Empirical analysis of dynamic load balancing techniques in cloud computing

Virtualization, dispersed registration, systems administration, programming, and web administrations are all examples of distributed computing. Customers, datacenters, and scattered servers are just a few of the components that make up a cloud. It includes things like internal failure adaption, high accessibility, flexibility, adaptability, lower client overhead, lower ownership costs, on-demand advantages, and so on. The basis of a feasible load adjusting computation is key to resolving these challenges. CPU load, memory limit, deferral, and system load are all examples of heaps. Burden adjustment is a method for distributing the load across the many hubs of a conveyance framework in order to optimize asset utilization and employment response time while avoiding a situation where some hubs are heavily loaded while others are idle or performing little work. Burden adjustment ensures that at any one time, each processor in the framework or each hub in the system does about the same amount of work. This method may be initiated by the sender, the collector, or the symmetric sort (the blend of sender-started and recipient started types). With some example data center loads, the goal is to create several dynamic load balancing techniques such as Round Robin, Throttled, Equally Spread Current Execution Load, and Shortest Job First algorithms

Covid-19 confirmed cases prediction in china based on barnacles mating optimizer-least squares support vector machines

The Covid19 has significantly changed the global landscape in every aspect including economy, social life, and many others. After almost two years of living with the pandemic, new challenges are faced by the research community. It may take some time before the world can be declared as totally safe from the virus. Therefore, prediction of Covid19 confirmed cases is vital for the sake of proper prevention and precaution steps. In this study, a hybrid Barnacles Mating Optimizer with Least Square Support Vector Machines (BMO-LSSVM) is proposed for prediction of Covid19 confirmed cases. The employed data are the Covid19 cases in China which are defined in daily periodicity. The BMO was utilized to obtain optimal values of LSSVM hyper-parameters. Later, with the optimized values of the hyper-parameters, the prediction task will be executed by LSSVM. Through the experiments, the study recommends the superiority of BMO-LSSVM over the other identified hybrid algorithms

Методи підвищення ефективності алгоритмів та методів балансування навантаження у хмарних середовищах інфокомунікаційних систем

Мета роботи: комплексний аналіз алгоритмів та способів балансування навантаження інфокомунікаційних систем, розроблених з використанням "хмарних" технологій, дослідження особливостей розгортання, функціонування та масштабування таких систем, пошук методів підвищення ефективності розподілу навантаження в хмарному середовищіThe рurроse оf the master’s thesis lies in comprehensive analysis of algorithms and methods of load balancing infocommunication systems developed using "cloud" technologies, study of the deployment, operation and scaling of such systems, search for methods to increase the efficiency of load distribution in a cloud environmen