To improve the efficiency of resource access in Software as a Service (SaaS) applications, a load balancing strategy combined with the important features of SaaS service was proposed. Firstly, the load balancing strategy was proposed by combining multi-tenancy and high scalability in SaaS service based on the distribution of request and global and local scalability. Secondly, the load balancing strategy model was constructed and simulated by a Petri net. Finally, this strategy was compared with Round Robin (RR), stochastic algorithm and Improved Least-Connection Scheduling (ILCS) load balancing algorithm in response time and throughput. The experimental results show that the response time and throughput of the proposed strategy become stable and they are superior to the other three algorithms after the request rate reaches 500 per second.

Focusing on the issue that most research results on correlation between advertising phrases stay in the literal level, and can not exploit deep semantic information of the phrases, which limits the performance of the task, a novel method was proposed to calculate the correlation between the phrases by using deep learning technique. Recursive AutoEncoder (RAE) was developed to make full use of semantic information in the word order and phrase, which made the phrase vector contain more deep semantic information, and built the calculating method of correlation under the advertising situation. Specifically, for a given list of a few phrases, reconstruction error was produced by merging the adjacent two elements. Phrase tree, which similar to the Huffman tree, was produced by merging two elements with smallest reconstruction error in turn. Gradient descent and Cosine distance were used to minimize the reconstruction error of phrase tree and measure the correlation between the phrases respectively. The experimental results show that the contribution of the important phrases is increased in the representation of the final phrase vector by introducing weight information, and RAE is more suitable for phrase calculation. The proposed method increases the accuracy by 4.59% and 3.21% respectively compared with LDA (Latent Dirichlet Allocation) and BM25 algorithm under the same condition of 50% recall rate, which proves its effectiveness.

Concerning the problem that the development cycle of existing elliptic curve algorithm system level design is long and the performance-overhead indicators are not clear, a method of Hardware/Software (HW/SW) co-design based on Electronic System Level (ESL) was proposed. This method presented several HW/SW partitions by analyzing the theories and implementations of SM2 algorithm, and generated cycle-accurate models for HW modules with SystemC. Module and system verification were proposed to compare the executing cycle counts of HW/SW modules to obtain the best partition. Finally, the ESL models were converted to Rigister Transfer Level (RTL) models according to the CFG (Control Flow Graph) and DFG (Data Flow Graph) to perform logic synthesis and comparison. In the condition of 50 MHz,180 nm CMOS technology, when getting best performance,the execute time of point-multiply was 20 ms, with 83 000 gates and the power consuption was 2.23 mW. The experimental result shows that the system analysis is conducive to performance and resources evaluation, and has high applicability in encryption chip based on elliptic curve algorithm. The embedded SoC (System on Chip) based on this algorithm can choose appropriate architecture based on performance and resource constraints.

In the process of advertising on search engines, it needs to calculate the correlation between auction word (Bidword) and user's query (Query) in real time. Dynamic Term weight in advertisements and phrase commercial value assessment must be considered in relevant calculation. Thus, a phrase related calculation approach named ADPCB was proposed based on behavioral analysis and Continuous Bag-Of-Words (CBOW) model to deal with those problems. Firstly, this approach got vector of each Term by CBOW. Secondly, to analyze advertiser's behavior and construct a global empowerment tree about phrases, the phrase structure was analyzed to obtain dynamic Term weight. Finally the phrase distributed representation produced by Term weight and linear combination was applied to the related measurement between Bidword and Query. Experiments were conducted on 10000 pairs Query and Bidword (positive and negative ratio is 1∶〖KG-*2〗1) with editorial judgments by using Word2vec, ADPCB performed better than Term Frequency-Inverse Document Frequency (TF-IDF) which combined with CBOW; when the accuracy was 0.70, ADPCB got higher recall than that of Latent Dirichlet Allocation (LDA), BM25 (Best Match25) and TF-IDF. The experimental results and analysis show that ADPCB can recognize the commercial value quality of the phrase to reduce the quantity of advertising trigger of low commercial value Query, it can be used in real-time calculation scene.