Earticle

Both performance and capacity of the main memory are the key to the computer systems in current architecture. DRAM, which is the most used main memory, can’t extend in capacity for its high energy consumption and repeatedly refresh. Fortunately, some new memory mediums, such as phase-change memory (PRAM), are used to replace traditional DRAM memory. These new memories have many advantages, like low energy consumption, without repeatedly refresh, high density storage, and so on. Therefore, these memories are promising. However, their low read/write performance and limited life are restricted the replacement process. In current time, hybrid memory, which consists of both PRAM and DRAM, is a good choice. In this way, the memory capacity can be extended. So, the most challenge for the hybrid memory is the performance. In this paper, we propose a dynamic hybrid LRU caching scheme (DH-LRU) for the last level cache in PRAM/DRAM hybrid main memory to improve the main memory performance. Compared with traditional cache policies, like LRU, FIFO, RANDOM, CFLRU, our DH-LRU improves performance by 4.6%. Moreover, energy consumption of write and read operation can be reduced up to 88.2%.

The current investment and management mode of power grid construction project is inadequate to the whole optimization, the overall process of the project has obvious time-phased, therefore, the paper strongly advocates to carry out life-cycle management in power grid construction project, and divides the construction project into five stages as preliminary planning, design and planning, construction and operation, scrap and disposal, and evaluate and control the cost of operating stage, investment benefit of decision stage, design stage management and risk of construction stage, and sets up performance evaluation model of power grid project, in order to provide certain practical significance for the establishment of life-cycle management mode of power grid enterprises.

In order to study the effect of the internet of things (IOT) on the architecture of logistics service supply chain (LSSC), this paper summarizes the application of IOT in related fields on the basis of the theories of IOT, analyzes the effect of IOT on logistics/service flow, information flow and fund flow in LSSC and the effect on the structure of LSSC, builds the architecture of LSSC based on IOT, and finally forecasts the application prospect of IOT in LSSC.

Genetic algorithm (GA) is a population-based approach for heuristic search in optimi- zation problems based on the principle of biologic evolution and natural selection. In this paper, we present a hybrid adaptive genetic algorithm with chaos searching technique for numerical optimization. On the one hand, two sets of crossover and mutation rates are for- mulated to automatically maintain the balance between exploration and exploitation during the genetic search process. On the other hand, the chaos searching technique is introduced into the adaptive genetic algorithm based on the decision mechanism for premature conver- gence adopted in this paper, whose main goal is to avoid being trapped into the local opti- mum. In addition, half of the total evolutionary generation is utilized as one of the decision conditions so as to speed up the convergent process. To validate the effectiveness and efficiency of the proposed approach, we apply it to four benchmark functions obtained from the literature, and the experimental results show that the proposed algorithm can find global optimal or the closer-to-optimal solutions and have faster search speed as well as higher convergence rate.

With the rapid growth of internet finance, the credit assessing is becoming more and more important. An effective classification model will help financial institutions gain more profits and reduce the loss of bad debts. In this paper, we propose a new Support Vector Machine (SVM) based ensemble model (SVM-BRS) to address the issue of credit analysis. The model combines random subspace strategy and boosting strategy, which encourages diversity. SVM is considered as a state-of-art model to solve classification problem. Therefore, the proposed model has the potential to generate more accuracy classification. Accordingly, this study compares the ANN, LR, SVM, Bagging SVM, Boosting SVM techniques and experience shows that the new SVM based ensemble model can be used as an alternative method for credit assessing.

Cloud Computing is one of the major trends in today’s information technology and the most likely research area. Cloud Computing is a model of sharing resources, software, information. The user access the cloud via internet and all the resources are open to all. Thus, cloud security is now a challenging problem for the cloud service providers. Cryptography is a good solution of the cloud security and several research works are published to ensure secure cloud using cryptographic tools. This paper proposes an authentic communication between a customer and cloud service provider using encryption, decryption and message digests.

According to the traditional stabilized biconjugate gradient algorithm (BiCGSTAB) deficiency in data locality, this paper proposed a QTBiCGSTAB algorithm whose core idea is that recursively divides sparse matrix with quarter tree into sub-matrix and reorders them, to improve the hit ratio of cache and enhance the algorithm’s efficiency. And the idea is good for algorithm being parallized, that is proved by the numerical experiments later. It mainly shows, firstly, QTBiCGSTAB algorithm is more efficiency than BiCGSTAB, and the speedup would reach 1:330. The target division length would be influnced on the algorithm’s performance; Secondly, for large linear system, parallized QTBiCGSTAB is more efficiency than serial’s.