Search

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is CPU steal time, which measures the amount of time a virtual CPU waits for the actual CPU. In this study, we focus on the impact of CPU steal time on virtual machine performance and the potential problems that can arise. We implement our work using an OpenStack-based cloud environment and investigate intrusive and non-intrusive monitoring methods. Our analysis provides insights into the importance of CPU steal time monitoring and its impact on cloud performance.

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.

Recently, with the large-scale outbreak of the global financial crisis and public safety incidents (such as COVID-19), high-performance computing has been widely applied to risk prediction, vaccine development, and other fields. In scenarios where high-performance computing infrastructure responds to the instantaneous explosion of computing demands, a crucial issue is to provide large-scale flexible allocation and adjustment of computing capability by rapidly constructing computing clusters. Existing large-scale computing cluster deployment solutions usually utilize source code deployment or other deployment tools. The great challenge of existing deployment methods is to reduce excessive image distribution time and refrain from configuration defects. In this article, we design an intelligent distributed registry deployment (IDRD) architecture based on the OpenStack cloud platform, which adaptively places distributed image repositories using the containerized deployment of multiple registries. We propose a server load priority algorithm to solve multiple registries placement problems in IDRD. Furthermore, we devise a clustering algorithm based on demand density that can optimize the global performance of IDRD and improve large-scale cluster load balancing capabilities, which has been implemented in the TianHe Supercomputing environment. Extensive experimental results demonstrate that IDRD can effectively reduce 30% − 50% of the distribution time of component images and significantly improve the efficiency of large-scale cluster deployment. [ABSTRACT FROM AUTHOR]

Abstract GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack-based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface, a JavaFX-based desktop client has also been developed to meet the requirements of large file uploads that are usually seen in NGS datasets. Users can store files in their respective object-based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally using OpenStack-based cloud features. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture which is also supported by Secure File Transfer Protocol (SFTP) for data upload and download. The data is uploaded by the user in individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and the output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per the user’s permissions. GenoVault is useful for maintaining the organization-wide NGS data generated in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud.

Analyzing compute functions by utilizing the IAAS model for private cloud computing services in packstack development is one of the large-scale data storage solutions. Problems that often occur when implementing various applications are the increased need for server resources, the monitoring process, performance efficiency, time constraints in building servers and upgrading hardware. These problems have an impact on long server downtime. The development of private cloud computing technology could become a solution to the problem. This research employed Openstack and Packstack by applying one server controller node and two servers compute nodes. Server administration with IAAS and self-service approaches made scalability testing simpler and time-efficient. The resizing of the virtual server (instance) that has been carried out in a running condition shows that the measurement of the overhead value in private cloud computing is more optimal with a downtime of 16 seconds.

The virtual network function (VNF) is a virtual machine with specific software. A set of related VNFs can be represented as a network service (NS). The placement of all the VNFs of an NS can be in a single host node, and in multiple host nodes. Also, the placement of VNFs of an NS can be done using mixed-mode. In this work, we first analyze the NS availability considering the deployment of NS with the use of multiple host nodes, single host node, and mixed-mode. In the availability analysis, we consider the failure perspective of VNFs as well as the failure perspective of host node(s). Further, we analyze the NS reliability considering the placement of VNFs of NS based in different host nodes, single host node, and mixed-mode. Then we compare the availability as well as the reliability of NS considering these three placement strategies. Comparison results show that the availability, as well as the reliability, are better considering single host node based placement of VNFs of NS. [ABSTRACT FROM AUTHOR]

The scope of the 5G network is not only limited to the enhancements in the form of the quality of service (QoS), but it also includes a wide range of services with various requirements. Besides this, many approaches and platforms are under the umbrella of 5G to achieve the goals of endto-end service provisioning. However, the management of multiple services over heterogeneous platforms is a complex task. Each platform and service have various requirements to be handled by domain experts. Still, if the next-generation network management is dependent on manual updates, it will become impossible to provide seamless service provisioning in runtime. Since the traffic for a particular type of service varies significantly over time, automatic provisioning of resources and orchestration in runtime need to be integrated. Besides, with the increase in the number of devices, amount, and variety of traffic, the management of resources with optimization becomes a challenging task. To this end, this manuscript provides a solution that automates the management and service provisioning through multiple platforms while assuring various aspects, including automation, resource management and service assurance. The solution consists of an intent-based system that automatically manages different orchestrators, and eliminates manual control by abstracting the complex configuration requirements into simple and generic contracts. The proposed system considers handling the scalability of resources in runtime by using Machine Learning (ML) to automate and optimize service resource utilization. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the frontier technologies, which over the last decade has gained a widespread commercial and educational user base. OpenStack is one of the popular open source cloud management platforms for establishing a private or public Infrastructure-as-a-Service (IAAS) cloud. Although OpenStack started with very few core modules, it now houses nearly 38 modules and is quite complex. Such a complex software bundle is bound to have an impact on the underlying hardware utilization of the host system. The objective is to monitor the usage of system resources by OpenStack on commodity hardware. This paper analyzes the effect of OpenStack on the host machine's hardware. An extensive empirical evaluation has been done on different types of hardware, at different virtualization levels and with different flavours of operating systems comparing the CPU utilization, memory consumption, disk I/O, network, and I/O requests. OpenStack was deployed using Devstack on a single node. The novel aspect of this work is monitoring the resource usage by OpenStack without creating virtual machines on commodity hardware. From the analysis of data, it is observed that standalone machine with Ubuntu server operating system is the least effected by OpenStack and thereby has more available resources for computation of user workloads. Doi: 10.28991/esj-2020-01246 Full Text: PDF

Era Pandemi menyebabkan peningkatan penggunaan aplikasi konferensi video yang tiba-tiba dan siginifikan. Bagi perusahaan harus segera beradaptasi dalam hal aplikasi komunikasi. Salah satu aplikasi yang bisa digunakan yaitu openmeetings. Openmeetings adalah aplikasi yang digunakan sebagai pengatur konferensi yang terinstal pada server. Umumnya server dibangun menggunakan komponen fisik, namun memiliki keterbatasan sehingga seringkali mengalami penurunan performa dalam segi kecepatan server dalam menjalankan layanan. Salah satu upaya meningkatkan performa server harus menambah atau mengganti perangkat keras sehingga kurang menguntungkan pada biaya operasional. Konsep Cloud selain efisien dari segi biaya operasi server, juga handal dari segi ketersediaan layanan. Sistem Cloud merupakan sebuah model Client-server, dapat diakses oleh pengguna dimana saja dan kapan saja. Membangun Cloud salah satunya dapat menggunakan software open source, yakni Openstack. Penelitian melakukan perbandingan antara harga komponen cloud dengan komponen fisik, kemudian melakukan pengujian Video Conference yang di jalankan dalam Openstack untuk mengetahui kinerja dari sisi Quality of Service (QoS) meliputi delay, packetloss, jitter dan throughput. Hasilnya disimpulkan menggunakan cloud lebih efisien dibandingkan dengan menggunakan server fisik. Nilai rata-rata terbesar pada sisi client, dengan throughput sebesar 639.85 kbps, delay sebesar 30.22 ms, jitter sebesar 10.35 ms dan packetloss sebesar 0.94%, untuk nilai rata-rata terbesar pada sisi server, dengan throughput sebesar 1,856.55 kbps, delay sebesar 10.19 ms, jitter sebesar 6.18 ms dan packetloss sebesar 0.87%.

Cloud-based technologies are driving positive changes in the way organizations can communicate. In running a global business, the need for travel and being available in meetings is a must. However, with expensive travel costs, an alternative solution to overcome this problem is required. This paper presents, a new approach that enhances current 2D video calls to 3D video calls benefiting from the unlimited features of the cloud-computing infrastructure. Three technologies were implemented, OpenStack cloud, webRTC call and 3D anaglyph effect to achieve the sense of 3D video. Experiments proved that our approach has better CPU performance by 47% compared to 65% for traditional peer-to-peer 3D video call. In addition, our implementation has better PSNR value by 0.991 dB compared to 3D video call on peer-to-peer. Survey results stated, 60% of participants found our anaglyph video has a good effect, and 45% found that our video is 75% similar to YouTube video.

Abstract Cloud computing is a novel computing paradigm, which connects plenty of computing resources and storage resources via Internet. Cloud computing provides a number of high-quality services, such as cloud storage, outsourcing computing, and on-demand self-service, which have been widely accepted by the public. In cloud computing, by submitting their tasks to cloud, plenty of applications share huge computation and storage resources. However, how to schedule resource efficiently is a big challenge in cloud computing.In this paper, we propose a SLA-aware resource algorithm to enable cloud storage more efficiently. In our scheme, we take advantage of the back-end node space utilization and I/O throughput comprehensively simultaneously. We compare and contrast the existing scheduling storage policies by implementing those algorithms. The extensive tests show that our algorithm achieves a considerable improvement in terms of violation rate and the number of used hosts.

More engaging and accessible solutions that offer outstanding user experience are needed. The virtual desktop infrastructure (VDI) is the first user access part of the cloud. Therefore, the performance of the VDI has a significant influence on the network. The graphics processing unit (GPU) can accelerate performance by assigning each virtual machine a dedicated GPU for performance-boosting to improve the user experience. In this paper, we propose a high-performance VDI using an integration of a GPU on OpenStack with a PCI pass-through. It might have certain benefits over using virtualized hardware, such as lower latency, better performance, and more functionality. The evaluation and comparison of the computer processing unit (CPU) and GPU are presented among the different benchmark tools.

The objective of the research was to compare network software technologies to evaluate the performance of switches based on year of inception, countries implemented, versions, type of software, operating system, programming language, features, tools, benefits, and cost range of implementation. It was concluded that OpUtils is the licensed software with more experience in the market in the technological platforms based on the evaluation of the performance of the software to manage the switches being one of the national and international companies that had a great apogee for its low cost and its quality service. On the other hand, Open vSwitch is the software that shows a high-level index in terms of software updates that allow blocking errors in the source code and patches in poorly developed configurations and maintain security in the management of ports and interfaces for switches. This is due to the fact that the software is based on open-source code allowing to optimize its processes. Finally, it is possible to maintain high standards for privileged users, stop anomalies in real time and stop the cause of the problem is necessary to use a licensed software such as Cisco Open SDN Controller for its full functionality. Finally, some recommendations for future studies were indicated, such as: (a) to perform a classification research covering more than 6 switch management software to increase the alternatives for the selection of a complete software that meets the needs of the stakeholders, (b) to increase the number of variables with the aim of taking the research to a deeper study to provide the support to technical network administrators in the security of devices (switches). [ABSTRACT FROM AUTHOR]

The cloud computing has inherent challenges to detect the Hyper Text Transfer Protocol (HTTP) flooding Distributed Denial of Service (DDoS) attack due to its natural characteristics like virtualization, elasticity and multi-tenancy. The usage of cloud computing is user-friendly, but the implementation of the cloud infrastructure such as compute node, networking, cloud storage is very complex in order to achieve its various characteristics. Similarly, detecting the HTTP flooding attack in the cloud is also very complex as it requires an understanding of various potential attack paths in such a complex environment. So, designing the cloud testbed framework to detect the HTTP flooding attacks is a challenging problem to be solved. The cloud testbed framework has to consider several aspects of attack scenarios while accounting the cloud characteristics. This paper reviews the existing DDoS attack detection framework and their gaps and proposes a cloud testbed framework for evaluating the HTTP flooding DDoS attack solution. The proposed framework is implemented using the OpenStack cloud environment. The Fédération Internationale de Football Association (FIFA) World Cup 1998 real-time dataset is used to generate the HTTP flooding attack to the OpenStack cloud testbed framework for the experimentation. [ABSTRACT FROM AUTHOR]

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructureoriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge. [ABSTRACT FROM AUTHOR]