James Hardy

Dot-base62x: building a compact and user-friendly text representation scheme of ipv6 addresses for cloud computing

Cloud computing has dramatically reshaped the whole IT industry in recent years. With the transition from IPv4 to IPv6, services running in Cloud computing will face problems associated with IPv6 addressing: the notation is too long (39 bytes), there are too many variants of a single IPv6 address and a potential conflict may exist with conventional http_URL notation caused by the use of the colon (:). This paper proposes a new scheme to represent an IPv6 address with a shorter, more compact notation (27 bytes), without variants or conflicts with http_URL. The proposal is known as dot-base62x as it is an IPv6 address with Base62x and uses the well-known period (or dot) as a group delimiter instead of the colon. The relative merits and demerits of other works that predate this paper have been reviewed and critically evaluated. Cloud computing, as a continuously emerging mainstream of network-based applications, is likely to be a forerunner in the use of IPv6 as the base protocol. As a result, Cloud computing will benefit most from the new, compact and user-friendly textual representation of IPv6 address proposed by this paper.

Software Aging in Virtualized Environments: Detection and Prediction

Software aging has been cited in many scenarios including Operating System, Web Servers, Real-time Systems. However, few studies have been conducted in long running virtualized environments where more and more software is being delivered as a service. Furthermore, state-of-the-art methods lack the ability to deal with miscellaneous upper applications and underlying systems transparently in virtualized scenarios. In this paper, we detect aging phenomenon by conducting experiments in physical and virtual machines and identify the differences between the two, and propose a feature code-based methodology for failure prediction through system call, then implement a prototype in virtual machine manager layer to predict failure time and rejuvenate transparently, which is suitable in virtualized scenarios. The evaluation shows the prediction deviation against reality is less than 10%.

An efficient algorithm for partially matched services in internet of services

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This large-scale information exchange results in new interactions between things and people. Unlike traditional web services, internet of services is highly dynamic and continuously changing due to constant degrade, vanish and possibly reappear of the devices, this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides’ leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. This paper proposed an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this paper. Experimental results from real-world services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process.

Event Detection and User Interest Discovering in Social Media Data Streams

Social media plays an increasingly important role in people’s life. Microblogging is a form of social media which allows people to share and disseminate real-life events. Broadcasting events in microblogging networks can be an effective method of creating awareness, divulging important information, and so on. However, many existing approaches at dissecting the information content primarily discuss the event detection model and ignore the user interest which can be discovered during event evolution. This leads to difficulty in tracking the most important events as they evolve including identifying the influential spreaders. There is further complication given that the influential spreaders interests will also change during event evolution. The influential spreaders play a key role in event evolution and this has been largely ignored in traditional event detection methods. To this end, we propose a user-interest model-based event evolution model, named the hot event evolution model. This model not only considers the user interest distribution but also uses the short text data in the social network to model the posts and the recommend methods to discover the user interests. This can resolve the problem of data sparsity, as exemplified by many existing event detection methods, and improve the accuracy of event detection. A hot event automatic filtering algorithm is initially applied to remove the influence of general events, improving the quality and efficiency of mining the event. Then, an automatic topic clustering algorithm is applied to arrange the short texts into clusters with similar topics. An improved user-interest model is proposed to combine the short texts of each cluster into a long text document simplifying the determination of the overall topic in relation to the interest distribution of each user during the evolution of important events. Finally, a novel cosine measure-based event similarity detection method is used to assess correlation between events, thereby detecting the process of event evolution. The experimental results on a real Twitter data set demonstrate the efficiency and accuracy of our proposed model for both event detection and user interest discovery during the evolution of hot events.

An Efficient Algorithm for Partially Matched Web Services Based on Consumer's QoS Requirements

With the fast growing number of web services and consumers, selecting a suitable service for the consumer is becoming a crucial issue. Quality of Service (QoS) plays a significant role for both service and consumer in determining requirements. A number of models have been proposed for service selection by considering QoS parameters. In this paper we propose a service selection algorithm by considering multiple consumer criteria and allowing partially matched services to be counted as a candidate for the selection process. Based on consumer criteria, the algorithm returns a list of recommended services. Experimental results from real world web services shows our algorithm has a significant improvement in the quality of recommended web services.

Interest-Aware Content Discovery in Peer-to-Peer Social Networks

With the increasing popularity and rapid development of Online Social Networks (OSNs), OSNs not only bring fundamental changes to information and communication technologies, but also make an extensive and profound impact on all aspects of our social life. Efficient content discovery is a fundamental challenge for large-scale distributed OSNs. However, the similarity between social networks and online social networks leads us to believe that the existing social theories are useful for improving the performance of social content discovery in online social networks. In this article, we propose an interest-aware social-like peer-to-peer (IASLP) model for social content discovery in OSNs by mimicking ten different social theories and strategies. In the IASLP network, network nodes with similar interests can meet, help each other, and co-operate autonomously to identify useful contents. The presented model has been evaluated and simulated in a dynamic environment with an evolving network. The experimental results show that the recall of IASLP is 20% higher than the existing method SESD while the overhead is 10% lower. The IASLP can generate higher flexibility and adaptability and achieve better performance than the existing methods.

Analysis, Modelling and Characterisation of Zombie Servers in Large-Scale Cloud Datacentres

Cloud datacentres are acknowledged as being massive energy consumers, which may have significant environment impacts. Service providers have an ethical responsibility to reduce the environmental impact of server resources and a simultaneous and complementary commercial desire to reduce energy costs. Zombie servers in the datacenters are one of the primary sources of undesirable energy expenditures by incurring idle resources during task execution. This paper investigates the cause, impact and energy-related implications of zombie servers. Important outcomes of this paper are the characterization of the diversity among the workload behaviors in resource consumption and the quantification of the presence of idle CPU and memory resources during task execution causing server zombieness. The undesirable power consumption of zombie servers is determined based on the profiles of currently available servers and their corresponding environmental implications are illustrated in this paper. Empirical analysis shows that cloud workloads are highly heterogeneous in resource consumption pattern and CPU resources may display 75.6% of idleness relative to their allocated level, while memory is 25.5% idle. The report concludes that significant reductions in power consumption and CO2 emission can be achieved by provisioning a realistic level of resources to servers, which are scaled to suit the anticipated workloads.

Context-Aware Service Discovery and Selection in Decentralized Environments

With the increasing use of web services in everyday tasks, we are entering an era of Internet of Services (IoS). Service discovery and selection has become critical issue in the area of web services. Traditional service discovery approaches are often supported by centralized registries that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, in this paper we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes, previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of the best available web service by taking into account the QoS properties of the web services. Experimental results were based on real world semantic web service dataset, the results showed that the approach achieved better performance and efficiency in both discovery and selection processes.

Reducing Vehicular Traffic Congestion Using Available Forward Road Capacity Detection

Road congestion is a complex, significant and continuing problem. This project is concerned with urban road systems and traffic light control. The objective is to create a novel traffic controller which uses available forward road capacity (AFRC) detection and locality awareness in conjunction with existing backpressure methods as a means to reduce the duration of congestion. The controller is an ongoing development, the initial version considers only local AFRC but is being actively developed to communicate to multiple downstream controllers and to upstream traffic guidance. Enhanced downstream communication will automatically identify a junction where congestion starts and actively postpone the onset, automatically identify congested region exits and actively resolve the congestion in a shorter time. The controller measures the degree of congestion and is capable of providing information to reduce the influx of traffic into an already congested region. The effectiveness of the controller is shown through simulation using commercial traffic simulation software tools. Initial results are very promising, delays have been reduced to less than 15% of comparative timed and VA systems, the performance improvement increases as the road becomes more congested. Consequences of reducing overall congestion duration include increased fuel efficiency, reduced fuel waste and a reduction in the negative effects on environment and health. The benefits of the system are equally applicable to all powered transport systems regardless of fuel source or control method including autonomous vehicles and vehicles equipped with v2x communication.

Applying Queueing Theory to the Design of a Traffic Light Controller

This case study explains some of the esoteric features of traffic light control and discusses existing systems before introducing a novel control system being developed as a research problem.