Thread-Level Speculation (TLS) technology can tap the parallel execution potential of programs and improve the utilization of multi-core resources. However, the current TACLeBench kernel benchmarks are not effectively analyzed in TLS parallelization. In response to this problem, the loop-level speculative execution analysis scheme and analysis tool were designed. With 7 representative TACLeBench kernel benchmarks selected, firstly, the initialization analysis was performed to the programs, the program hot fragments were selected to insert the loop identifier. Then, the cross-compilation was performed to these fragments, the program speculative thread and the memory address related data were recorded, and the maximun potential of the loop-level parallelism was analyzed. Finally, the program runtime characteristics (thread granularity, parallelizable coverage, dependency characteristics) and the impacts of the source code on the speedup ratio were comprehensively discussed. Experimental results show that:1) this type of programs is suitable for TLS acceleration, compared with serial execution results, under the loop structure speculative execution, the speedup ratios for most programs are above 2, and the highest speedup ratio in them can reach 20.79; 2) by using TLS to accelerate the TACLeBench kernel programs, most applications can effectively make use of 4-core to 16-core computing resources.

Under the condition of being confronted with highly concurrent requests, the existing Web services would bring about the increase of the response time, even the problem that server goes down. To solve this problem, a kind of distributed self-elasticity architecture for the Web system named ECAP (self-Elasticity Cloud Application Platform) was proposed based on cloud computing. The architecture built on the Infrastructure as a Service (IaaS) platform of OpenStack. It combined Platform as a Service (PaaS) platform of Cloudify to realize the ECAP. In addition, it realized the fuzzy analytic hierarchy scheduling method by building the fuzzy matrix in the scale values of virtual machine resource template. At last, the test applications were uploaded in the cloud platform, and the test analysis was given by using the tool of pressure test. The experimental result shows that ECAP performs better in the average response time and the load performance than that of the common application server.

Concerning the large-scale concurrent video stream scheduling problem of low resource utilization and load imbalance under cloud environment, a Video-on-Demand (VOD) scheduling policy based on Ant Colony Optimization (ACO) algorithm named VodAco was proposed. The correlation of video stream expected performance and server idle performance was analyzed, and a mathematical model was built based on the definition of comprehensive matching degree, then ACO method was adopted to hunt the best scheduling schemes. The contrast experiments with Round Robin (RR) and greedy schemes were tested on CloudSim. The experimental results show that the proposed policy has more obvious advantages in task completion time, platform resources occupancy and node load balancing performance.