{'fraction_non_alphanumeric': 0.017094017094017096, 'fraction_numerical': 0.0026298487836949377, 'mean_word_length': 4.9921259842519685, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Assessment of various scheduling and load balancing algorithms in integrated cloud-fog environment



It is required to design a suitable scheduling algorithm that enhances the timely execution of goals such as load distribution, cost monitoring, and minimal time lag to react, increased security awareness, optimized energy usage, dependability, and so on. In order to attain these criteria, a variety of scheduling strategies based on hybrid, heuristic, and meta-heuristic techniques are under consideration.



IoT devices and a variety of network resources make up the integrated cloud-fog environment. Every fog node has devices that release or request resources. A good scheduling algorithm is required in order to maintain the requests for resources made by various IoT devices.



This research focuses on analysis of numerous scheduling challenges and techniques employed in a cloud-fog context. This work evaluates and analyses the most important fog computing scheduling algorithms.



The survey of simulation tools used by the researchers is done. From the compared results, the highest percentage in the literature has 60% of scheduling algorithm which is related to task scheduling and 37% of the researchers have used iFogSim simulation tool for the implementation of the proposed algorithm defined in their research paper.



The findings in the paper provide a roadmap of the proposed efficient scheduling algorithms and can help researches to develop and choose algorithms close to their case studies.
