Efficient Resource Allocation for Time-Sensitive IoT Applications in Cloud and Fog Environments
Lina Husain Kazem

Msr. Lina Husain Kazem, PhD. Fahad Aldosariو PhD. Wadee Alhalabi College of Computer and Information Systems Umm Alqura university.
Manuscript received on 6 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 2356-2363 | Volume-8 Issue-3 September 2019 | Retrieval Number: B1568078219/2019©BEIESP | DOI: 10.35940/ijrte.B1568.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: INowadays, with the quick development of internet and cloud technologies, a big number of physical objects are linked to the Internet and every day, more objects are connected to the Internet. It provides great benefits that lead to a significant improvement in the quality of our daily life. Examples include: Smart City, Smart Homes, Autonomous Driving Cars or Airplanes and Health Monitoring Systems. On the other hand, Cloud Computing provides to the IoT systems a series of services such as data computing, processing or storage, analysis and securing. It is estimated that by the year 2025, approximately trillion IoT devices will be used. As a result, a huge amount of data is going to be generated. In addition, in order to efficiently and accurately work, there are situations where IoT applications (such as Self Driving, Health Monitoring, etc.) require quick responses. In this context, the traditional Cloud Computing systems will have difficulties in handling and providing services. To balance this scenario and to overcome the drawbacks of cloud computing, a new computing model called fog computing has proposed. In this paper, a comparison between fog computing and cloud computing paradigms were performed. The scheduling task for an IoT application in a cloud-fog computing system was considered. For the simulation and evaluation purposes, the CloudAnalyst simulation toolkit was used. The obtained numerical results showed the fog computing achieves better performance and works more efficient than Cloud computing. It also reduced the response time, processing time ,and cost of transfer data to the cloud.
Keywords: Fog Computing, Networks, Cloud Computing, IoT, Data Center, Cloud Analyst

Scope of the Article:
Energy Efficient Building Technology