The result of automatic traffic-congestion detection method in bad weather is inaccurate. In response to this situation, we propose a detection method of traffic congestion based on histogram equalisation and discrete-frame difference. This method uses discrete-frame difference algorithm to extract the images that have vehicle information firstly. Then, the method employs histogram equalisation algorithm to eliminate the noise of the vehicle images. We also propose a corresponding traffic congestion index statistical algorithm in this step. After that, this method recognises vehicles from the video and computes the traffic congestion index. Finally, the method transforms the dimension of traffic congestion index and obtains the state of traffic congestion. It is proved by experiments that the method increases the accuracy rate of automatic traffic congestion detection in bad weather. This method has a wonderful prospect in some areas where there is usually bad weather.

Energy efficiency is one of the biggest challenges of designing future heterogeneous multicore system, beyond performance, hereby, we propose an energy efficiency analytical model for heterogeneous multicore system based on the enhanced Amdahl's law. The model extends the traditional computing-centric model by considering the overhead of data preparation which potentially includes the overhead of communication, data transfer, synchronisation, etc. The analysis clearly shows that decreasing the overhead of data preparation is a promising approach to reach higher performance of computation and greater energy efficiency. Therefore, more informed trade-offs should be taken when we design a modern heterogeneous processor system within limited budget of energy.

For the failures of two-dimensional products affected by the calendar time and usage time, traditional one-dimensional preventive maintenance cannot satisfy the actual maintenance demand of two-dimensional products. Therefore, this paper proposes two-dimensional preventive maintenance strategy for two-dimensional products and makes decision for two-dimensional products through calendar time and usage time. Firstly, we establish failure rate function expression of two-dimensional products and analyse the process of two-dimensional preventive maintenance in detail, after proposing a cost model and availability model from the view of economy and mission in finite span. According to the cost and availability model, through examples, we derive the optimal two-dimensional age-replacement interval by numerical algorithm and particle swarm algorithm, and verify the model's applicability and validity through contrast and analysis. Lastly, we give a brief discussion of future research area about two-dimensional maintenance.

Reconfiguration is a powerful computational model in which the processors can be changed dynamically during the execution phase of the system. This paper presents dynamic reconfigurable register file allocation in hyper pipelined OR1200 (Open RISC) for single instruction multiple data (SIMD). The OR1200 instantly reconfigures the actual register file to the reconfigurable register file according to the requirement of the application. The unused general purpose registers obtained during the reconfiguration process can be used for hyper pipelining technique which improves overall performance of the single core processor system. Thus releasing the unused register reduces the power consumption and increases the execution speed of OR1200. This proposed reconfigurable technique is implemented using Verilog and it is tested using MediaBench multimedia benchmark dataset which ensures reduced register utilisation of 16.80% for multimedia dataset and power reduction up to 72.7% with reconfigurable modules. The proposed technique is configured in Virtex-6 field programmable gate array (FPGA) and results are analysed with the existing and proposed reconfigured OR1200.

The epidemic routing has been integrated into many applications, ranging from the worm propagation of online social networks to the message diffusion in offline physical systems. Modelling epidemic routing provides a baseline to evaluate system performance; it also becomes very desirable for engineers to have theoretical guidance before they deploy the real system. Early works analyse the dynamics of epidemic routing with the average contact rate, i.e., each node will encounter the same number of other nodes in a time slot. They neglect the status of encountered nodes (i.e., infected or susceptible), resulting in the defectiveness of existing models. In this paper, we observe that the infectivity of nodes has heterogeneity rather than homogeneity, two nodes with the same contact rate may behave different infectivities. Motivated by this observation, we first use infection rate to reflect the infectivity of infected nodes. We then model the epidemic routing with the average infection rate, instead of the contact rate. We finally compare our model with the existing works through theoretical analysis and simulations. The results show that our model has a closer match than those of the state-of-the-art works, which provides an upper bound on the number of infected nodes.

Energy efficiency has become an increasingly prominent issue in cloud computing. Task consolidation is an effective way to maximise utilisation of cloud computing resources and reduce energy consumption. Presented in this paper is an improved energy-aware task consolidation algorithm to optimise the scheduling of tasks in cloud computing data centres. Our algorithm was developed based on the linear relationship between energy consumption and the CPU utilisation. Instead of consolidating tasks to a service node until its CPU utilisation reaches 100%, our algorithm uses an optimal point, where the CPU utilisation of a service node reaches 70%, and ensures that every task is preferentially assigned to service nodes that satisfy the 70% CPU utilisation. Our experiments demonstrate that our algorithm can significantly reduce the energy consumption of cloud computing data centres without performance degradation while maintaining good performance compared to the energy-conscious task consolidation (ECTC) approach.

Cloud computing is a new computing model, which provides various services through networks. Cloud customers can obtain resource and service on demand. There are a large number of storage services that can be used to manage the needs of customers in the cloud computing environment. The cloud storage services can let users in a convenient and reliable way store and share data from anywhere, on any device, and at any time. However, a lot of resources and energies are wasted in cloud computing environment. Especially, there is huge waste when it is a low-IO demand cloud system. We seek to save the resource and save the hardware cost at the same time. Nowadays, the important problem is how to save the resources and energies. It is hard to handle it in a high-IO demand system. But we find a way to handle it in a low-IO system. Maximising resource utilisation is the most popular approach which saves the resources and energies in cloud computing. In this paper, we propose to maximise resource utilisation of storage in the cloud environment. We assign the data which is stored in chunk devices of cloud by our data placement algorithms. Our algorithms are running on a central 'decision centre' which holds all devices' status of cloud. As the experiment has shown, minimising the disk usage can reduce the waste of the resources and energies. Finally, our experiment shows that our approach is efficient.

Processing large volumes of data to drive their core business has been the primary objective of many firms and scientific applications in these days. Cloud computing being a large-scale distributed computing paradigm can be used to cater for the needs of data intensive applications. There are various approaches for managing the workload on a data intensive cloud. Live migration of a virtual machine is the most prominent paradigm. Existing approaches to live migration use network attached storage where just the run time state needs to be transferred. Live migration of virtual machines with local persistent storage has been shown to have performance advantages like security, availability and privacy. This paper presents an optimised approach for migration of a virtual machine along with its local storage by considering the locality of storage access. Count map combined with a restricted block transfer mechanism is used to minimise the downtime and overhead. The solution proposed is tested by various parameters like bandwidth, write access patterns and threshold. Results show the improvement in downtime and reduction in overhead.

Overcoming performance anomalies without server downtimes is of the utmost importance in videostreaming services. These services have large user abandonment costs when failures occur after users watch a significant part of a video. Reboot is the most popular and effective technique for overcoming performance anomalies but requires long server warm-up times. During warm-up, the server is unavailable or provides limited capacity to process end-users' requests. This paper presents a recovery technique for performance anomalies in HTTP streaming services, which relies on container-based virtualisation to implement an efficient multiphase server reboot technique that minimises the service downtime. The recovery process includes analysis of variance of request-response times to delimit the server warm-up period, after which the server is running at its full capacity. Experimental results show that the virtual container recovery process completes in 72 seconds, which contrasts with the 434 seconds required for a full operating system recovery. Both recovery types generate service downtimes imperceptible to end-users.

Low cost broadband services used widely in the recent years increased the demand for multimedia applications. Many of these applications require different quality of service (QoS) in terms of throughput and delay. In the current resource constrained wireless networks, resource management plays a vital role. In order to handle the resources effectively and to increase the QoS, proper packet scheduling algorithms need to be developed. Low latency queuing (LLQ) is a packet scheduling algorithm which combines strict priority queuing (SPQ) to class-based weighted fair queuing (CBWFQ). In this paper, an enhanced low latency queuing (ELLQ) algorithm is proposed in which an additional SPQ is introduced along with the existing SPQ. Further, the QoS is improved by integrating congestion avoidance algorithm with ELLQ. Simulation results using the OPNET modeller show that the proposed algorithm outperforms the existing algorithm in terms of throughput and delay for the multimedia applications.