Applying Cluster Computing to Enable a Large-scale Smart Grid Stability Monitoring Application

Author(s):  
John Interrante ◽  
Kareem S. Aggour
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mahdi Saadatmand ◽  
Gevork B. Gharehpetian ◽  
Pierluigi Siano ◽  
Hassan Haes Alhelou
Keyword(s):  

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 991
Author(s):  
Peidong Zhu ◽  
Peng Xun ◽  
Yifan Hu ◽  
Yinqiao Xiong

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.


2014 ◽  
Vol 47 (3) ◽  
pp. 1879-1885 ◽  
Author(s):  
Samira Rahnama ◽  
S. Ehsan Shafiei ◽  
Jakob Stoustrup ◽  
Henrik Rasmussen ◽  
Jan Bendtsen
Keyword(s):  

2016 ◽  
Vol 13 (10) ◽  
pp. 119-136 ◽  
Author(s):  
Yan Wang ◽  
Qingxu Deng ◽  
Genghao Liu ◽  
Xiuping Hao ◽  
Baoyan Song

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Weihua Huang

Multiuser fair sharing of clusters is a classic problem in cluster construction. However, the cluster computing system for hybrid big data applications has the characteristics of heterogeneous requirements, which makes more and more cluster resource managers support fine-grained multidimensional learning resource management. In this context, it is oriented to multiusers of multidimensional learning resources. Shared clusters have become a new topic. A single consideration of a fair-shared cluster will result in a huge waste of resources in the context of discrete and dynamic resource allocation. Fairness and efficiency of cluster resource sharing for multidimensional learning resources are equally important. This paper studies big data processing technology and representative systems and analyzes multidimensional analysis and performance optimization technology. This article discusses the importance of discrete multidimensional learning resource allocation optimization in dynamic scenarios. At the same time, in view of the fact that most of the resources of the big data application cluster system are supplied to large jobs that account for a small proportion of job submissions, while the small jobs that account for a large proportion only use the characteristics of a small part of the system’s resources, the expected residual multidimensionality of large-scale work is proposed. The server with the least learning resources is allocated first, and only fair strategies are considered for small assignments. The topic index is distributed and stored on the system to realize the parallel processing of search to improve the efficiency of search processing. The effectiveness of RDIBT is verified through experimental simulation. The results show that RDIBT has higher performance than LSII index technology in index creation speed and search response speed. In addition, RDIBT can also ensure the scalability of the index system.


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