提出了一个移动用户智能电网(Mobile User Smart Grid,MUSG)概念,给出了MUSG场景下的用电模式描述,并利用基于身份的密码技术给出了一种基于身份的MUSG认证方案。分析表明,利用该认证方案不仅可以有效解决智能电网中移动用户的认证问题,满足移动用户即时用电和户外用电的需求,并且在保障系统安全性的同时有效降低了系统的计算和通信开销,减轻了系统的负载压力,提高了系统的使用效率。这为智能电网业务由固定用户向移动用户过渡奠定了基础。
The archiving of Internet traffic is an essential function for retrospective network event analysis and forensic computer communication. The state-of-the-art approach for network monitoring and analysis involves storage and analysis of network flow statistic. However, this approach loses much valuable information within the Internet traffic. With the advancement of commodity hardware, in particular the volume of storage devices and the speed of interconnect technologies used in network adapter cards and multi-core processors, it is now possible to capture 10 Gbps and beyond real-time network traffic using a commodity computer, such as n2disk. Also with the advancement of distributed file system （such as Hadoop, ZFS, etc.） and open cloud computing platform （such as OpenStack, CloudStack, and Eucalyptus, etc.）, it is practical to store such large volume of traffic data and fully in-depth analyse the inside communication within an acceptable latency. In this paper, based on well- known TimeMachine, we present TIFAflow, the design and implementation of a novel system for archiving and querying network flows. Firstly, we enhance the traffic archiving system named TImemachine＋FAstbit （TIFA） with flow granularity, i.e., supply the system with flow table and flow module. Secondly, based on real network traces, we conduct performance comparison experiments of TIFAflow with other implementations such as common database solution, TimeMachine and TIFA system. Finally, based on comparison results, we demonstrate that TIFAflow has a higher performance improvement in storing and querying performance than TimeMachine and TIFA, both in time and space metrics.
This paper presents a novel trust model based on multiple decision factor theory （MDFT） and a trust routing algorithm based on MDFT to exactly evaluate routing node trust and establish a trustworthy routing path. MDFT integrates four dimensional trust decision factors including behavior, state, recommend and node liveness to realize an exactly finer-grained trust evaluation. On the basis of MDFT, a trust routing algorithm is presented and validated in open shortest path first （OSPF） protocol. Simulation resuRs show that the algorithm can reflect the routing node trust accurately and has better dynamic response ability. Under the circumstance of existing deceptive nodes, the algorithm has better anti-deception performance and higher attack node detection rate than conventional algorithm.
The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energy-proportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is service-oriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.
Collaborative filtering solves information overload problem by presenting personalized content to individual users based on their interests, which has been extensively applied in real-world recommender systems. As a class of simple but efficient collaborative filtering method, similarity based approaches make predictions by finding users with similar taste or items that have been similarly chosen. However, as the number of users or items grows rapidly, the traditional approach is suffering from the data sparsity problem. Inaccurate similarities derived from the sparse user-item associations would generate the inaccurate neighborhood for each user or item. Consequently, its poor recommendation drives us to propose a Threshold based Similarity Transitivity （TST） method in this paper. TST firstly filters out those inaccurate similarities by setting an intersection threshold and then replaces them with the transitivity similarity. Besides, the TST method is designed to be scalable with MapReduce framework based on cloud computing platform. We evaluate our algorithm on the public data set MovieLens and a real-world data set from AppChina （an Android application market） with several well-known metrics including precision, recall, coverage, and popularity. The experimental results demonstrate that TST copes well with the tradeoff between quality and quantity of similarity by setting an appropriate threshold. Moreover, we can experimentally find the optimal threshold which will be smaller as the data set becomes sparser. The experimental results also show that TST significantly outperforms the traditional approach even when the data becomes sparser.
