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7Conclusion
With the advent of blockchain technology and smart contracts, it becomes possible to replace centralized arbitration with a decentralized infrastructure, which is focused on increasing the speed and efficiency of the implementation of the agreement for all participants involved in the agreement, and at the same time reducing possible risks and costs. Smart Contracts - this is an evolving and extensible platform that allows developers to model and generate their functional code for deployment on multiple blockchain platforms, and by synthesizing cryptocurrency and smart contracts provides great opportunities for legal-binding enforceabil-ity in various types of industrial fields.
According to Joshua Fairfield, it is possible to bring technology and law together so that, in addition to the proper functioning of the system, maximum protection of the rights of the parties is possible. He argues that law can develop side by side with technology, because law is a kind of technology that people create and
help them succeed in the face of technological development. [7].
References
1. V. Metreveli, Foundations of Roman Law, Tbilisi 2005, p. 72
2. Commentary on the Georgian Civil Code, Book Three, Tbilisi., 2001, p. 44
3. Smart Contracts: A Legal Framework and Guidance for Informed Legislators, 2018, p. 9
4. https://bitcoinethereumnews.com/crypto/nick-szabo-who-is-he-and-what-is-his-influence-on-mod-ern-cryptocurrencies-crypto-news/
5. https://nakamotoinstitute.org/smart-contracts/
6. https://ethereum.org/en/developers/docs/smart-contracts/languages/
7. Joshua A.T. Fairfield "Runawey Tecnology Can Law Keep Up?" Cambridge University Press 2021
CLOUD COMPUTiNG: A REViEW OF THE AVAiLABLE PLATFORMS
Seyidova I.,
Candidate of Technical Sciences Azerbaijan State Oil and Industry University, Azerbaijan
Hashimov O. Master
Azerbaijan State Oil and Industry University, Azerbaijan DOI: 10.5281/zenodo.7479794
ABSTRACT
Cloud computing is a technology that has been developing and up-to-date in recent years. All transactions made on personal computers through cloud computing have become possible through remote servers. By using cloud computing infrastructures, large-scale storage systems can be installed and high-level calculations can be made without installing server systems.
Examination of data in different dimensions of cloud computing infrastructures will be beneficial in data-based studies on cloud systems. In this study, different dimensions of data on cloud computing infrastructures were examined and what kind of strategies were followed in these dimensions and analysis of various platforms were examined. Based on past studies, it has been tried to predict the analysis of various platforms in cloud computing.
Keywords: Cloud computing, cloud data, dimensions of data in the cloud.
INTRODUCTION
Cloud computing fulfills the long-standing dream of computing as a utility and allows for the rental of IT capabilities, thus representing a modulation point in the natural characteristics of computing and IT service delivery (Rajkumar Buyya., et al., 2011, D. Parkhill ). Cloud computing provides services in the form of infrastructure, platform, or software as a service on a pay-as-you-go model. With the trend of the cloud model, power is shifting to consumers. This paradigm marks a rudimentary but massive shift from the traditional desk-top-as-a-platform model to the Internet-as-a-platform model. To provide infinite scalability, guaranteed performance, easy access, and near-constant availability, these computing platforms are typically deployed in clusters with huge numbers of servers hosted in dedicated data centers (D. Parkhill., et al., 1966). In the cloud, virtualization occurs at several levels. It can range from "what does what" (server and application virtualization) to "what goes where" (storage virtual-
ization) to "who does where" (mobility and virtual networks). The beauty of virtualized solutions is that the user can simultaneously run multiple operating systems on the same host.
Cloud computing has recently emerged as a shortcut to a specific type of data center or, more commonly, a group of data centers. Computing capabilities have become a bottleneck for systems using traditional grid computing, which requires higher hardware requirements. Cloud computing is a kind of computing platform distributed in a large-scale data center, which meets the requirements of scientific research and ecommerce by dynamically providing multiple types of server resources (Wenhong Tian., et al., 2010). The cloud computing platform uses virtualization technology to transparently and dynamically provide virtual computing and storage resources to meet various user requirements according to relative scheduling strategies [10]
1. COMPARATIVE ANALYSIS OF CLOUD COMPUTING
Cloud computing differs from traditional IT approaches in that it focuses on service delivery as well as the consumer usage model. Behind the scenes, service providers use system architecture, specific technologies, industry best practices, and design to enable and support the delivery of a service-oriented and elas-tically scalable environment to deliver better services to multiple customers. Platform as a Service solutions provide application development platforms and environments to seamlessly integrate cloud computing into existing services, applications, and infrastructure with a market-driven approach.
Cloud computing comes from years of advances in grid computing, virtualization, service computing, web computing, and related technologies. Cloud computing provides both platforms and applications on demand over the Internet or intranet (David Bernstein et al. 2010, Armbrust M. et al. 2009, Boss G. et al. 2007; Daniel N. and others, 2008). Some examples of new cloud computing platforms are Amazon EC2 [1], IBM Cloud [6], Google App Engine [11], and Microsoft Azure [8].
The cloud lets you share, aggregate, and distribute software, storage, and network computing resources on demand. Some of the key benefits of cloud computing include complexity hiding and abstraction, virtualized resources, and efficient use of distributed resources (Armbrust, M., et al., 2009). Typically, the cloud can be divided into three categories: public, private, or hybrid, depending on the deployment model [9].
A public cloud (public) is a cloud that is available to the general public on a pay-as-you-go basis. In a typical public cloud, a third-party provider provides services such as computing, storage, networking, virtual-ization, and applications to various customers. Enterprises are adopting public cloud services to screen
capital and operational costs using the elastic scalability of the cloud and market-driven costing features. However, public cloud computing also raises concerns about data security, data transfer, management, performance, and level of control.
A private cloud is an organization's data center that is not accessible to the general public. In a private cloud environment, internal IT resources are used to serve their internal users and customers.
A hybrid cloud is the seamless use of a public cloud along with a private cloud when needed. The focus on service delivery and the consumer usage model distinguishes cloud computing from traditional IT approaches. Rice. 19].
Cloud computing platform as a service (PaaS) is one of the key cloud computing services. PaaS is the provision of a computing platform and environment and solution stack as a service without downloading or installing software for developers, IT managers, or end users, also known as "cloud software". Through virtu-alization and other resource-sharing mechanisms, cloud computing can greatly reduce user costs, which is the need and high demand of the modern user. Virtualization techniques allow multiple logical platforms to be exposed on a single physical machine (Windows, Linux, etc.) so that resources can be better distributed and more users can benefit and be served at the same time. Most cloud computing platforms are based on vir-tualized environments. The laboratory of virtualized cloud computing consists of four main parts: software and hardware platforms (PaaS resources) provided by virtual and real servers; resource management nodes; database servers and users who access these resources via the Internet or intranet [5].
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Picture 1. A bird's eye view of cloud computing.
Cloud computing is an evolving tool. In general, the user may store their data, which may be stored somewhere in some part of the world. There is no need to know about the data, where the data goes, and where their data is. For more details, there are differences in the world of cloud computing as to where the physical data resides, where the processing takes place, and where the data is accessed from. For example, if a user wants to run some application (such as web services), they do not need to have special software or hardware
on their computer to run their application. Cloud computing has the functions of cluster computing, distributed computing, service computing (Xiujuan Zhang., et al., 2010), and service computing (Lijun Mei., et al., 2008). The user needs to run a one-time application, so the user can use the cloud platform as the best choice. In the future, users may have a dummy terminal along with a keyboard and mouse for data transfer. There is no need to invest in resources (software and hardware), instead, they can rent resources. Moreover, they can
choose the best available computing platform as many companies are going to offer cloud applications such as mobile services in the near future. There are many data centers available today where users can store their data up to 5 GB and 10 GB, some cloud services provide 30 GB of space for free through their online operating system. In any case, cloud computing is still in its infancy, many complex problems are waiting to be solved.
2. COMPONENTS OF A CLOUD SYSTEM
The open-source cloud computing systems Eucalyptus, Open Nebula, and Nimbus typically consist of six components.
Hardware and operating system. Hardware and software are the backbones of any physical machine in a cloud system. Proper installation is important for any software system. For two reasons, we can make a special node of the physical hardware and operating system. First, to run pure virtualization, if the physical host processors do not have the necessary hardware, this limits the system to only paravirtualization. Secondly, the commercial cloud computing environment is not as flexible, and the open-source environment must be flexible to work with different systems.
Hypervisor: Hypervisor, also known as Virtual Machine Monitor (VMM). Typically, popular VMMs consist of Xen, KVM, and open-source Virtual Box, while VMware is commercial. These programs provide the foundation for running a virtual machine (VM). Each of these frameworks relies on the Libvirt library. The input to Libvirt may vary slightly depending on the version of VMM you are using. For this reason, different cloud platforms support different hypervisor subsets.
Network: The network is an important component of cloud computing and includes DNS, DHCP, and physical machine subnetting. The virtual bridge, which is also part of the network, provides each virtual machine (VM) with a unique virtual MAC address. To work with a physical host, the DNS and DHCP processes must be configured according to the cloud infrastructure to communicate with the MAC address as well as the virtual machine (VM) IP address.
Platform. The cloud structure itself is an important component of the cloud system. From the front end, the framework process input, through VMM we can manage the VM, and then with the DHCP and IP bridging programs, we can manage the MAC and IP addresses of the VM.
Disk Image: A virtual hard disk is a basic requirement for running a virtual machine. When we need one virtual machine on one physical machine, the virtual machine installs the operating system and other software after creating the blank disk image. However, from cloud computing, we expect hundreds of virtual machines to be built and pulled down quickly, but installing a full operating system on each virtual machine is not practical. To avoid this problem, cloud computing has a desktop image source, we can easily copy this desktop image to any virtual machine based on which this virtual machine can run. In any cloud, we need to distinguish between two different disk images: a template disk image and a runtime disk image. Template disk images are images stored in a disk image source that can be used across multiple virtual machines.
Front-End: For a user request, there must be an interface through which the user can interact with the virtual machine (VM), and specify the parameter to enter the created VMs. The front end of some cloud computing systems performs various types of scheduling to allocate certain resources to a user for which they are allowed. In addition, one of the most customizable elements of the entire cloud system is the front end.
The role of open-source cloud computing is to create a digital identity management mechanism (A.Ca-voukian., et al., 2008), and outlines some of the technological building blocks needed for controlled trust and identity verification. Open Nebula and Nimbus are technically sound and popular. The current cloud focuses on the problem of interoperability, which is necessary for an enterprise cloud system. Most open-source clouds are provided by IaaS.
Table 1.
Comparison of open source cloud platforms
Feature OpenNebula Eucalyptus Nimbus
Computing architecture - Cluster in the IaaS cloud - Focused on efficient, dynamic, and scalable management of virtual machines in data centers (private cloud), including a large number of virtual and physical servers. - Based on the Haizea plan - Ability to set up multiple clusters, each with private internal network addresses, in the same cloud. - Private cloud. - Science cloud - Client-side cloud computing interface for TeraPort cluster with Globus support - Nimbus Context Broker, which combines multiple deployed virtual machines into turnkey virtual clusters. - Heterogeneous clusters of autoconfigured VMs with one command
Virtualization management - Xen KVM and on-demand access to Amazon EC2- VMWare - Xen hypervisor - KVM - Xen Virtualization
Service IaaS IaaS IaaS
Load balancing - Nginx server configured as a load balancer, round robin is used -Simple cloud controller with load balancing - Runs a self-tuning virtual cluster, i.e. context broker.
Compatibility -Interaction between in-cloud services - Multiple cloud computing interfaces using the same "internal" infrastructure. -Standards: "rough consensus and working code"
Fault tolerance -The daemon can be restarted and all running virtual machines will be restored. -Persistent database to store host and virtual machine information - A separate cluster in the Eucalyptus cloud reduces the chance of a correlated outage - Periodic check of working units and restoration
Safety - Firewall, virtual private network tunnel -WS-security for authentication, cloud controller generates public/private key -PKI credentials required -Works with grid proxy VOMS, Shibboleth (via GridShip), Custom PDPs
Programming environment -Java, Ruby -Hibernate, Axis2 and Axis2c, Java Python, Java
Storage -Database, persistent storage for ONE data structure The SQLite3 backend is the core component of OpenNebula's internal data structures. -Walrus (interface for storage subsystem) - FTP and SCP mesh
Deployment Dynamic Deployment Dynamic Deployment Dynamic Deployment
Cloud type Private Public Public
Os support Linux Linux Linux
Scalability Dynamical, Scalable Scalable Scalable
Deployment Method Command line Command line Command line
Structure Modules Modules Lightweight components
As can be seen from Table 1, there are many similarities between these solutions. All of them are scalable, and provide support for Linux, and IaaS, allowing you to use the Java language. But there are differences, for example, OpenNebula, although it is a more mature solution, does not support EC2. Also, OpenNeblua is private. Eucalyptus is the answer to EC2 and supports large application deployments. Nimbus is a "scientific" cloud
3. CLOUD PLATFORM AS A SERVICE (PAAS)
There are various cloud computing platforms in our global network; each of them has its characteristics and advantages (Nawsher Khan., et al., 2011). For a better understanding, we analyze these platforms and give a comparison from different aspects of implementation.
Table 2.
Comparison of some cloud computing platforms
Various platforms
Platforms Amazon Elastic Compute Cloud (EC2) Microsoft Azure Google App Engine Sun Network.com (Sun Grid) GRIDS Lab Aneka
Focus Infrastructure Platform Platform Infrastructure Enterprise clouds
Service Type Computing Storage (Amazon S3) Web and non-web applications Web applications Computing Computing
User Access Interface Amazon EC2 command-line tools Microsoft windows azure portal Web-based administration scripts, Sun Grid web portal Work-bench, web-based portal
Additional Service Providers Yes Yes No Yes No
Virtualization The OS layer runs on the Xen hypervisor OS level via factory controller Application container Job management system (Sun Grid Engine)) Resource manager and scheduler
Web API Yes Yes Yes Yes Yes
Dynamic QoS negotiation No No No No SLA-base resources reservation
Programming environment Amazon Machine Images (AMI) Microsoft.NET Python Solaris OS. Java, C, C++, FORTRAN APIs supporting models in c# .Net
As can be seen from Table 2, there are also many similarities between these solutions. All of them provide web API support and have a friendly interface. But there are differences, for example, AWS is a more mature solution, has many built-in services (which, however, can be considered both as an advantage and a disadvantage), is the most common, but difficult for beginners, and is not suitable for hybrid clouds. Microsoft Azure, in turn, provides perfect integration with Microsoft services and is ideal for hybrid clouds. Google Cloud, on the other hand, is most often intended for Big Data computing, Machine Learning, and Cloud - native applications
Cloud computing and conventional computing data centers are being built in seemingly unexpected places such as Quincy, Washington (Google, Microsoft, Yahoo!, etc.) and San Antonio, Texas (Microsoft, US National Security Agency, etc.). The motivation for choosing these locations is that electricity, cooling, labor, real estate purchase costs, and taxes vary geographically, and of these costs, electricity and cooling costs alone can account for a third of a data center's costs. A successful example is Elastic Compute Cloud (EC2) from Amazon Web Services (AWS), which sells 1.0 GHz x86 ISA "chunks" for $0.10 per hour, and a new "chunk" or instance can be added in 2-5 minutes. Amazon Scalable Storage (S3) service charges $0.12 to $0.15 per GB per month, with an additional $0.10 to $0.15 per GB bandwidth charge for moving data in and out through the Internet.
CONCLUSION
Cloud computing is a promising paradigm for delivering IT services in the form of computing tools. At present, the thesis has been a comparative analysis of high probability and closed-source cloud platforms. In analyzing various cloud computing environments, we found that significant philosophical differences apply
between them regarding their overall design scheme as well as their scope of use. Also, we found that while AWS and Microsoft Azure are still the most popular solutions at the moment, open-source cloud solutions are becoming more and more popular. After this analysis, the user will be able to better understand the characteristics and make the right choice of cloud platform, implementation, and deployment requirements.
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1. Amazon EC2, http://aws.amazon.com/ec2/,
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