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

Abstract

Nowadays, 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, theCloudAnalyst 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.

Cloud Computing: Optimization using Particle Swarm Optimization to Improve Performance of Cloud

“…Comparative analysis has been accumulated and presented in table (6,7,8) given. Table6 shows the, Average Waiting Time of each algorithm (FCFS, SJF and Round Robin) along with different number of processing elements (pesNumber) given in the table.Table7 shows the Average Turnaround Time of each algorithm (FCFS, SJF and Round Robin) along with different number of processing elements (pesNumber).…”

“…In article [6], In terms of time delays and response times, the comparison between fog and cloud computing. The performance of fog computing is superior to cloud computing, as long as the delay and response times are reduced.…”

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

“…Therefore, an integrated edge-fog-cloud architecture offers scalable data analytics and trustworthy solutions to overcome IoMT application challenges (e.g., the problem of reducing service execution time and energy consumption of IoMT applications) [13]. A number of studies in the literature [10,11,[14][15][16] have highlighted the importance of edge, fog, and cloud computing in terms of optimizing the placement of IoMT applications, considering several performance metrics such as energy consumption, service latency, resource usage, and security [17,18]. This section presents some of the related work for the placement of IoMT applications in edge-fog-cloud systems.…”

“…In [15], the authors developed an optimization conceptual fog computing framework that could reduce network communication delays and enhance fog resource utilization via the application of a genetic algorithm. To achieve better performance, authors in [11] conducted a simulation analysis for integrating fog computing with cloud computing paradigms. They found that offloading IoMT data to fog computing reduced response time by approximately 86% compared to the cloud layer.…”

“…However, determining the data that must be handled locally from the data that needs to be sent to the cloud servers is critical, since IoMT devices and their applications that entail real-time interactions are a rising source of big data. Therefore, the efficient placement of IoMT applications that require intensive computational processing on edge-fog-cloud systems should be the primary focus in order to assimilate the IoMT system capabilities [10,11].…”