A Knowledge and Intelligent-based Strategy for Resource Discovery on IaaS Cloud Systems (original) (raw)
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Texila International Journal of Academic Research, 2019
Cloud Computing (CC) and Artificial Intelligence (AI) marks the dawn of a new era of transformation, where customers can avail resources from service providers, that offer users or machines pay per use computers as virtual machines, raw (block) storage, firewalls, load balancers, and network devices enabling smart solutions; promptly, efficiently and economically. The resulting application from the twin-technologies of cloud computing and artificial intelligence could be combined to significantly enhance resource management services such as allocation, provisioning, requirement mapping, adaptation, discovery, estimation, and modeling. The conclusion that follows is scalability, quality of service, optimal utility, reduced overheads, improved throughput, reduced latency, specialized environment, cost effectiveness and simplified interface. This study aims to improve the performance of AI in cloud resource management for best optimization. The rest of the paper is organized as follows as Introduction to Artificial intelligence, cloud computing, Review of Literature, Resource Management in OpenStack, Issues and challenges and conclusion.
Adaptive Neural Fuzzy Interface System for Cloud Computing
Cloud computing is an improving area in research and in industry, which consists of distributed computing, internet, web services and virtualization.One of the most important technologies to load forecasting in the cloud computing is to ensure the maximize utilization of the system resource. Under the premise that the load is known in the next level, the cloud computing node can assign the physical machines in advance, and hence reduces the waiting time of the task, which also reduces the cloud computing node's resource consumption. A neural fuzzy technique called Adaptive network based fuzzy inference system (ANFIS) has been used as a prime tool in the present work. Using this hybrid method, at first an initial fuzzy model along with its input variables are derived with the help of the rules extracted from the input output data of the system that is being represented. Next the neural network is used to fine tune the rules of the initial fuzzy model to produce the final ANFIS model of the system. In this proposed work ANFIS is used as the backbone for the load balancing in the cloud computing. I. INTRODUCTION Adaptive network based fuzzy inference system (ANFIS) is a neuro fuzzy technique where the fusion is made between the neural network and the fuzzy inference system. In ANFIS the parameters can be estimated in such a way that both the Sugeno and Tsukamoto fuzzy models are represented by the ANFIS architecture. Again with minor constraints the ANFIS model resembles the Radial basis function network (RBFN) functionally. This ANFIS methodology comprises of a hybrid system of fuzzy logic and neural network technique. The fuzzy logic takes into account the imprecision and uncertainty of the system that is being modeled while the neural network gives it a sense of adaptability. Cloud computing is a new pattern of large-scale distributed computing. It has stimulated computing and data away from desktop and manageable PCs, into large data centers [1]. It has the ability to connect the power of Internet and wide area network (WAN) to make use of resources that are available remotely, thereby providing cost-effective solution to most of the real life requirements [2]. It gives the scalable IT resources such as applications and services, in addition to the infrastructure on which they control, over the Internet, on pay-per-use basis to regulate the capacity rapidly and easily. It helps to occupy changes in demand and helps any organization in stay away from the capital costs of software and hardware [3] [4]. Therefore, cloud computing is a structure for enabling an appropriate, on-demand network access to a common pool of computing resources.Cloud service is divided into three models. They are,as shown in Fig. 1. Cloud Software as a service (Saas): The competence provided to the consumer is to make use of the provider's applications consecutively running on a cloud communications. The applications are easy to get from several client devices throughout a thin client interface such as a web browser. The consumer does not deal with the fundamental cloud infrastructure. Cloud Platform as a Service (Paas): The capability provided to the consumer is to arrange on the cloud communications consumer formed or obtained applications created by means of programming languages and tools sustained by the provider. The consumer does not supervise or control the fundamental cloud structure, but has control over the applications and perhaps application hosting environment configurations.
2016
This paper address the problem of managing cloud system, consisting a set of virtual machines (VMs), operating under dynamic workloads conditions. The objective of the paper is to find the best workload-VM pair in such a way to give a guarantee on his Quality-of-Services (QoS) and, at the same time, to minimize the energy consumption of the physical infrastructure running them. For that we propose a fuzzy controller, which is able to allocate the best VMs to each cloud application in such a way that the minimum amount of physical capacity needed to meet its QoS requirements. In this way the VMs are dynamically selected hence the number of physical resources that needed to be active at any given instant of time is reduced in comparison to the statically provisioned systems. This reduces the energy requirement to run a given cloud workload. We implemented a prototype of our controller on CloudSim, and tested it over different load conditions in which we compare the proposed technique ...
Applications Of Fuzzy Logic in Cloud Computing: A Review
2017
The objective of this paper is the analysis of various applications of Fuzzy Logic in Cloud Computing. This paper reviews the already available application areas of Fuzzy Logic in Cloud Computing. Methods/ Statistical analysis: Various studies on application areas of Fuzzy Logic in cloud computing systems have been considered. Relative analysis has been made to categorize these application areas. Findings: Cloud computing is web based technology that has brought a lot of improvement in the field of Information Technology. It is a pay-as-you-go service model that delivers services on the basis of demand of users. Because of its capability to deal with uncertainties, Fuzzy Logic has given a good response in cloud computing. Various Fuzzy Logic based application areas in cloud computing are prevalent in the existing literature like Load balancing, Job Scheduling, QOS optimization etc. Results have shown that Fuzzy Logic helps in improvement in various areas in Cloud Computing. Applicat...
An Optimal Resource Provisioning Algorithm for Cloud Computing Environment
Oriental journal of computer science and technology, 2017
Resource Provisioning in a Cloud Computing Environment ensures flexible and dynamic access of the cloud resources to the end users. The Multi-Objective Decision Making approach considers assigning priorities to the decision alternatives in the environment. Each alternative represents a cloud resource defined in terms of various characteristics termed as decision criteria. The provisioning objectives refer to the heterogeneous requirements of the cloud users. This research study proposes a Resource Interest Score Evaluation Optimal Resource Provisioning (RISE-ORP) algorithm which uses Analytical Hierarchy Process (AHP) and Ant Colony Optimization (ACO) as a unified MOMD approach to design an optimal resource provisioning system. It uses AHP as a method to rank the cloud resources for provisioning. The ACO is used to examine the cloud resources for which resource traits best satisfy the provisioning. The performance of this approach is analyzed using CloudSim. The experimental results...