The design and implementation of resource monitoring for cloud computing service platform

2013 ◽  
Author(s):  
Lei Xiaojiang ◽  
Shang Yanlei
Keyword(s):  
Cloud Computing ◽  
Resource Monitoring ◽  
Service Platform ◽  
Design And Implementation ◽  
Cloud Computing Service

Design and Implementation of a Prototype Cloud Video Surveillance System

2014 ◽  
Vol 18 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Yong-Hua Xiong ◽  
◽  
Shao-Yun Wan ◽  
Yong He ◽  
Dan Su
Keyword(s):  
Cloud Computing ◽  
Video Surveillance ◽  
Surveillance System ◽  
Distributed Storage ◽  
Video Data ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Campus Network ◽  
Design And Implementation ◽  
Cloud Computing Service

Cloud-based video surveillance systems, as a new cloud computing service model, are an emerging research topic, both at home and abroad. Current research is mainly focused on exploring applications of the system. This paper proposes a design and implementation method for cloud-based video surveillance systems using the characteristics of cloud computing, such as parallel computing, large storage space, and easy expandability. The system architecture and function modules are built, and a prototype cloud-based video surveillance system is established in a campus network using key technologies, including virtual machine task access control, video-data distributed storage, and database-active communicationmethods. Using the system, the user is able to place a webcam in a location that requires monitoring so that video surveillance can be achieved, and video data can be viewed through a browser. The system has the following advantages: low investment and maintenance cost, high portability, easily extendable, superior data security, and excellent sharing. As a private cloud server in the campus network, the system is able to not only provide convenient video surveillance services, but it can also be an excellent practical experimental platform for cloud computing-related research, which carries outstanding application value.

An Adaptive Load Balancing Method of Adjustment Based on Cloud Computing Resources

2013 ◽  
Vol 385-386 ◽  
pp. 1708-1712
Author(s):  
Xiao Ping Jiang ◽  
Teng Jiang ◽  
Tao Zhang ◽  
Cheng Hua Li
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Utilization Rate ◽  
Computing Technology ◽  
Service Platform ◽  
Technology System ◽  
Cluster Architecture ◽  
Cloud Computing Service

By combining LVS cluster architecture and could computing technology, system architecture of the cloud computing service platform is proposed. Cloud computing technology is suitable to support large-scale applications with flash crowds by support elastic amounts of bandwidth and storage resource etc. But traditional algorithms of load balancing provided by LVS are unsuitable for the proposed service platform, because these algorithms are designed for static server resource provided by traditional cluster technology. Taking both the overall utilization rate of server resources and the active connections of the server into counter, an adaptive adjustable load balancing algorithms (Least Comprehensive Utilization and Connection Scheduling algorithm, called LUCU) is proposed in this paper. According the utilization of cloud resource and the users demand, automatic switching between Round Robin (RR) algorithm and LUCU algorithm is achieved. When the cloud capacities are not able to meet the instantaneous demands, LUCU is chosen instead of RR. The proposed platform and algorithm are verified and evaluated using large-scare simulation experiments. The test results show that the equilibrium load is nearly achieved by adopting the proposed algorithms.

C-MAS: The Cloud Mutual Authentication Scheme

2013 ◽  
Vol 756-759 ◽  
pp. 3209-3214
Author(s):  
Zhen Peng Liu ◽  
Feng Long Wu ◽  
Kai Yu Shang ◽  
Wen Lei Chai
Keyword(s):  
Cloud Computing ◽  
Trusted Computing ◽  
Mutual Authentication ◽  
Computing Time ◽  
Cloud Service ◽  
Authentication Scheme ◽  
Session Key ◽  
Service Platform ◽  
Cloud Computing Environment ◽  
Cloud Computing Service

A cloud mutual authentication scheme (C-MAS) is proposed to solve the problem of authentication between user and cloud computing server. Trusted computing technology and traditional smart card methods are used in cloud computing service platform. The scheme completes the authentication of both sides in cloud computing, generates the session key according consulting, at the same time, verifies the credibility of cloud service platform. Analysis shows that our scheme can resist various kinds of possible attacks, so it is therefore more secure than other schemes. And the computing time meet the requirements of cloud computing environment.

scholarly journals DisOpenFaaS: A Distributed Function-as-a-Service Platform

2019 ◽  
Author(s):  
Lucas Vieira ◽  
Adbys Vasconcelos ◽  
Ítalo Batista ◽  
Rodolfo A. M. Silva ◽  
Francisco Brasileiro
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Platform ◽  
Service Model ◽  
Service Platform ◽  
Hybrid Clouds ◽  
Cloud Computing Service ◽  
Low Costs ◽  
Auto Scaling ◽  
Distributed Infrastructure

The adoption of cloud computing is increasing due to low costs of infrastructure, as well as having virtually infinite resources available for demand based scaling. The increasing interest in this topic, there is a continuous search for better ways to manage such infrastructures. One of the most recent steps was the development of Function-as-a-Service (FaaS). FaaS is a cloud computing service model where developers can deploy functions to a cloud platform and have them executed based on the triggering of events, or by making HTTP(S) requests. We propose an architecture for deploying FaaS platforms in hybrid clouds that can be composed by multiple cloud providers. This architecture enable privately deployed FaaS platforms to perform auto-scaling of virtual machines in a distributed infrastructure, while considering the scenario where the users of such platform are scattered around the globe. This allows the execution of requests in servers geographically located as close as possible from the client.

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