In order to solve the problem that Scale Invariant Feature Transform (SIFT) algorithm has low matching accuracy and long time consuming in image matching, an improved image matching algorithm based on grid motion statistical feature, namely RANSAC-GMS, was proposed. Firstly, the image was pre-matched by Oriented FAST and Rotated BRIEF (ORB) algorithm and Grid-based Motion Statistics (GMS) was used to support the estimator to distinguish the correct matching points from the wrong matching points. Then, an improved RANdom SAmple Consensus (RANSAC) algorithm was used to filter the feature points according to the distance similarity between the matching points, and an evaluation function was used to reorganize the filtered new datasets to eliminate the mismatching points. The experiments were carried out on Oxford standard image library and images taken in reality. Experimental results show that the average matching accuracy of the proposed algorithm in image matching is over 91%. Compared with algorithms such as GMS, SIFT and ORB, the near-scene matching accuracy and the far-scene matching accuracy of the proposed algorithm are improved by 16.15 percentage points and 3.56 percentage points respectively. The proposed algorithm can effectively eliminate mismatching points and achieve further improvement of image matching accuracy.

In the era of big data, data intensive computing always relies on distributed Heterogeneous Computing System (HCS), and an effective task scheduling method can improve the efficiency of a HCS. Based on a Directed Acyclic Graph (DAG) model, a task scheduling algorithm for heterogeneous computing named HSFT (Heterogeneous scheduling algorithm with immediate Successor Finish Time) was proposed. In the heterogeneous environment, especially when the computation cost and communication cost vary largely, the balance between them was considered and a more reasonable method was adopted, the product of the computation cost standard deviation and mean value was taken as the computation weight, and the ratio between the out degree communication cost weight and out degree was taken as the communication weight. Furthermore, based on the consideration of Earliest Finish Time (EFT), the immediate Successor Finish Time (SFT) was used for processor selection strategy. The experimental results on randomly generated DAGs show that the proposed algorithm performs better than HEFT (Heterogeneous Earliest Finish Time), SDBATS (Standard Deviation-Based Algorithm for Task Scheduling) and PEFT (Predict Earliest Finish Time) in terms of makespan, schedule length ratio, and efficiency without increasing time complexity.

Private desktop cloud is widely used in various kinds of application scenarios, such as centralized computing, centralized management, and remote office. Existing private clouds are mostly based on OpenStack operating system, however, OpenStack encounters a problem that it takes a long time to start a virtual machine when it is used in private desktop cloud, so it is unable to meet the real-time requirement in some application scenarios. In this paper, using template image strategy and network attached storage strategy for the cloud storage layer, Instant-Start Virtual Machine (ISVM) private desktop cloud architecture was proposed. ISVM private desktop cloud architecture consisted of cloud management layer, cloud storage layer, and cloud service layer. Through testing and analyzing, the time of starting virtual machines in ISVM architecture is about one percent of the time in OpenStack architecture, reaching millisecond order of magnitude and meeting the real-time requirement of some application scenarios.

Multiple Directed Acyclic Graphic (DAG) scheduling algorithms are supposed to take many factors into account, such as execution time, communication overhead, cost and fairness of all DAG. Therefore, in order to increase fairness and reduce cost, a new scheduling strategy CAFS (Communication Aware Fair Scheduling), based on CA-DAG (Communication Aware-DAG), was proposed. Also, a BD (Backward Difference) rule was introduced to optimize finish time of all DAGs. CAFS is consisted of two parts: the pre-scheduling part adopts CACO (Communication Aware Cost Optimization) to optimize the total cost, and utilizes the classical fairness algorithm to decide the sequence for scheduling. Based on the sequence the second part schedules all the DAGs using BD rule to reduce the finish time. The simulation results show that CAFS can reduce the finish time without increasing cost and guarantee the fairness, and the average execution time has been reduced by 19.82%.

Because of the defects of ADO.NET, the general database engine DbEngine was proposed, in which communication and operation were encapsulated. It provided simple and easy interface for application, so reduced the possibility of making errors and helped some persons who never used ADO.NET and SQL to develop excellent program.

Because of the defects of ADO.NET, the general database engine DbEngine was proposed, in which communication and operation were encapsulated. It provided simple and easy interface for application, so reduced the possibility of making errors and helped some persons who never used ADO.NET and SQL to develop excellent program.