Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.

As clouds increase in size and as machines of different types are added to the infrastructure in order to maximize performance and power efficiency, heterogeneous clouds are being created. However, exploiting different architectures poses significant challenges. To efficiently access heterogeneous resources and, at the same time, to exploit these resources to reduce application development effort, to make optimisations easier and to simplify service deployment, requires a re-evaluation of our approach to service delivery. We propose a novel cloud management and delivery architecture based on the principles of self-organisation and self-management that shifts the deployment and optimisation effort from the consumer to the software stack running on the cloud infrastructure. Our goal is to address inefficient use of resources and consequently to deliver savings to the cloud provider and consumer in terms of reduced power consumption and improved service delivery, with hyperscale systems particularly in mind. The framework is general but also endeavours to enable cloud services for high performance computing. Infrastructure-as-a-Service provision is the primary use case, however, we posit that genomics, oil and gas exploration, and ray tracing are three downstream use cases that will benefit from the proposed architecture.

The popularity and commercial use of cloud computing has prompted an increased concern among cloud service providers for energy efficiency while still maintaining quality of service. One of the key techniques used for the efficient use of cloud server resources is virtual machine placement. This work introduces a precise VM placement algorithm that ensures energy efficiency and also prevents Service Level Agreement (SLA) violation. The mathematical model of the algorithm is supported by a sophisticated data analytic system implemented as a service. The precision of the algorithm is achieved by allowing each individual VM to build its own data model on demand over an appropriate time horizon. Thus the data model can reflect accurately the characteristics of resource usage of the VM. The algorithm can communicate synchronously or asynchronously with the data analytic service which is deployed as a cloud-based solution. In the experiments, several advanced data modelling and use forecasting techniques were evaluated. Results from simulation-based experiments show that the VM placement algorithm (supported by the data analytic service) can effectively reduce power consumption, the number of VM migrations, and prevent SLA violation, it also compares very favourably with other placement algorithms.

Driven by the successful service model and growing demand, cloud computing has evolved from a moderate-sized data center consisting of homogeneous resources to a heterogeneous hyper-scale computing ecosystem. This evolution has made the modern cloud environment increasingly complex. Large-scale empirical studies of essential concepts such as resource allocation, virtual machine migration, and operational cost reduction have typically been conducted using simulations. This paper presents an agent-based cloud simulation model for resource management. The focus is on how service placement strategies, service migration, and server consolidation affect the overall performance of homogeneous and heterogeneous clouds, in terms of energy consumption, resource utilization, and violation of service-level agreements. The main cloud elements are modeled as autonomous agents whose properties are encapsulated. The complex relationships between components are realized through asynchronous agent-to-agent interactions. Operating states and statistics are displayed in real time. In the evaluation, the efficiency of the simulator is studied empirically. The performance of various resource management algorithms is assessed using statistical methods, and the accuracy of server energy consumption models is examined. The results show that agent-based models can accurately reflect cloud status at a fine-grained level.

A substantial amount of information in companies and on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. Compression as an effective means to reduce data size has been employed by many emerging data analytic platforms, whom the main purpose of data compression is to save storage space and reduce data transmission cost over the network. Since general purpose compression methods endeavour to achieve higher compression ratios by leveraging data transformation techniques and contextual data, this context-dependency forces the access to the compressed data to be sequential. Processing such compressed data in parallel, such as desirable in a distributed environment, is extremely challenging. This work proposes techniques for more efficient textual big data analysis with an emphasis on content-aware compression schemes suitable for the Hadoop analytic platform. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of public and private real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.