In recent years, cloud computing technology has gained a great deal of interest from both academia and industry. Cloud computing's success benefited from its ability to offer global IT services such as core infrastructure, platforms, and applications to cloud customers around the web. It also promises on-demand offerings and new ways of pricing packages. However, cloud job scheduling is still NP-complete and has become more difficult due to certain factors such as resource dynamics and on-demand customer application requirements. To fill this void, this chapter presents the seagull optimization algorithm (SOA) for scheduling work in the cloud world. The efficiency of the SOA approach is compared to that of state-of-the-art job scheduling algorithms by having them all implemented in the CloudSim toolkit.
TopThere has recently been considerable concern using metaheuristics to overcome various problems (MHs). They will use previously solve another set of problems with optimization traditional methods that cannot be resolved. Based on these benefits, multiple studies have found that the MH methods give good outcomes for cloud task planning issues. In addition to other conventional approaches, computing (Kennedy & Eberhart, 1995), (Dorigo et. al, 2006), a full review was performed by the writers in (Aleem et. al, 2019), (Babikir et. al, 2019), (Elkadeem et. al, 2019) different metaheuristics for the resolution cloud computing task planning issues.
Guo et al. (Guo et. al, 2012) proposed an approach for task planning according to the algorithm to be updated of PSO Minimize user activity processing costs by embedding Operators for PSO crossover and mutation Procedure. Findings suggested that the PSO was changed offers good performance particularly with a large scale Data set.
Khalili and Babamir have also established a similar approach (Ewees & Elaziz, 2020) using various techniques to change the PSO version Update your weight of inertia. A variant of method was used in one Cloud environment for workload reduction timetable (Heilig et. al, 2018) Depending on the complex shipping queues (TSDQ) Timetable. First approach merged fuzzy logic with first approach the second method combined PSO (TSDQ-FLPSA), PSO (TSDQ-SAPSO) simulated annealing.