A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment

An Improved Bat Algorithm With Time-Varying Wavelet Perturbations for Cloud Computing Resources Scheduling

Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the best balance between speed of operation, average response time, and integrated system utilization in the resource allocation process in cloud computing, an improved bat algorithm with time-varying wavelet perturbations was proposed. The algorithm provided a perturbation strategy of time-varying Morlet wavelet with the waving property to prevent from local optimum greatly and improve the converging speed and accuracy through the guide of individual distribution to control diversity and time-varying coefficient of wavelets. The experiments showed the proposed could significantly upgrade the overall performance and the capability of resource scheduling in cloud service compared to similar algorithms.

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO

The use of a cognitive radio power allocation algorithm is an effective method to improve spectral utilization. However, there are three problems with traditional cognitive radio power allocation algorithms: (1) based on the ideal channel model analysis, channel fluctuation is not considered; (2) they do not consider fairness among cognitive users; and (3) some algorithms are complex and locating the optimal power allocation scheme is not an easy task. For the above problems, this study establishes a robust model which adds the cognitive user transmission rate variance constraint to solve the maximum channel capacity time power allocation scheme by considering the worst-case channel transmission model, and finally solves this complex non-convex optimization problem by using the hybrid particle swarm algorithm. Simulation results show that the algorithm has good robustness, improves the fairness among the cognitive users, makes full use of the channel resources under the constraints, and has a simple algorithm, fast convergence, and good optimization results.

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.

Energy and Quality Aware Multi-Objective Resource Allocation Algorithm in Cloud

Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional Rider-Harmony search algorithm (Fractional Rider-HSA) for resource allocation in the cloud. The Fractional Rider-HSA combines fractional calculus, Rider Optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.

SWSA: A Hybrid Scientific Workflow Scheduling Algorithm Based on Metaheuristic Approach in Cloud Computing Environment

The cloud computing environment with a set of distributed computing resources is a suitable platform for the execution of large-scale applications. One of these applications is scientific workflow applications in which a large set of interrelated tasks are executed for a certain purpose. Scientific workflow scheduling is one of the main challenges in this area, which aims at the optimal assignment of tasks to computational resources. Given the heterogeneity of cloud computing resources, the scientific workflow scheduling is an NP-Complete problem that can be solved by heuristic methods. In this paper, an improved evolutionary algorithm called Scientific Workflow Scheduling Algorithm (SWSA) for scheduling scientific workflows in the cloud will be provided by ranking tasks and improving the initial population of tasks. The objective of this algorithm is to create a balance and an improvement in the parameters of the execution cost and workflow execution completion time. In this proposed approach, a heuristic algorithm is used to rank and generate the initial population, which increases the convergence rate. The experimental results show that SWSA is more efficient in terms of cost and execution time compared with other approaches.

Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually unlimited resources that can be attained, organized, and used as required. Resource scheduling plays a fundamental role in the well-organized allocation of resources to every task in the cloud environment. However along with these gains many challenges are required to be considered to propose an efficient scheduling algorithm. An efficient Scheduling algorithm must enhance the implementation of goals like scheduling cost, load balancing, makespan time, security awareness, energy consumption, reliability, service level agreement maintenance, etc. To achieve the aforementioned goals many state-of-the-art scheduling techniques have been proposed based upon hybrid, heuristic, and meta-heuristic approaches. This work reviewed existing algorithms from the perspective of the scheduling objective and strategies. We conduct a comparative analysis of existing strategies along with the outcomes they provide. We highlight the drawbacks for insight into further research and open challenges. The findings aid researchers by providing a roadmap to propose efficient scheduling algorithms.