU models backing specific instances, and in absence of provider knowledge or ability to specify model or performance, we can estimate the likelihood of a user obtaining particular models in respect to a request, and that this can be used to gauge likely price/performance.

We propose a method for improving object recognition in street scene images by identifying and filtering out background aspects. We analyse the semantic relationships between foreground and background objects and use the information obtained to remove areas of the image that are misclassified as foreground objects. We show that such background filtering improves the performance of four traditional object recognition methods by over 40%. Our method is independent of the recognition algorithms used for individual objects, and can be extended to generic object recognition in other environments by adapting other object models.

Grid computing continues to hold promise for the high-availability of a wide range of computational systems and techniques. It is suggested that grids will attain greater acceptance by a larger audience of commercial end-users if binding service level agreements (SLAs) are provided. We discuss grid commoditization, the use of grid technologies for financial risk analysis, and the potential formulation of the grid economy. Our aim is to predict availability and capability for risk analysis in and of grids. The considerations involved may be more widely applicable to the configuration and management of related architectures including those of P2P systems and clouds. In this paper, we explore and evaluate some of the factors involved in the automatic construction of SLAs for the grid economy.

In this paper we discuss the ease with which email can be used to breach confidence by the propagation of corporate secrets and intelligence, and propose an intelligent filtering system for outgoing emails aimed at preventing disclosures. We report on a number of experiments undertaken with a corpus of over half a million Enron emails and the use of a variety of techniques from the field of Corpus Linguistics for reducing the number of false alarms produced by naive keyword filtering systems, and discuss the results in detail. We also give due consideration to the danger of missing messages that should have been prevented from propagation.

In this paper we explore the distribution of training of self-organised maps (SOM) on Grid middleware. We propose a two-level architecture and discuss an experimental methodology comprising ensembles of SOMs distributed over a Grid with periodic averaging of weights. The purpose of the experiments is to begin to systematically assess the potential for reducing the overall time taken for training by a distributed training regime against the impact on precision. Several issues are considered: (i) the optimum number of ensembles; (ii) the impact of different types of training data; and (iii) the appropriate period of averaging. The proposed architecture has been evaluated in a Grid environment, with clock-time performance recorded.