Dynamic and Adaptive Monitoring and Analysis for Many-task Ensemble Computing

Author(s):  
Shantenu Jha ◽  
Allen D. Malony
Keyword(s):  
Author(s):  
Сергій Олегович Ареф’єв

The paper covers the current issues of counteraction to constantly arising crisis phenomena in the process of using the enterprise potential. For about 15 years the efforts to comply with legislation have been steadily rising, and more and more emphasis is paid to various aspects of corporate social responsibility. There is a wide range of activities, such as increasing employee awareness, creating a management system to prevent abrupt changeover, a solid corporate structure and timely disclosure of information, as well as managing the organization as an integration of its potentials. Adaptive monitoring is viewed as a critical component in finding and controlling the reserves for further utilization of enterprise resources in the context of developing its long-term strategies. Building the subsystems for change management strategies can form the basis for creating anti-crisis potential. However, there is another barrier to the process of adaptation which is a vulnerable internal environment. Apparently, the goals of the chosen strategies in each of the business areas are not always announced, and this can increase the entropy level within the enterprise, creating threats and hazards that give rise to crisis phenomena. From a dynamic perception, adaptive management concept involves the construction of a decomposition of its possible implementation scenarios subject to the type of threats to enterprise performance and characteristics of its potentials. The search for the development models that can retain the enterprise resources is a fundamental challenge for its operation in the future. It is about facilitating the transition from product economy to the system economy, from a dissipative approach to resources to an adaptive management practices, to a cultural leap towards economic and environmental sustainability that should affect the entire society, from strengthening of the territory and cooperation among all stakeholders to gain the resource utilization efficiency beyond renewable energy, starting with raw materials and local waste management to create an integrated technology network and from a number of integrated technologies, from deindustrialized territories reconstruction towards new relationships between agriculture, industry and academia, conducting local case studies to test the effects of innovations, thus boosting the process of transforming the research results into new pilot projects.


2020 ◽  
Vol 111 (1-2) ◽  
pp. 341-357
Author(s):  
Y. Zhang ◽  
X. Beudaert ◽  
J. Argandoña ◽  
S. Ratchev ◽  
J. Munoa

Abstract Cyber-physical production systems (CPPS) are mechatronic systems monitored and controlled by software brains and digital information. Despite its fast development along with the advancement of Industry 4.0 paradigms, an adaptive monitoring system remains challenging when considering integration with traditional manufacturing factories. In this paper, a failure predictive tool is developed and implemented. The predictive mechanism, underpinned by a hybrid model of the dynamic principal component analysis and the gradient boosting decision trees, is capable of anticipating the production stop before one occurs. The proposed methodology is implemented and experimented on a repetitive milling process hosted in a real-world CPPS hub. The online testing results have shown the accuracy of the predicted production failures using the proposed predictive tool is as high as 73% measured by the AUC score.


2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Paulo A. L. Rego ◽  
Fernando A. M. Trinta ◽  
Masum Z. Hasan ◽  
Jose N. de Souza

Mobile cloud computing is an approach for mobile devices with processing and storage limitations to take advantage of remote resources that assist in performing computationally intensive or data-intensive tasks. The migration of tasks or data is commonly referred to as offloading, and its proper use can bring benefits such as performance improvement or reduced power consumption on mobile devices. In this paper, we face three challenges for any offloading solution: the decision of when and where to perform offloading, the decision of which metrics must be monitored by the offloading system, and the support for user’s mobility in a hybrid environment composed of cloudlets and public cloud instances. We introduce novel approaches based on machine learning and software-defined networking techniques for handling these challenges. In addition, we present details of our offloading system and the experiments conducted to assess the proposed approaches.


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