Cloud customers service selection scheme based on improved conventional cat swarm optimization

Gabi, Danlami; Ismail, Abdul Samad; Zainal, Anazida; Zakaria, Zalmiyah; Abraham, Ajith; Dankolo, Nasiru Muhammed

doi:10.1007/s00521-020-04834-6

Cited by 18 publications

(15 citation statements)

References 53 publications

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“…In some specific scenarios, we can give weight to constrains to meet complex scenes. For multi-objectives problems, the objectives can be integrated based on their weights in realistic scenarios or can be evaluated by Pareto efficiency [23,95,120]. Approaches for scheduling in Cloud computing can be classified into six categories including Dynamic Programming(DP), Probability algorithm (Random), Heuristic method, Meta-Heuristic algorithm, Hybrid algorithms and Machine Learning.…”

Section: Discussionmentioning

confidence: 99%

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

Zhou¹,

Tian²,

Buyya³

2021

Preprint

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As the quantity and complexity of information processed by software systems increase, large-scale software systems have an increasing requirement for high-performance distributed computing systems. With the acceleration of the Internet in Web 2.0, Cloud computing as a paradigm to provide dynamic, uncertain and elastic services has shown superiorities to meet the computing needs dynamically. Without an appropriate scheduling approach, extensive Cloud computing may cause high energy consumptions and high cost, in addition that high energy consumption will cause massive carbon dioxide emissions. Moreover, inappropriate scheduling will reduce the service life of physical devices as well as increase response time to users' request. Hence, efficient scheduling of resource or optimal allocation of request, that usually a NP-hard problem, is one of the prominent issues in emerging trends of Cloud computing. Focusing on improving quality of service (QoS), reducing cost and abating contamination, researchers have conducted extensive work on resource scheduling problems of Cloud computing over years. Nevertheless, growing complexity of Cloud computing, that the super-massive distributed system, is limiting the application of scheduling approaches. Machine learning, a utility method to tackle problems in complex scenes, is used to resolve the resource scheduling of Cloud computing as an innovative idea in recent years. Deep reinforcement learning (DRL), a combination of deep learning (DL) and reinforcement learning (RL), is one branch of the machine learning and has a considerable prospect in resource scheduling of Cloud computing. This paper surveys the methods of resource scheduling with focus on DRL-based scheduling approaches in Cloud computing, also reviews the application of DRL as well as discusses challenges and future directions of DRL in scheduling of Cloud computing.

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Section: Discussionmentioning

confidence: 99%

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

Zhou¹,

Tian²,

Buyya³

2021

Preprint

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“…Most of the research focuses on solving the problem of choosing the services in the cloud environment solely based on non-functional requirements and quality characteristics. Thus, in [15], the scheme of choosing cloud services, based on a multi-purpose model of task planning by their cost and calculation time, was proposed. A multi-critical model for selecting cloud services taking into consideration their quality indicators, the values of which are determined from different sources, was developed in study [16].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…A similar problem, but for conventional corporate networks, was considered in research [17]. In general, it should be recognized that the problems of selecting services, explored in [15][16][17], are based on the assumption that the functional requirements of users of these services are met as fully as possible. The validity of this assumption in [15][16][17] is not verified.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Improving a method for selecting information technology services

Рetrychenko

Левыкин

Iuriev

2021

EEJET

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The main purpose of modern information systems (IS) is to support the procedures of controlling many business processes (BP) of an enterprise. At the same time, due to a weak formalization of BP, processes of development, reengineering of various service-oriented IS and IT services are a complex problem. To address this problem, the task to improve the method for choosing IT services that meet an assigned set of functional and non-functional limitations was stated. The specific features of the original method for choosing IT services were analyzed, its main shortcomings were identified. The model of the BP precedent was modified to establish the relationship between descriptions of the precedent, functional requirements, and the used IT services. The method for selecting IT services for the IS was improved by adding the functions of requirements analysis and searching for descriptions of IT services that partially meet the stated functional requirements. In the method, the adaptive linear associator of the mADALIN neuron was used to quantify the degree of match of the functional requirement and the description of the function of the IT service. These proposals were the basis for the improved method for selecting the IT service that best fits the set of constraints that are formed. Based on the result of the conducted research, an experimental test of the improved method for selecting an IT service to solve the problem of automation of the activities of sale force of the electronic policy OSAGO was carried out. The information technology implementing the original method for choosing IT services was compared to the improved method. It was shown that the improved method makes it possible to identify situations of a match of functional requirements of a customer and the descriptions of IT services. This makes it possible to select those IT services that match the functional requirements to a degree above the assigned minimum limit

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“…The CloudSim simulator was used for experiments. The authors in [25] proposed an algorithm named Dynamic Multi-Objective Orthogonal Taguchi-Cat.…”

Section: Related Workmentioning

confidence: 99%

An H-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Environment

Bhagwan¹,

Kumar²

2021

IJIES

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Cloud computing technology is becoming popular in both academia and industries these days because of its dynamic and flexible infrastructure which provides good computing facilities through web networks without a place specification. In order to process the workflows, the cloud technology uses datacenters which consist of hosts having various homogeneous and heterogeneous virtual machines. Virtual machines play the role of real machines with multiple operating systems environments. Due to a wide acceptance of the cloud, the load on the cloud is increasing regularly. So, scheduling plays a key role to manage a machine load by mapping several workflows with available virtual machines. The scheduling can be divided into two types: dynamic and static scheduling. Dynamic scheduling can be done effectively by meta-heuristic computing techniques. In the field of cloud computing, many scientists have developed various algorithms for workflow scheduling in order to schedule the workflows. The Cat Swarm Optimization works fine as compared to the Max-Min algorithm, Particle Swarm, and Ant Colony Optimization algorithms. In this research, a new algorithm is designed named H-CSO by using the concept of Heterogeneous Earliest Finish Time and Cat Swarm Optimization algorithms. After experiments, it is found that the proposed H-CSO algorithm gives efficient makespan at realistic costs as matched to the Cat Swarm Optimization. The newly designed H-CSO algorithm is 2.99%, 2.87%, 3.35%, and 5.77% efficient for CyberShake_1000, Montage_1000, Inspiral_1000, and Sipht_1000 datasets respectively as compared to the standard Cat Swarm Optimization in terms of average makespan reduction. In case of cost reduction, the proposed algorithm is 4.03%, 5.12%, 2.42%, and 3.93 effective as compared to the Cat Swarm Optimization for CyberShake_1000, Montage_1000, Inspiral_1000, and Sipht_1000 datasets respectively.

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Cloud customers service selection scheme based on improved conventional cat swarm optimization

Cited by 18 publications

References 53 publications

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud Computing: A Review and Future Directions

Improving a method for selecting information technology services

An H-CSO Algorithm for Workflow Scheduling in Heterogeneous Cloud Environment

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