Analysis and Design of Advance Scalable QoS Based Resource Provisioning Framework

In today’s growing cloud world, where users are continuously demanding a large number of services or resources at the same time, cloud providers aim to meet their needs while maintaining service quality, an ideal QoS-based resource provisioning is required. In the consideration of the quality-of-service parameters, it is essential to place a greater emphasis on the scalability attribute, which aids in the design of complex resource provisioning frameworks. This study aims to determine how much work is done in light of scalability as the most important QoS attribute. We first conducted a detailed survey on similar QoS-based resource provisioning proposed frameworks/techniques in this article, which discusses QoS parameters with increasingly growing cloud usage expectations. Second, this paper focuses on scalability as the main QOS characteristic, with types, issues, review questions and research gaps discussed in detail, revealing that less work has been performed thus far. We will try to address scalability and resource provisioning problems with our proposed advance scalable QoS-based resource provisioning framework by integrating new modules resource scheduler, load balancer, resource tracker, and cloud user budget tracker in the resource provisioning process. Cloud providers can easily achieve scalability of resources while performing resource provisioning by integrating the working specialty of these sub modules.

Evolution of the HEPS Jupyter-based remote data analysis System

High Energy Photon Source(HEPS) Experiment is expected to produce large amount of data and have diverse computing requirements for data analysis. Generally, scientists need to spend several days to setup their experimental environment, which greatly reduce the scientists’ work efficiency. In response to the above problems, we introduce a remote data analysis system for HEPS. The system provides users a web-based interactive interface based Jupyter, which makes scientists are able to process data analysis anytime and anywhere. Particularly, we discuss the system architecture as well as the key points of this system. A solution of managing and scheduling heterogeneous computing resources (CPU and GPU) is proposed, which adopts Kubernetes to achieve centralized heterogeneous resources management and resource expansion on demand. An improved Kubernetes resource scheduler is discussed, which dispatches upper applications to nodes combining with the computing cluster status. The system can transparently and quickly deploy the data analysis environment for users in seconds and reach the maximum resource utilization. We also introduce an automated deployment solution to improve the work efficiency of developers and help deploy multidisciplinary applications faster and automatically. A unified certification is illustrated to make sure the security of remote data access and data analysis. Finally, we will show the running status of the system.

Optimal Resource Scheduler for LTE A based Networks

Long Term Evolution (LTE) may be a commonplace for prime rate radio transmission for cellular telephone and knowledge terminal. It is a complex technology that provides an accumulated network capability and speed by employing a totally special wireless interface beside the counter work enhancements. In the proposed method we deal with the setback of bandwidth administration in LTE-A systems and suggest valuable and profitable solutions to improve the quality of support in these networks. This paper presents an Optimal Resource Schedule which enhances the system throughput of LTE A based networks