PurposeGrid computing, cloud computing (CC), utility computing and software as a service are emerging technologies predicted to result in massive consolidation as meta‐level computing services of everything beneath one umbrella in the future. The purpose of this study is to foster the understanding and differentiation, by using the three aforementioned types of computing technologies and software, as a service by both public and private libraries to meet their expectations and strategic objectives.Design/methodology/approachThe approach in this study is a review based on comparing the four computing technologies with a brief analysis for researching and designing the mind map of a new meta‐level computing service approach, taking into consideration the need for new economic tariff and pricing models as well as service‐level agreements.FindingsSince it is anticipated that there will be likely potential consolidation and integration of computing services, a study of these four most advanced computing technologies and their methodologies is presented through their definition, characteristics, functionalities, advantages and disadvantages. This is a well‐timed technological advancement for libraries.Practical implicationsIt appears that the future of library services will become even more integrated, running over CC platforms based on usage rather than just storage of data.Social implicationsLibraries will become an open useful resource to all and sundry in a global context, and that will have huge societal benefits never imagined before.Originality/valueConcisely addresses the strategies, functional characteristics, advantages and disadvantages by comparing these technologies from several service aspects with a view to assisting in creating the next generation outer space computing.
Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.
In this paper, we developed a computing architecture and algorithms for supporting soft real-time task scheduling in a cloud computing environment through the dynamic provisioning of virtual machines. The architecture integrated three modified soft real-time task scheduling algorithms, namely, Earliest Deadline First, Earliest Deadline until Zero-Laxity, and Unfair Semi-Greedy. A deadline look-ahead module was incorporated into each of the algorithms to fire deadline exceptions and avoid the missing deadlines, and to maintain the system criticality. The results of the implementation of the proposed algorithms are presented in this paper in terms of the average deadline exceptions, the extra resources consumed by each algorithm in handling deadline exceptions, and the average response time. The results not only suggest the feasibility of the soft real-time scheduling of periodic real-time tasks in cloud computing but that the process can also be scaled up to handle the near-hard real-time task scheduling. INDEX TERMS Real-time, cloud computing, virtual machine, deadline, laxity.
Cloud computing, as a trend technology, has stemmed from the concept of virtualization. Virtualization makes the resources available to the public to use without any concern for ownership or maintenance cost. In addition, the hosted applications in cloud computing platforms are highly interactive and require intensive resources. The new trend is to duplicate these applications in multiple virtual machines based on demand. Such a scheme needs an efficient resource provisioning to manage the resource assignment to multiple virtual machines properly. One of the issues in the current resource provisioning technique is assigning the resources proactively without predicting the workload of hosted applications, which cause load imbalance and resource wasting. Thus, this paper proposes a new model to predict the application workload. The experimental results show the ability of the proposed model to allocate more virtual machines and to balance the workload among the physical machines.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.