Abstract
Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.
Highlights
In the modern era of Industry 4.0 and Smart Manufacturing, concepts and paradigms such as Cloud Computing and Internet of Things (IoT) are becoming more important by enabling innovations and advances in many domains [1,2,3]
The group of basic initialization parameters includes settings that are used in all population-based metaheuristics, while the other two groups further separate control parameters that are common for both algorithms, monarch butterfly optimization (MBO) and MBO-artificial bee colony (ABC), from the parameters that are specific for the MBO-ABC implementation
It can be stated that the MBO-ABC hybrid metaheuristics are able to accomplish better solution quality, robustness and scalability than other algorithms that are included in comparative analysis
Summary
In the modern era of Industry 4.0 and Smart Manufacturing, concepts and paradigms such as Cloud Computing and Internet of Things (IoT) are becoming more important by enabling innovations and advances in many domains [1,2,3]. A cloud computing paradigm enables efficient utilization of available physical resources through the virtualization technology, where different clients share the same underlying physical hardware infrastructure. In order to provide and to maintain guaranteed quality of service (QoS) to the end-users, cloud systems should utilize algorithms that map tasks (cloudlets) submitted by the clients to the available resources in an efficient manner. With the increasing number of resources and submitted tasks, many potential mapping combinations exist and, in large-scale cloud environments, the cloudlet scheduling problem belongs to the category of NP (nondeterministic polynomial time) hard optimization. For solving a task scheduling problem in cloud environments, exhaustive search methods and deterministic algorithms are not able to generate optimal or suboptimal results within a reasonable computational time, and, for this reason, these approaches can not be applied efficiently in this domain. Due to the fact that many real-life problems such as wireless sensor networks (WSNs) node localization, transportation problems, portfolio optimization, etc., can be modeled as optimization tasks, metaheuristic algorithms are widely implemented for addressing many practical NP-hard challenges, as can be seen from the literature
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
