Cloud Infrastructure Services Research Articles

Research and design of urban flood control and drainage dispatching system

In order to solve the problem of frequent flood disasters in urban areas, water conservancy models such as hydrological forecast, flood simulation and operation decision-making were studied and constructed for the waterway-region-urban flood control and drainage engineering facilities, and urban flood control and drainage operation system was designed with the model as the core. The overall structure of the system is "five horizontal and two vertical". The "five horizontal" bottom up are respectively the intelligent perception layer, cloud infrastructure layer, data layer, service layer and application layer, and the "two vertical" are respectively the security system and the standard specification system. On this basis, the function customization of the system is completed to achieve comprehensive display, early warning and forecast, flood simulation, risk assessment, project scheduling and intelligent decision-making functions.

Ofcom Refers the Market for Cloud Infrastructure Services to the CMA for an In-Depth Competition Probe

Ofcom Refers the Market for Cloud Infrastructure Services to the CMA for an In-Depth Competition Probe

INSTABILITY OF CLOUD INFRASTRUCTURE RESOURCES AND SERVICES

Increasing non-stationarity of cloud infrastructure resources and services leads to a significant decrease in its productivity. Therefore, the purpose of the article is to identify the reasons for non-stationarity of cloud infrastructure resources and services; finding ways to reduce the level of non-stationarity . As a result of the research, the following results were obtained . Sources leading to the specified non-stationarity are identified . Existing approaches to reducing non-stationarity are analyzed . An example of basic resource allocation using standard linear programming methods is given. Variants of application of these methods for dynamic redistribution of resources are shown. Conclusion. Allocation and dynamic redistribution of resources in the cloud infrastructure can be done using standard linear programming methods. But due to the significant non-stationarity of the cloud environment, the proposed approach will reduce the productivity of cloud resources. At the same time, with an increase in the number of variables and restrictions, the computational complexity of the proposed algorithm will grow exponentially . Therefore, it is necessary to look for other approaches for the distribution and redistribution of cloud resources in conditions of significant non-stationarity .

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.

Reviewing a New Optimized an ANFIS-Based Framework for Detecting Intrusion Detection System with Machine Learning Algorithms (Deep Learning Algorithm)

Today's world is becoming more interconnected due to the current global internet, communication, or ways of doing business that have recently shifted to cloud computing platforms in order to increase their speed and productivity. But such can also be affected by cyber attacks on cloud infrastructure services to be executed on various cloud platforms, increasing the number of attacks on such systems to neutralize any harm caused by a cyber attack on such cloud-based infrastructure. Although network administrators utilize intrusion detection systems (IDS) to detect threats and anomalies, they frequently only make available post-attack ready to act in cyber warfare. If we could predict risky behavior, network administrators or security-enhancing software could intervene before harm was done. Incoming intrusion detection messages should be viewed as a sequence. The fundamental function of an intrusion detection system (IDS) is to distinguish between regular and abnormal network traffic. As a result, robust intrusion detection systems (IDS) using deep learning model are required to find such cyber risk in form of threats and anomalies on cloud based infrastructure.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ МАСШТАБУВАННЯ АРХІТЕКТУРИ ХМАРНИХ ДОДАТКІВ

Abstract. The areas of improvement of the method of dynamic load balancing for very heavy calcula-tions and tasks with scaling on cloud infrastructure, which differ from known methods, are considered. An analysis of existing methods and algorithms that provide solutions to load balancing problems in cloud in-frastructures was carried out. Methods of load balancing are considered. A model of the load distribution system for cloud infrastructure services based on a modification of the dynamic load balancing method has been developed. Experimentally verified characteristics of the created model. Comparison of available load balancing methods. The practical implementation of tracking results allows to reduce the processing time of tasks that come from the messaging system and enables flexible configuration of servers and applications. The conducted experimental studies showed that the processing time of operations decreases when using the proposed method of load balancing. Keywords: cloud applications, cloud infrastructure, servers, load balancing, message exchange system.

Modified Computational Ranking Model for Cloud Trust Factor Using Fuzzy Logic

Through the use of the internet and cloud computing, users may access their data as well as the programmes they have installed. It is now more challenging than ever before to choose which cloud service providers to take advantage of. When it comes to the dependability of the cloud infrastructure service, those who supply cloud services, as well as those who seek cloud services, have an equal responsibility to exercise utmost care. Because of this, further caution is required to ensure that the appropriate values are reached in light of the ever-increasing need for correct decision-making. The purpose of this study is to provide an updated computational ranking approach for decision-making in an environment with many criteria by using fuzzy logic in the context of a public cloud scenario. This improved computational ranking system is also sometimes referred to as the improvised VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. It gives users access to a trustworthy assortment of cloud services that fit their needs. The activity that is part of the suggested technique has been broken down into nine discrete parts for your convenience. To verify these stages, a numerical example has been evaluated for each of the six different scenarios, and the outcomes have been simulated.

System Observability and Monitoring for Multi Cloud Native Network Service based Function

Abstract: The simplicity and agility of public Cloud infrastructure is driving large service based and application oriented Enterprises to host their business functions as Cloud Native Services and Applications. These Enterprises often host their applications and services in multiple cloud infrastructures either due to business, compliance and functionality requirements. On these cloud infrastructure platforms, applications and services are primarily designed and deployed as microservices which are small and independent services and functions. Each service microservice is typically self contained and interacts with other services using predefined Service based interfaces or APIs. As these services could be hosted in multiple cloud environments on infrastructures such as virtual machines and containers, there’s a need to provide a holistic visibility of services across many infrastructures, form factors and service interfaces. Keywords: Microservice, API, Virtual Machine, Visibility, Multicloud.

Retraction Note to: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

Retraction Note to: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

Retraction Note to: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services

Retraction Note to: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services

LB Scalability: Achieving the Right Balance Between Being Stateful and Stateless

A high performance Layer-4 load balancer (LB) is one of the most important components of a cloud service infrastructure. Such an LB uses network and transport layer information for deciding how to distribute client requests across a group of servers. A crucial requirement for a stateful LB is per connection consistency (PCC); namely, that all the packets of the same connection will be forwarded to the same server, as long as the server is alive, even if the pool of servers or the assignment function changes. The challenge is in designing a high throughput, low latency solution that is also scalable. This paper proposes a highly scalable LB, called Prism, implemented using a programmable switch ASIC. As far as we know, Prism is the first reported stateful LB that can process millions of connections per second and hundreds of millions connections in total, while ensuring PCC. This is due to the fact that Prism forwards all the packets in hardware, even during server pool changes, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">while avoiding the need to maintain a hardware state per every active connection</b> . We implemented a prototype of the proposed architecture and showed that Prism can scale to 100 million simultaneous connections, and can accommodate more than one pool update per second.

Development of a prototype of a private cloud based on OpenStack using automatic deployment tools

The article is devoted to the topical issue of finding effective methods of implementing a private cloud based on the OpenStack cloud platform. The problem of creating a private cloud based on the OpenStack cloud platform with the help of automatic deployment tools to explore the possibility of virtualization of data centers. The article believes that OpenStack is a set of free software modules that can be used to create cloud infrastructure services and cloud storage. It is shown that OpenStack has advantages: flexibility, scalability, cost-effectiveness, which allows for virtualization of data centers based on open source software. Hence, OpenStack is ideal for heterogeneous infrastructures and can work in conjunction with other enterprise and open source technologies. It is shown that OpenStack allows you to solve the most complex IT tasks to date: from processing «Big Data» to «Internet of Things» (IoT, Internet of Things), due to the fact that OpenStack APIs are compatible with Elastic Compute services Cloud (EC2) and Simple Storage Service (S3), so they can be ported to discrete OpenStack environments. Therefore, many businesses and service providers expect OpenStack to help them turn their data centers into scalable automated matrices of physical resources and virtualized services. avoid binding to the supplier, prisk innovate, increase scalability and cost-effectiveness. Therefore, these disparate data centers, controlled by automation and API programming interfaces, can support the application of the DevOps model in providing software for innovative mobility programs, social networks, and many other types of applications based on NFV (Network Function Virtualization). The article presents the following task: to build a private cloud based on the OpenStack cloud platform, it is necessary to determine a set of free software that can be used to create cloud infrastructure services and cloud storage, both public and private. You also need to identify effective methods for deploying a private cloud based on OpenStack using automatic deployment tools, namely: installing and configuring a private cloud based on Openstack on a dedicated server. To solve this problem, you need to create a prototype of an open platform in the cloud based on OpenStack (test models).

Framework and deployment of a cloud-based advanced planning and scheduling system

• This paper presented a cloud-based and advanced planning and scheduling (C-APS) system framework, it can be used as a reference for system developers to develop a cloud-based and object-oriented APS with simulation-based scheduling engine that can be applied in practice. • This paper proposed the service structure and approach of deploying the C-APS system in the public cloud infrastructure, platform, and service provider (e.g., AWS TM ) and hybrid cloud platforms. Readers may reference this approach to deploy a server-based enterprise information application system to a cloud-based system. • This paper presented the package diagram to collect the necessary data for building the case company's virtual factory model required by the C-APS system; this feature can significantly improve modeling efficiency and data stability. Besides, the C-APS's objected-oriented, simulation-based scheduling engine has the characteristics of quickly expanding and adjusting to the virtual plant model. • The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain; the scheduling quality is high; the computing time is short and acceptable for practical application. Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application.

RETRACTED ARTICLE: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services

The cloud computing provides on demand access to shared resources over internet in a cloud platform powerfully adaptable and metered way. Cloud computing empowers the user get to wherever to a shared pool of configurable resources and gives different administrations to the resource assignment like scientific operations, services computing through virtualization. To give guaranteed productive execution to clients, tasks ought to be proficiently mapped to accessible resources. In this manner, Task Scheduling is noteworthy issue in the cloud infrastructure administrations. The essential target of task execution planning includes reserving the infrastructure assets and limiting the goal of the execution plan. In this research work, we proposed metaheuristic optimization technique with load balancing to enhance the cloud infrastructure service provider’s performance there by depleting the scheduling issues. The proposed technique is pertinent for static and dynamic task condition, where static methods VM parameters are fixed, dynamic means parameters are chosen runtime. The proposed algorithm consists of two phases MHOS-S and MHO-D for dealing with static and dynamic properties of the task submitted. The result analysis by comparing with few traditional metaheuristic algorithms proves that the proposed technique performs better in complex environments.

RETRACTED ARTICLE: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

In cloud computing infrastructure-based services, resource scheduling is still an open issue. Normally resource scheduling involves multi-objective fulfillment but often developed as single-objective problems and solutions are proposed. For dealing with multi-objective problems, optimization techniques come in-aid to develop various techniques as cloud resource scheduling is a soft computing problem. The ultimate aim of cloud resource scheduling is to reduce the billing cost of users and to increase the revenue of cloud service providers. In this paper, the MCAMO technique is proposed for cloud resource scheduling especially dealing with infrastructure-based cloud services. This method deals with multi-objective by applying multi constraints while resource scheduling in infrastructure cloud services. The proposed method is novel as it deals with the constraints of the submitted jobs along with fulfilling the objectives of the cloud service client. For a powerful arrangement, the fitness value worth takes a base worth value and the improved determination of the asset resources relies upon the MCAMO calculation. The performance of the MCAMO technique is assessed by comparing through few existing multi-objective constraints applied VM machines scheduling techniques using the cloudsim simulator. The comparison proves that the proposed MCAMO technique provides optimized resource scheduling than other methods.

Federated Service Chaining: Architecture and Challenges

Emerging edge computing has seen latency- sensitive services moving rapidly from cloud to the edge to take advantage of its close vicinity to end users, while cloud is retained for carrying out latency-insensitive and computation-intensive tasks. When more edge computing service providers come to the market, the network will become increasingly more fragmented because of their proprietary services and policies deployed in the network. This means that the Internet can become more cumbersome and riskier as there will be more tiers and potential vulnerabilities that could be exploited. To tackle this issue, we envisage a federated service chaining paradigm in which operators can share and put service functions in other participants' networks so as to improve resource utilization, collaboratively mitigate cyber threats, and enable service innovations. In this position article, building on our past experience in enabling federated cloud infrastructure and heterogeneous service chaining, we present a Federated Service Chaining architecture followed by discussions of its key components. Several key research challenges are described for the successful realization of such architecture. We hope this article can open a discussion in the research community and generate enough research interest to significantly advance this field.

Decentralizing Privacy Implementation at Cloud Storage Using Blockchain-Based Hybrid Algorithm

Cloud computing (CC) is a distributed and centralized network of interconnected and interrelated systems that has emerged as an area of incredible impact, potential, and growth in the IT domain. In the last few years, this paradigm has thrived to become the fastest evolutionary segment for business and various industries. Despite the many advantages provided by the cloud, serious challenges related to privacy and security have arisen such as privacy exposure, data loss, manipulation, service-level agreement (SLA) violation, and concerns over the service and deployment models. In this article, a blockchain-based hybrid algorithm is proposed to tackle the privacy inefficiency of existing tools. A novel hybrid technique is used to encrypt data ahead of outsourcing it to data centers, and a unique digital signature is generated at clients and stored on a decentralized set of blocks. A virtual cloud similar to actual cloud service infrastructure was built to test the proposed framework. Despite the additional computational powers needed to run the proposed framework due to blockchain integration, the results show that data integrity and reliability are preserved and user’s privacy is increased. Our results are discussed and benchmarked against the standard privacy tests standards such as changes in the stored data, the negligible overheads on cloud performance, and the data records of the blockchain structure.

Modelling Methods in Solving Research and Design Problems in Architecture Activities

The modern architectural and city-planning science requires combining the efforts of a number of various research teams, including representatives of different branches of scientific knowledge, with the purpose of designing the most rational and efficient methods of research organization. Due to the dynamic nature of architectural and city-planning objects and quick alteration of the parameters of their elements and components, prompt methods of solving research, designing or forecasting problems are necessary. The era of the fifth technological paradigm is characterized with the establishment of such branches of industry as electronics, microelectronics, information technologies. Since 2010ies we live in the phase of the sixth technological paradigm development, in which the production processes are based on cloud infrastructure and virtual services. One of methodological preconditions in the further development of architecture is the search for optimal solutions in the process of research and designing developments, which could be the most quickly and efficiently implemented by means of computers and information models. For this purpose, first of all, the appropriate formalization of architectural objects is necessary – i.e. creating functional mathematical models and software products for implementing the process of research and solutions optimization in computing techniques. Current studies in the sphere of architecture more and more lead to realizing the necessity of system approach to research and designing of architectural objects as complicated systems, having the hierarchic structure. The most promising are the system methods of research, designing and expert evaluation of architectural objects of any degree of complexity.

Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem

In recent years, scheduling problems have attracted enormous attentions from practitioners and researches in manufacturing systems, for instance, the scheduling of computing resource in cloud infrastructure and cloud services. The scheduling problems in cloud services, big data and other service-oriented computing problems are regarded as non-separable problems. In this paper, a hybrid biogeography-based optimization with the enhanced mutation operator and CMA-ES (HBBO-CMA) is proposed to enhance the ability of exploitation on non-separable problems and alleviate the rotational variance. In the migration operator, the rotationally invariant migration operator is designed to reduce the dependence of BBO on the coordinate system and control the diversity of population. In the mutation operator, an enhanced mutation operator, which is sampled from the mean value and stand deviation of the variables of population, is employed to effectively escape the local optimum. Furthermore, the CMA-ES, which has outstanding performance on the non-separable problem, is applied to extend the exploitation of HBBO-CMA. Experimental results on CEC-2017 demonstrated the effectiveness of the proposed HBBO-CMA.