Multi-resource management using an advanced scheduling algorithm to the least amount of time

• Published in: International journal of information technology (Singapore. Online) 2024-04, Vol.16 (4), p.2283-2293
• Main Authors: Shakya, Santosh, Tripathi, Priyanka
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Imaging; Computer Science; Image Processing and Computer Vision; Machine Learning; Original Research; Pattern Recognition and Graphics; Software Engineering; Vision
• ISSN: 2511-2104; 2511-2112
• DOI: 10.1007/s41870-024-01752-x

The scheduling of appropriate resources for cloud workloads is a difficult task, as it depends on the quality of service needs of cloud applications. Due to their limited data storage and energy capabilities, Internet of Things (IoT) applications demands high-speed data transfer and low latency. Many IoT devices generate data continuously and want to store it on the cloud quickly and efficiently. Dynamic virtual machine (VM) allocation in cloud data centers (DCs) is taking advantage of the cloud computing paradigm. Each VM request is characterized by four parameters: CPU, RAM, disk, and bandwidth. Allocators are designed to accept as many VM requests as possible, considering the power consumption of the IoT device’s network. Resource scheduling and time consumption is the two most significant problems in cloud computing. To overcome this problem, in this paper, the author has extended CloudSim with a multi-resource scheduling and minimum time consumption model that allows a more accurate valuation of time consumption in dynamic multi-resource scheduling. The author proposes a new scheduling algorithm advance scheduling algorithm (ASA), which provides a better solution to other scheduling algorithms like Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). Also tries to reduce energy consumption and time to give a task to the VM.