Browsing by Author "Zhao, Bo"

The goal of range analysis is to determine a program variable's minimum and maximum value at runtime and it becomes more complex to calculate the range space when the variable is a pointer. In this paper, we analyzed the optimization problem of data transmission in parallelization for heterogeneous structure and distributed memory structure. On the basis of symbolic range analysis, we proposed a demand-driven pointer-range analysis technique for data transmission optimization. At first, we introduced the analysis framework of this technique and the representations of pointer range. Then we described the algorithm of the demand-driven pointer-range analysis. The experimental results with various benchmarks demonstrate that our technique can bring about significant performance improvement.

Live Virtual Machine (VM) migration within a data center is an important technology for cloud management, and has brought many benefits to both cloud providers and users. With the development of cloud computing, across-data-center VM migration is also desired. Normally, there is no shared storage system between data centers, hence the storage data (disk image) of a VM will be migrated to the destination data center as well. However, the slow network speed of the Internet and the comparatively large size of VM disk image make VM storage data migration become a bottleneck for live VM migration across data centers. In this paper, based on a detailed analysis of VM deployment models and the nature of VM image data, we design and implement a new migration system, called CBase. The key concept of CBase is a newly introduced central base image repository for reliable and efficient data sharing between VMs and data centers. With this central repository, further performance optimizations to VM storage data migration are made possible. Two migration mechanisms (data deduplication and Peer-to-Peer (P2P) file sharing) are utilized to accelerate base image migration, and a strategy is designed to elevate the synchronization of newly-written disk blocks. The results from an extensive experiment show that CBase significantly outperforms existing migration mechanisms under different conditions regarding total migration time and total network traffic. In particular, CBase with data deduplication is better than P2P file sharing for base image migration in our experimental environment.

Live Virtual Machine (VM) migration is an important technology for cloud management and guaranteeing a high service quality. However, existing studies are mainly focusing on improving migration performance but without much consideration of user's requirements. Also, the migration process is transparent to cloud managers and users. Once a migration is initialized, it is uncontrollable as desired. In this paper, we quantitatively analyze the migration process of pre-copy and figure out the relationship between migration performances and dynamic influence factors. Then, by taking user's requirements into consideration, a migration control algorithm is proposed through tuning the dynamic factors. The migration convergence problem of pre-copy is solved as well with the performance control algorithm. We base our study on Xen platform, and the experimental results verify the efficiency of our migration control algorithm.

Merchants sometimes run big promotions (e.g., discounts or cash coupons) on particular dates (e.g., Boxing-day Sales, "Black Friday" or "Double 11 (Nov 11th)", in order to attract a large number of new buyers. Unfortunately, many of the attracted buyers are one-time deal hunters, and these promotions may have little long lasting impact on sales. To alleviate this problem, it is important for merchants to identify who can be converted into repeated buyers. By targeting on these potential loyal customers, merchants can greatly reduce the promotion cost and enhance the return on investment (ROI). It is well known that in the field of online advertising, customer targeting is extremely challenging, especially for fresh buyers. With the long-term user behavior log accumulated by Tmall.com, we get a set of merchants and their corresponding new buyers acquired during the promotion on the "Double 11" day. Our goal is to predict which new buyers for given merchants will become loyal customers in the future. In other words, we need to predict the probability that these new buyers would purchase items from the same merchants again within 6 months. A data set containing around 200k users is given for training, while the other of similar size for testing. We extracted as many features as possible and find the key features to train our models. We proposed merged model of different classification models and merged lightGBM model with different parameter sets. The experimental results show that our merged models can bring about great performance improvements comparing with the original models.

With the popularity of mobile devices (such as smartphones and tablets) and the development of the Internet of Things, mobile edge computing is envisioned as a promising approach to improving the computation capabilities and energy efficiencies of mobile devices. It deploys cloud data centers at the edge of the network to lower service latency. To satisfy the high latency requirement of mobile applications, virtual machines (VMs) have to be correspondingly migrated between edge cloud data centers because of user mobility. In this paper, we try to minimize the network overhead resulting from constantly migrating a VM to cater for the movement of its user. First, we elaborate on two simple migration algorithms (M‐All and M‐Edge), and then, two optimized algorithms are designed by classifying user mobilities into two categories (certain and uncertain moving trajectories). Specifically, a weight‐based algorithm (M‐Weight) and a mobility prediction–based heuristic algorithm (M‐Predict) are proposed for the two types of user mobilities, respectively. Numerical results demonstrate that the two optimized algorithms can significantly lower the network overhead of user mobility–induced VM migration in mobile edge computing environments.