As a "killer" application in cloud computing, cloud gaming is leading the revolution of way of gaming. However, the high latency between the cloud and end devices hurts user experience. Aiming at the problem, a low latency cloud gaming system deployed on edge nodes, called Gaming@Edge, was proposed based on edge computing concept. To reduce the overhead of edge nodes for improving the concurrency, a cloud gaming running mechanism based on compressed graphics streaming, named GSGOD (Graphics Stream based Game-on-Demand), was implemented in Gaming@Edge system. The logic computing and rendering in the game running were separated and a computing fusion of edge nodes and end devices was built by GSGOD. Moreover, the network data transmission and latency were optimized through the mechanisms such as data caching, instruction pipeline processing and lazy object updating in GSGOD. The experimental results show that Gaming@Edge can reduce average network latency by 74% and increase concurrency of game instances by 4.3 times compared to traditional cloud gaming system.

To solve the difficulty of attribute correlation in the process of simulating table data, an H model was proposed for describing not-temporal attribute correlation in table data. Firstly, the key attributes of the evaluation subject and the evaluated subject were extracted from the data set, by the twofold frequency statistics, four relationships of the key attributes were obtained. Then, the Maximum Information Coefficient (MIC) of each relationship was calculated to evaluate the correlation of each relationship, and each relationship was fitted by the Stretched Exponential (SE) distribution. Finally, the data scales of the evaluation subject and the evaluated subject were set. According to the result of fitting, the activity of the evaluation subject was calculated, and the popularity of the evaluated subject was calculated. H model was obtained through the association that was established by equal sum of activity and popularity. The experimental results show that H model can effectively describe the correlation characteristics of the non-temporal attributes in real data sets.

The software system running on the server cluster needs large-scale data sets of Web log to meet the performance test requirement, but the existing simulation generation algorithm cannot meet the requirements due to the single model. Aiming at this problem, a new multi-fractal Web log simulation generation algorithm based on alpha stable process was proposed. Firstly, the self-similarity of Web log was described by alpha stable process in Long Range Dependence (LRD). Secondly, the multi-fractal of Web log was described by binomial- b model in Short Range Dependence (SRD). Finally, the model of long range dependence and the model of short range dependence were integrated into the improved ON/OFF framework. Compared with the single model, the parameters of the proposed algorithm has clear physical meaning equipped with good performance of self-similarity and multi-fractal. The experimental results show that the proposed algorithm can accurately simulate the real Web log and be effectively applied in Web log simulation generation with large-scale data sets.

Focusing on the issue of improving the efficiency of session sequence modeling in the anomaly detection analysis of big data platform, a session identification algorithm based on Dynamic Adjustive Interval Time threShold of adjacent requests (DAITS) was proposed. Firstly, the factor of website pages and the average factor of users access time to the page were combined. Then, the appropriate weighting factor was used to dynamically adjust the time threshold. Finally, the session was divided according to whether the time threshold was exceeded. The experimental results show that compared with the traditional methods of using fixed thresholds, the precision of session identification was increased by 14.8% and the recall was increased by 13.2%; compared with the existing methods with dynamic adjustive thresholds, the precision of session identification was increased by 6.2% and the recall was increased by 3.2%.

When the existing simulation generation algorithm uses the distribution of the static model to generate a Web log, there is a big difference with real data. In order to solve the problem, a new algorithm of Web Log Simulation Generation based on user interest migration (WLSG) was proposed. Firstly, the relationship between Web log and time was modeled. Secondly, the migration of user interest was simulated when the user accessed to the file in different time. Finally, it was also simulated that the user adaptively access to the file which he was most interested in at the current moment. Compared with the distribution of the existing static model, the proposed algorithm had significantly improved the self-similarity by about 2.86% on average. The experimental results show that, the proposed algorithm can well simulate Web log by user interest in migration to change user access sequence, which is capable of being effectively applied in the Web log simulation generation.

Due to the problem that the nodes having the same distance but different position in the complex environment, brings shortage to accuracy and stability of indoor positioning, a new indoor positioning algorithm with Dynamic Environment Attenuation Factor (DEAF) was proposed. This algorithm built a DEAF model and redefined the way to assume the value. In this algorithm, particle filtering method was firstly used to smooth the Received Signal Strength Indication (RSSI); then, the DEAF model was used to calculate the estimation distance of the node; finally, the trilateration was used to get the position of the target node. Comparative experiments had been done using several filtering models, and the results show that this dynamic environment attenuation factor model combined with particle filtering can resolve the problem of the environment difference very well. This algorithm reduces the mean error to about 0.68 m, and the result has higher positioning accuracy and good stability.

Concerning the poor generalization ability (the recommended performance for new users and items) and low predictive accuracy of Probabilistic Matrix Factorization (PMF) in recommendation system, a new algorithm of Probabilistic Matrix Factorization algorithm based on AdaBoost (AdaBoostPMF) was proposed. Firstly, the initial weight for each sample was assigned. Secondly, the feature vectors of users and items were learned by each round of PMF stochastic gradient descent method and the global mean and standard deviation of the prediction error were calculated. The sample weights were adaptively adjusted by using AdaBoost from the a global perspective, which made the proposed algorithm pay more attention to training those samples with the larger prediction error than others. Finally, the sample weights were assigned to predictive error, which found the more appropriate optimum direction for feature vectors of users and items. Compared with traditional PMF algorithm, the proposed AdaBoostPMF algorithm could significantly improve the prediction precision by about 2.5% on average. The experimental results show that, the proposed algorithm can better fit the user feature vector and the item feature vector and improve the prediction accuracy by weighting the samples with larger prediction error.The proposed algorithm can be effectively applied to the personalized recommendation.

The traditional graph-based recommendation algorithm neglects the combined time factor which results in the poor recommendation quality. In order to solve this problem, a personalized recommendation algorithm integrating roulette walk and combined time effect was proposed. Based on the user-item bipartite graph, the algorithm introduced attenuation function to quantize combined time factor as association probability of the nodes; Then roulette selection model was utilized to select the next target node according to those associated probability of the nodes skillfully; Finally, the top-N recommendation for each user was provided. The experimental results show that the improved algorithm is better in terms of precision, recall and coverage index, compared with the conventional PersonalRank random-walk algorithm.

In order to solve the problem of high-precise indoor positioning calculation using received signal strength, a novel WMKF (Kalman Filtering and Weighted Median) positioning algorithm was proposed. The algorithm was different from previous indoor localization algorithms. Firstly, Kalman filter method was used to smooth random error, and weighted median method was made to reduce the influence of gross error, then the log distance path loss model was used to obtain the decline curve and calculate the estimated distance. Finally, the centroid method was used to get the position of the target node. The experimental results show that, this WMKF algorithm initially improve that the poor stability of positioning in a relatively complex environment, and effectively enhanced the positioning accuracy, making the accuracy between 0.81m to 1m.

In recommendation system, it is difficult to predict the behavior of users on items and give the accurate recommendation. In order to improve the accuracy of recommendation system, the recommendation potential was introduced and a novel personalized hybrid recommendation model fused with recommendation potential was proposed. Firstly, the trend momentum was calculated according to the visits of items in recent short time and long time; then, the current recommendation potential was calculated utilizing trend momentum; finally, the hybrid recommendation model was achieved according to the fusion of recommendation potential and personalized recommendation model. The experimental results show that the personalized trend prediction fused with recommendation potential can improve the accuracy of recommendation system in a large scale.

In a Virtual Machine (VM) computing environment, it is difficult to monitor and allocate the VM's memory in real-time. To overcome these shortcomings, a real-time method of monitoring and adjusting memory for Xen virtual machine called Xen Memory Monitor and Control (XMMC) was proposed and implemented. This method used hypercall of Xen, which could not only real-time monitor the VM's memory usage, but also dynamically real-time allocated the VM's memory by demand. The experimental results show that XMMC only causes a very small performance loss, less than 5%, to VM's applications. It can real-time monitor and adjust on demand VM's memory resource occupations, which provides convenience for the management of multiple virtual machines.

Because of its unique advantage of information propagation, the online social network has been a popular social communication platform. In view of the characteristics of the form of information propagation and the dynamics theory of infectious diseases, this paper put forward the model of information propagation through online social network. The model considered the influence of different users' behaviors on the transmission mechanism, set up the evolution equations of different user nodes, simulated the process of information propagation, and analyzed the behavior characteristics of the different types of users and main factors that influenced the information propagation. The experimental results show that different types of users have special behavior rules in the process of information propagation, i.e., information cannot be transported endlessly, and be reached at a stationary state, and the larger the spread coefficient or immune coefficient is, the faster it reached the stationary state.