A cloud database route scheduling method using a hybrid optimization algorithm

Abstract

Cloud computing has appeared as a technology allowing a company to employ computing resources such as applications, software, and hardware to calculate over the Internet. Scholars have paid great attention to cloud computing because of its cutting-edge availability, cost decrement, and boundless applications. A cloud database is a data storage site on the web where the optimal path is spotted to access the needed database. So, placing the ideal path to a database is crucial. The cloud database defined the scheduling problem to choose the perfect route. Cloud database path scheduling is a multifaceted procedure consisting of congestion control, routing list, and network flow distribution. It has a postponement in searching for the needed source route from the cloud database. Offering numerous infinite resources with the growing database workload is an NP-Hard optimization problem where the query request needs optimal schedules to respond to the required services. So, we have used a hybrid cuckoo search (CS) and genetic algorithm (GA), motivated by a social bird's phenomenon, to solve this problem. Integrating genetic operators has dramatically enhanced the balance between the capability of searching and utilization.

Optimal Corrective Maintenance Policies via an Availability-Cost Hybrid Factor for Software Aging Systems

“…Zheng et al [16] proposed a stochastic framework composed of a composite stochastic Petri reward net and its resulting non-Markovian availability model to capture the dynamic behavior of an operational software system in which time-based software rejuvenation and checkpointing are both aperiodically conducted. Baghi and Navimipour [17] utilized a hybrid cuckoo search (CS) and genetic algorithm (GA) to optimize the scheduling of cloud database paths, aiming to enhance the availability and reduce the cost of cloud computing.…”