An Intelligent and Cost-Efficient Resource Consolidation Algorithm in Nanoscale Computing Environments
| Autor: | ALam Han, JongBeom Lim, Tae-Young Kim, Me-Suk Kim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Cloud resources
edge cloud Computer science Cloud computing 02 engineering and technology lcsh:Technology Turnaround time Scheduling (computing) lcsh:Chemistry nanoscale computing Fog computing Server 0202 electrical engineering electronic engineering information engineering General Materials Science Hidden Markov model lcsh:QH301-705.5 Instrumentation Fluid Flow and Transfer Processes Cost efficiency lcsh:T business.industry Process Chemistry and Technology General Engineering 020206 networking & telecommunications lcsh:QC1-999 Computer Science Applications resource consolidation lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 020201 artificial intelligence & image processing fog computing lcsh:Engineering (General). Civil engineering (General) business Algorithm lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 10, Iss 6494, p 6494 (2020) Applied Sciences Volume 10 Issue 18 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10186494 |
| Popis: | Because the Internet of things (IoT) and fog computing are prevalent, an efficient resource consolidation scheme in nanoscale computing environments is urgently needed. In nanoscale environments, a great many small devices collaborate to achieve a predefined goal. The representative case would be the edge cloud, where small computing servers are deployed close to the cloud users to enhance the responsiveness and reduce turnaround time. In this paper, we propose an intelligent and cost-efficient resource consolidation algorithm in nanoscale computing environments. The proposed algorithm is designed to predict nanoscale devices&rsquo scheduling decisions and perform the resource consolidation that reconfigures cloud resources dynamically when needed without interrupting and disconnecting the cloud user. Because of the large number of nanoscale devices in the system, we developed an efficient resource consolidation algorithm in terms of complexity and employed the hidden Markov model to predict the devices&rsquo scheduling decision. The performance evaluation shows that our resource consolidation algorithm is effective for predicting the devices&rsquo scheduling decisions and efficiency in terms of overhead cost and complexity. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|