Abstract
The cloud computing is very efficient platform for storing and processing huge data applications. IoT, Edge computing models helps the cloud infrastructure for processing data with less network latencies, efficient data processing with minimum energy consumption for better infrastructure. More researches on cloud computing process makes them matured in providing best services to the users. Also IT services on cloud have developed rapidly. The different cloud services have different energy level with is memory, processing speed, bandwidth and system capability. The users have different data and requirements to process their job. Process of finding the best resources with less energy consumption is main goal of our proposed article. The edge devices, IoT devices first focused to help cloud architecture. Edge and IoT are promising supportive technology to address the NP hard problems like less latency and consume less energy during data transfer. However, when the information and data are increasing in cloud, it’s very difficult to solve the energy consumption problem in heterogeneous cloud infrastructure. In our proposed work edge server clouds and central server clouds works collaboratively for reducing the energy consumption. in this article we implemented the novel dynamic speed (NDS) scaling algorithm . This NDS algorithm computes the workload of CPU for particular data application. The speed processor scaling is a methodology used for consuming less energy and gaining less rates. if processing speed is high then energy consumption will be higher vise versa if processing speed is low then energy consumption will be less. This ideology is developed using NDS algorithm in edge cloud devices to compute data using less energy. The proposed algorithm is compared with existing energy saving algorithms and its efficiency is better than other algorithms is evaluated.
Fuzzy Logic based Model to Reprioritize Cloud Computing Process Requests using Extended Parameters
