Cache Performance Optimization of QoC Framework

Abstract

The main aim of this paper is based on the cache performance test of the QoC: quality of experience framework for cloud computing on the server. QoC framework is based on the server-side design and implementation of the use of hierarchical architecture. Reverse proxy technology is used to build a server cluster, which is composed of front-end access layer to achieve the server for load balancing, improve the performance of the system and the use of built-in distributed cache server. The cluster consists of the cache acceleration layer, which reduces the load of the backend database. The second database server cluster, which is constructed by the database master and slave synchronization technology, forms the data storage layer, which realizes the database read and writes separation and data redundancy. The server-side hierarchical architecture improves the performance and stability of the entire system, and has a high degree of scalability, laying a solid foundation for future expansion of system business logic and increases user volume. This paper presents new cache replacement algorithm for inconsistent video file size and then analyzes the specific needs for the multi-terminal type of QoC framework, and gives the client and server-side outline design; it describes the implementation details of the client and the server-side and finally the whole system of detailed functional and performance testing.

Advanced Mechanism to Achieve QoS and Profit Maximization of Brokers in Cloud Computing

“…The quality dissatisfaction can have a negative impact on the profit maximization of the cloud service provider. In [15] we study how customer satisfaction can have a huge impact in deriving an optimal configuration for profit maximization. More and more people are drawn towards the concept of cloud computing because of its numerous advantages.…”

Highlight the Features of AWS, GCP and Microsoft Azure that Have an Impact when Choosing a Cloud Service Provider

“…Archived information is put in storage on a minor-cost level of storage, helping as an approach to decrease the most important storage consumption plus associated expenses. A significant characteristic of a company's data archiving approach is to record that one data and recognize what data is an applicant for archiving [57]. Below is a table of important storage features concerning CSPs.…”

An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment

“…Recurrent Neural Networks (RNNs) is a popular scheme used in natural language processing. Even though, practically, RNNs are affected by the problem of vanishing/exploding gradient, and their small structure still provides efficacy and helps reduce the over fitting problem [24] [30]. In this technical work, a recurrent neural network is suggested to be built with multiple semantically heterogeneous embedding's inside a self-training architecture.…”