Multi-provider cloud computing network infrastructure optimization

Abstract

h i g h l i g h t s• Byte-hit rate and hit rate could be optimal simultaneously in a nonuniform cost model. • i-Cloud outperformed popular LRU, GDSF and LFUDA schemes in a nonuniform cost environment.• i-Cloud's performances were stable and close to those of infinite cache size. • Window size had small performance effect when relative cache sizes were big.• Accounting data-out charge rates improved all performance aspects at small cache sizes. a b s t r a c tCloud-adopting enterprises have been increasingly employing multiple cloud providers concurrently, for example, to consume unique services and to mitigate data lock-in risk. As a consequence, the enterprises must be able to address contrasting quality-of-service degrees offered by the different providers. This paper presents an intelligent cloud cache eviction approach, namely i-Cloud, as the core component of a client-side cloud cache. i-Cloud is capable of reducing public cloud data-out expenses, improving cloud network scalability and lowering cloud service access latencies specifically in multi-provider cloud environments. Trace-driven simulations have shown that i-Cloud outperformed well-known approaches in all performance metrics. In addition, i-Cloud is not only able to achieve optimal performances in all metrics simultaneously but also delivered relatively stable performances across all performance metrics. The results have also indicated that taking the nonuniformity of data-out charge rates into cache eviction decisions improved caching performances in all metrics.

Research on the Prediction of the E-commerce Profit Based on the Improved Parallel PSO-LSSVM Algorithm in Cloud Computing Environment

“…When the two extremes are found, each particle adjusts its velocity and position iteratively according to the formula 1 and 2. w is the inertia weight. 1 c and 2 c are acceleration constant. 1 r and 2 r are the random numbers from 0 to 1.…”

“…(12) Where, 1 c and 2 c are the random number in [0,1]. w is the inertia weight, and w is also the key parameter in the particle swarm optimization algorithm.…”

“…min w is the minimum value of inertia weight. 1 c and 2 c are the self-learning factor and the social-learning factors. In this paper, we adopt the dynamic adjustment method to obtain the values of 1 c and 2 c .…”

“…The continuous development of the Internet has prompted the emergence of the cloud computing technology [1]. The cloud computing not only completely changes the business model and the way that people work, but also brings the development rapidly of the e-commerce industry to a new opportunity [2][3].…”

“…Wang Yi researched on the customer behavior and the sales forecasting model in the electronic commerce [1]. The author thought that the website's visit and the purchase transaction behavior could produce the massive data in the electronic commerce environment.…”