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3rd International Symposium on Women in Computing and Informatics (WCI)

作者： Karande, Pooja Gaherwar, Sonam Kurhekar, Manish VNIT Nagpur Dept Comp Nagpur Maharashtra India

ISBN: (纸本)9781450333610

cloud computing is an evolving technology which provides reliable computing resources as a utility rather than having to build and maintain computing infrastructures in-house. Today users and organizations wants to migrate from physical system to a virtual cloud instance. In other words, users want to migrate a traditional operating system, pre-installed on their physical system into a virtual machine instance run running on cloud. Most of the tune user requires instance configuration to be exactly same as that of his/her personal physical system. In this paper, we have implemented a process of migration of physical system to virtual openstack cloud instance. We have achieved this with the use of system images of the physical system and created a seini-automated script.

关键词： P2V Physical to Virtual Migration openstack cloud

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Security Assessment of openstack cloud using outside and inside software tools 14

Security Assessment of OpenStack cloud using outside and ins...

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14th International Conference on Development and Application Systems (DAS)

作者： Gordin, Ionel Graur, Adrian Potorac, Alin Balan, Doru Univ Stefan Cel Mare Suceava Romania

ISBN: (纸本)9781538614938

Companies wanting to enjoy the benefits of cloud computing are having the option to use the services of a public cloud (i.e. Google cloud, Amazon EC2, Microsoft Azure) or to make their own private cloud infrastructure. Public clouds are using proprietary cloud software and security is usually maintained by issuing companies. For private clouds the security remains a concern. There are many elements that cloud affect the cloud integrity and because the security is maintained by a third party, misconfiguration could arise. The current study performs a security analysis for private cloud solution openstack Pike version from outside and as well from inside the cloud using the best security scan software for this purpose: Nessus, Metasploit and OpenVAS. Inside the cloud are installed 4 virtual machines with different operating systems. The experiment also checks the hypervisor-based virtual machines isolation. The study concludes about the security threats found on multi-tenant environment on and provides concrete solutions for each situation and how to address them using patches or appropriate alternative solutions.

关键词： cloud computing security security vulnerability openstack cloud

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Design and Implementation of a forensic framework for cloud in openstack cloud platform 3

Design and Implementation of a forensic framework for Cloud ...

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Saibharath, S. Geethakumari, G. BITS Pilani Dept Comp Sci & Informat Syst Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479930807

In this paper, a forensic framework has been developed to do cloud forensics in openstack for infrastructure as a service model using the existing forensic tools. For the instances which have been allotted to the user, the snapshots of volatile random access memory and image from the hard disk (cinder) in the specific path where it is mounted on should be acquired to do forensics. Adding to internal, external and floating ip address, for every task or modification a cloud end user does through the cloud api or dashboard (in openstack cloud platform), packets get transferred through ISP and then the changes get updated in the cloud setup. So network forensics is an integral part of cloud forensics. Our forensic framework obtains live snapshots, image evidences, packet captures and log evidences and does analysis on it. Simulation is carried out through Digital forensic framework on image files of block storage and live snapshots, Wireshark on raw network captures, XML and Java for structuring log files. cloud forensic process for image acquisition and analysis has been defined by steps used in simulation. Two scenarios of integrity checking in object storage has been simulated through JSch are detailed. Discussion on finding various attacks happened from the evidences obtained is elaborated.

关键词： cloud forensics openstack cloud cloud computing Digital forensics

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Design and Analysis of openstack cloud Smart Factory Platform for Manufacturing Big Data Applications 19th

Design and Analysis of OpenStack Cloud Smart Factory Platfor...

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19th International Conference on Computational Science and Its Applications (ICCSA)

作者： Ahn, Dae Jun Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence Suwon 16419 Gyeonggi Do South Korea

ISBN: (纸本)9783030242961

In building an open source cloud platform, you can configure the openstack cloud architecture to implement services with capabilities for smart factories. In addition, we study the limitations, speed, and security analysis of existing cloud. In addition, we confirm that the data that accumulates when smart factory data is generated becomes unstructured data, and solves the problem with the prototype and various types of open source cloud to overcome this problem. The Smart Factory is composed of users in various environments in connection with all tasks such as distribution, production, and research, as well as the manufacturing environment. We design a smart factory cloud that integrates these environments. When storing files of various types, it is possible to store secure data by encrypting and decrypting various types of files and the like. In addition, for the convenience of cloud users, all data can be stored, edited and modified on any local PC connected to the cloud. Finally, open source-based cloud computing optimized for big data environments for smart factories can be configured.

关键词： Smart Factory BigData OpenSource openstack cloud Local cloud disk

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Ceph Distributed File System Benchmarks on an openstack cloud 4

Ceph Distributed File System Benchmarks on an Openstack Clou...

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4th IEEE International Conference on cloud Computing in Emerging Markets (CCEM)

作者： Zhang, X. Gaddam, S. Chronopoulos, A. T. Univ Texas San Antonio Dept Comp Sci 1 UTSA Circle San Antonio TX 78249 USA

ISBN: (纸本)9781467385664

Ceph is a distributed file system that provides high performance, reliability, and scalability. Ceph maximizes the separation between data and metadata management by replacing allocation tables with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clusters of unreliable object storage devices (OSDs). In this paper, we investigate the performance of Ceph on an Open Stack cloud using well-known benchmarks. Our results show its good performance and scalability.

关键词： openstack cloud Ceph distributed file storage benchmarks

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A performance analysis of openstack cloud vs Real System on Hadoop Clusters 1

A performance analysis of OpenStack Cloud vs Real System on ...

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1st IEEE International Conference on Edge Computing (IEEE EDGE)

作者： Adhikari, Binod Kumar Zuo, Wanli Maharjan, Ramesh Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Tribhuvan Univ Amrit Campus Kathmandu Nepal

ISBN: (纸本)9781538620175

This paper introduces the performance analysis of openstack cloud with the commodity computers in the big data environments. It is necessary to analyze the performance of virtual system in cloud infrastructure with the commodity computers to identify effective and efficient system for processing big data. To determine performance measures, virtual systems were created in the openstack cloud and Hadoop distributed framework was configured on all the virtual instances and commodity computers to form their respective clusters. For the experiment, image to pdf conversion and word count program are executed in the same configuration of openstack cloud and Commodity computers. We found that personal computers hadoop cluster was as powerful and fault tolerant as the cloud hadoop cluster but not as scalable as the cloud cluster. This paper concludes that the data storage and analysis in hadoop cluster in cloud is more flexible and easily scalable than the real system cluster. This paper also concludes the cluster in commodities computers are faster than the cloud clusters.

关键词： openstack cloud Hadoop distributed system virtualization big data

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Designing Network Intrusion and Detection System using Signature-Based Method for Protecting openstack Private cloud 6

Designing Network Intrusion and Detection System using Signa...

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6th International Annual Engineering Seminar (InAES)

作者： Santoso, Berkah I. Idrus, M. Rien Suryatama Gunawan, Irwan Prasetya Bakrie Univ Dept Informat Jakarta 12920 Indonesia

ISBN: (纸本)9781509007417

Nowadays, cloud computing has become a preferred solution for start-up businesses and corporations. Unfortunately, cloud computing user usually does not pay attention to security aspect of cloud services. cloud computing security is a mandatory requirement that must be fulfilled by the cloud provider. One of the solution for improving the security aspect of cloud computing services is using Network-based Intrusion Detection System (NIDS). NIDS detects and monitors attacks through the network. The signature-based method in NIDS performs identification data packet through network. In this research, the authors designed, implemented and evaluated the performance of configured NIDS. The authors also perform analysis of the result and performance evaluation of NIDS on openstack private cloud. The aim of this research is to evaluate the NIDS performance and its accuracy in classifying attacks. The results reveal that the model is functioning securely and accurately. Moreover, the real-time alert of NIDS is able to detect the classified attacks through network successfully.

关键词： network intrusion detection openstack cloud signature-based

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Towards Smart Green Energy Metering Design for openstack/Amazon Elastic cloud Integration

Towards Smart Green Energy Metering Design for OpenStack/Ama...

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IEEE PES/IAS PowerAfrica Conference

作者： Okafor, K. C. Okoye, J. A. Onoshakpor, R. M. Fed Univ Technol Owerri Dept Mechatron Engn Comp Syst & Software Dev Owerri Nigeria Chukwuemeka Odumegwu Ojukwu Univ Dept Elect & Elect Engn Uli Anambra State Nigeria Rivers State Univ Sci & Tech Dept Elect & Elect Engn Port Harcourt Nigeria

ISBN: (纸本)9781728110103

Energy demand in most African countries is insufficient thereby hindering economic development. By leveraging renewable technologies, the control and management of renewable micro-grids requires a robust/reliable design that supports smart integration via cloud for dynamic service delivery. Motivated by this concern, this paper developed smart green energy management system (SGEMS) for optimizing demand side management (DSM) in renewable micro-grids. The architecture employs solar photovoltaic (PV) to generate energy and meters user consumption pattern. SGEMS global model for solar PV metering platform is introduced including structured openstack cloud application with active load-balancers, Trove/Hadoop Bigdata application program interface (API), OpenFlow firewall, and dynamic network scaling. These features manage user access with little computational overhead. An experimental demonstration of openstack/Amazon EC2 instance scenarios for transactional workload is briefly highlighted. The work discussed the usefulness of openstack engine as a distributed API middleware for SGEMS-energy application running on AWS EC2.

关键词： cloud Data Center openstack cloud Smart Metering Renewable energy Micro-grid Integration

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Hybrid approach with Deep Auto-Encoder and optimized LSTM based Deep Learning approach to detect anomaly in cloud logs

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 6257-6271页

作者： Pankajashan, Savaridassan Maragatham, G. Devi, T. Kirthiga SRM Inst Sci & Technol Dept Informat Technol Chennai Tamil Nadu India

Anomaly-based detection is coupled with recognizing the uncommon, to catch the unusual activity, and to find the strange action behind that activity. Anomaly-based detection has a wide scope of critical applications, from bank application security to regular sciences to medical systems to marketing apps. Anomaly-based detection adopted by various Machine Learning techniques is really a type of system that consists of artificial intelligence. With the ever-expanding volume and new sorts of information, for example, sensor information from an incontestably enormous amount of IoT devices and from network flow data from cloud computing, it is implicitly understood without surprise that there is a developing enthusiasm for having the option to deal with more conclusions automatically by means of AI and ML applications. But with respect to anomaly detection, many applications of the scheme are simply the passion for detection. In this paper, Machine Learning (ML) techniques, namely the SVM, Isolation forest classifiers experimented and with reference to Deep Learning (DL) techniques, the proposed DA-LSTM (Deep Auto-Encoder LSTM) model are adopted for preprocessing of log data and anomaly-based detection to get better performance measures of detection. An enhanced LSTM (long-short-term memory) model, optimizing for the suitable parameter using a genetic algorithm (GA), is utilized to recognize better the anomaly from the log data that is filtered, adopting a Deep Auto-Encoder (DA). The Deep Neural network models are utilized to change over unstructured log information to training ready features, which are reasonable for log classification in detecting anomalies. These models are assessed, utilizing two benchmark datasets, the openstack logs, and CIDDS-001 intrusion detection openstack server dataset. The outcomes acquired show that the DA-LSTM model performs better than other notable ML techniques. We further investigated the performance metrics of the ML and DL models thr

关键词： Anomaly detection classification deep learning hyperparameter optimization long short term memory model artificial neural networks openstack cloud

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EEVMC: An Energy Efficient Virtual Machine Consolidation Approach for cloud Data Centers

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IEEE ACCESS 2024年 12卷 105234-105245页

作者： Rehman, Attique Ur Lu, Songfeng Ali, Mubashir Smarandache, Florentin Alshamrani, Sultan S. Alshehri, Abdullah Arslan, Farrukh Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China Lahore Garrison Univ Dept Software Engn Lahore 54810 Pakistan Univ New Mexico Math Phys & Nat Sci Div Gallup NM 87301 USA Taif Univ Coll Comp & Informat Technol Dept Informat Technol Taif 21944 Saudi Arabia Al Baha Univ Fac Comp & Informat Technol Informat Technol Dept Al Baha 65799 Saudi Arabia Univ Engn & Technol Dept Elect Engn Lahore 54500 Pakistan

The dynamic landscape of cloud computing design presents significant challenges regarding power consumption and quality of service (QoS). Virtual machine (VM) consolidation is essential for reducing power usage and enhancing QoS by relocating VMs between hosts. openstack Neat, a leading framework for VM consolidation, employs the Modified Best-Fit Decreasing (MBFD) VM placement technique, which faces issues related to energy consumption and QoS. To address these issues, we propose an Energy Efficient VM Consolidation (EEVMC) approach. Our method introduces a novel host selection criterion based on the incurred loss during VM placement to identify the most efficient host. For validation, we conducted simulations using real-time workload traces from Planet-Lab and Materna over ten days, leveraging the latest cloudSim toolkit to compare our approach with state-of-the-art techniques. For Planet-Lab's workload, our EEVMC approach shows a reduction in energy consumption by 80.35%, 59.76%, 21.59%, and 7.40%, and fewer system-level agreement (SLA) violations by 94.51%, 94.85%, 47.17%, and 17.78% when compared to Modified Best-Fit Decreasing (MBFD), Power-Aware Best Fit Decreasing (PABFD), Medium Fit Power Efficient Decreasing (MFPED), and Power-Efficient Best-Fit Decreasing (PEBFD), respectively. Similarly, for Materna, EEVMC achieves a reduction in energy consumption by 16.10%, 61.0%, 4.94%, and 4.82%, and fewer SLA violations by 76.99%, 88.88%, 12.50%, and 48.65% against the same benchmarks. Additionally, Loss-Aware Performance Efficient Decreasing (LAPED) significantly reduces the total number of VM migrations and SLA time per active host, indicating a substantial improvement in cloud computing efficiency.

关键词： cloud computing Data centers Energy efficiency Quality of service Energy consumption Virtual machines Power demand Virtual machine consolidation quality of service energy efficient VM migration placement algorithm openstack cloud

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