Аннотации:
Huge data processing applications are stored efficiently using cloud computing platform. Few technologies like edge computing, Internet of Things (IoT) model helps cloud computing framework for executing data with less energy and latencies for better infrastructure. Recently researches focused on providing excellent services to cloud computing users. Also, cloud-based services are highly developed over IT field. Energy a level varies based on the cloud setup like speed, memory, service capability and bandwidth. The user job requirements are varied based its nature. The process of identifying efficient energy resources for the user job is main aim of this research work. Initially IoT, Edge devices capture the job and process to help cloud infrastructure. Data transferring process is a Non-deterministic Polynomial (NP) hard problem which can be easily supported by technologies like IoT and Edge computing. Main issue is, tremendous increase of data over the cloud causes difficult to manage consumption of energy. In this research work server clouds with edge devices and centralized cloud servers are combined to work for providing efficient energy consumption. Here, in this paper we implement novel dynamic speed scaling (NDS) algorithm. The CPU workload is first computed using NDS algorithm for incoming application. The less energy computation is achieved using energy internet of things (EIoT). Fine methodology called speed processor scaling, helps to consume less energy and less computation price. High energy consumption of processor is due to high computation speed. likewise, less energy is consumed during less processor computation speed. This technique is building in NDS algorithm and computed in Edge devices for less energy consumption. The result evaluation proves that proposed technique consumes only 10 s for data computation when compared to other existing techniques.
