On-Line Repetitive Dynamical Hierarchical Optimization of Large-Scale Systems

1995 ◽  
Vol 28 (10) ◽  
pp. 209-214
Author(s):  
S.S.S. Hassoun ◽  
L.J.C. Woolliscroft
Keyword(s):  
Large Scale ◽  
Hierarchical Optimization ◽  
Large Scale Systems ◽  
On Line

Role of Cloud Computing in On-Line Social Networking and In-Depth Analysis of Cloud-Sim Simulator

2019 ◽  
pp. 34-46 ◽  
Author(s):  
Suresh Annamalai ◽  
Udendhran R.
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Social Networking ◽  
Large Scale ◽  
System Capacity ◽  
Large Scale Systems ◽  
Depth Analysis ◽  
On Line ◽  
Effective Cost

In this chapter, the authors introduced cloudsim simulator and cloud computing role in online social networking. The communication incurred by other activities such as management jobs is negligible. Social relationships can be established for numerous reasons. For example, family members, colleagues, or classmates often have strong social interactions resulting in large communication load. Cloud computing as well as social network-based applications will become dominant in many aspects of life in the next few decades. The performance of such large-scale systems is characterized by system capacity in terms of number of users/clients, flexibility, scalability, and effective cost of operation, etc. Popular social networks have hundreds of millions of users and continue to grow.

scholarly journals Fault Detection for Large Scale Systems Using Dynamic Principal Components Analysis with Adaptation

2007 ◽  
Vol 2 (2) ◽  
pp. 185 ◽  
Author(s):  
Jesús Mina ◽  
Cristina Verde
Keyword(s):  
Fault Detection ◽  
Large Scale ◽  
Principal Component ◽  
False Alarms ◽  
Large Scale Systems ◽  
Linear Dynamic Systems ◽  
Dynamic Principal Component Analysis ◽  
On Line ◽  
Components Analysis ◽  
Monitoring Phase

The Dynamic Principal Component Analysis is an adequate tool for the monitoring of large scale systems based on the model of multivariate historical data under the assumption of stationarity, however, false alarms occur for non-stationary new observations during the monitoring phase. In order to reduce the false alarms rate, this paper extends the DPCA based monitoring for non-stationary data of linear dynamic systems, including an on-line means estimator to standardize new observations according to the estimated means. The effectiveness of the proposed methodology is evaluated for fault detection in a interconnected tanks system.

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