Cloud Management Research Articles

Decentralized access control for secure microservices cooperation with blockchain

With the rapid advancement of cloud-native computing, the microservice with high concurrency and low coupling has ushered in an unprecedented period of vigorous development. However, due to the mutability and complexity of cooperation procedures, it is difficult to realize high-efficient security management on these microservices. Traditional centralized access control has the defects of relying on a centralized cloud manager and a single point of failure. Meanwhile, decentralized mechanisms are defective by inconsistent policies defined by different participants. This paper first proposes a blockchain-based distributed access control policies and scheme, especially for microservices cooperation with dynamic access policies. We store the authorized security policies on the blockchain to solve the inconsistent policy problem while enabling individual management of personalized access policies by the providers rather than a central authority. Then we propose a graph-based decision-making scheme to achieve an efficient access control for microservices cooperation. Through the evaluations and experiments, it shows that our solution can realize effective distributed access control at an affordable cost.

Performance Evaluation of Communication Infrastructure for Peer-to-Peer Energy Trading in Community Microgrids

With the rapidly growing energy consumption and the rising number of prosumers, next-generation energy management systems are facing significant impacts by peer-to-peer (P2P) energy trading, which will enable prosumers to sell and purchase energy locally. Until now, the large-scale deployment of P2P energy trading has still posed many technical challenges for both physical and virtual layers. Although the communication infrastructure represents the cornerstone to enabling real-time monitoring and control, less attention has been given to the performance of different communication technologies to support P2P implementations. This work investigates the scalability and performance of the communication infrastructure that supports P2P energy trading on a community microgrid. Five levels make up the developed P2P architecture: the power grid, communication network, cloud management, blockchain, and application. Based on the IEC 61850 standard, we developed a communication network model for a smart consumer that comprised renewable energy sources and energy storage devices. Two different scenarios were investigated: a home area network for a smart prosumer and a neighborhood area network for a community-based P2P architecture. Through simulations, the suggested network models were assessed for their channel bandwidth and end-to-end latency utilizing different communication technologies.

Research on the risk management of enterprise cloud accounting application under the background of big data

Under the background of big data era, cloud accounting as an important basis for enterprise construction and innovation development, can not only improve the efficiency of enterprise accounting work, but also improve the application mode of traditional accounting information. According to the analysis of the application of enterprise cloud accounting in recent years, although the new technology concept has improved the enterprise financial management mode, it also brings certain risks to the innovation and development of enterprises. Therefore, some scholars have increased the risk assessment of cloud accounting in the research, and built a cloud accounting system based on big data technology, so as to quickly explore and deal with the hidden security problems. After understanding the basic concept and functional characteristics of Yuan accounting, this paper mainly studies the structure of enterprise cloud accounting system under the background of big data, and determines effective measures of risk management from the perspective of long-term development, which is of great importance to the innovation and development of enterprises in the new era.

FEATURES OF CREATING MANAGEMENT ACCOUNTING OF THE ENTERPRISE BASED ON CLOUD TECHNOLOGIES

This article is devoted to the application of cloud technologies in the management accounting automation system at enterprises. The author considered the terminology of "cloud" and "cloud technologies" and adapted the definition of "cloud management accounting technology" as a technology that allows remote processing and storage of data for thepurpose of making management decisions and managing an economic object. The main advantages of using cloud technologies in management accounting were systematize. The advantages such as a business scaling and its information security, communication speed and data access, backup, update and administration of cloud technologies and databases. In this article, the author singles out specific tasks of management accounting that affect the development and use of information technologies in its automation. The tasks such as: timely provision of information to officials in order to make management decisions for each group of information users; provision of real, timely and comprehensive information about the object of management; provision of a wide range of modern tools for analysis and management decision-making. The issue of processing accounting information in the clouds and setting up communication between the internal and external environment is covered. The author also defined the procedure for processing accounting information based on cloud technologies, which consists of four gradual stages.The author has defined the features of building management accounting of the enterprise based on cloud technologies. The features among which are: taking into account the range of users for whom the management cloud of the enterprise will be available; determination of a specific list of management tasks that can be automated on the basis of cloud technologies; selection, in accordance with the first two features, of a specific list of cloud tools

Development and application of a sports health cloud management platform model based on internet of things technology

AbstractThe real‐time sports and physical sign data display of various intelligent sports equipment and software can provide help for professional athletes to formulate sports strategies. However, for those who do not have any special knowledge, visual data cannot help them to make correct sports plans. Based on the above problems, this paper designed and implemented a cloud computing platform that can collect the user's movement and physical sign information, so as to provide personalized sports prescription service for them. In the performance test of the platform, the experimental results showed that the 50% line response time value and 90% line response time value were the maximum when the number of test threads reaches 1000 and 900, and the maximum value was 2896 and 3136 ms respectively; when the number of test threads was 200, the minimum value was 86 and 132 ms. Therefore, it is very necessary to develop and apply the sports health CMP based on the Internet of Things (IoT) technology.

Agro Analysis System for Precision Agriculture

Abstract: Huge amount of data is collected by the sensors from the end. Subsequently, this considerably big amount of data must be processed, analyzed and stored in a cost effective way. In this manner, an enormous pool of computing resources and storage must be provided to compute this vast amount of data. We focused on introducing the latest technologies such as sensors, WSN to radically revise approaches to agriculture by collecting the data about the various parameters of soil, analyzing the data and performing the computations, giving the best optimal solutions for the farming. The application of computing in the agricultural economy will open up a vast range of prospects, such as the vast storage of agriculture information, the cloud management of agricultural production process, the storage of agricultural economy information, early-warning and policymaking based on the agricultural products market, the tracing management of agricultural products quality.

Practical Efficient Microservice Autoscaling

Motivation. In recent years, the adoption of microservices in production systems has been steadily growing. With their loosely-coupled and lightweight components, microservices are easier to manage than traditional monolithic applications. However, microservices introduce new challenges towards efficient resource management because their high number of individually manageable components creates a large configuration space. Moreover, microservices have complex inter-dependencies, making identifying "good" configurations harder. Even the same amount of resources can result in different response latencies based on how the resource is distributed among different microservices. Meanwhile, existing cloud management approaches are designed for monolithic deployments and cannot capture the complex interactions between microservices.

Crypt Cloud: Secure and Expressive Data access by CP-ABE

Secure distributed storage, a new cloud management, is designed to give cloud clients with out-of-control data flexible access to information while maintaining the confidentiality of redistributed data. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is regarded as one of the most promising methods for confirming the administration's security. Nevertheless, the inherent "win or bust" decoding feature of CP-ABE may result in an unavoidable security breach known as the abuse of access certification (such as unscrambling rights). In this paper, we research the two major cases of access capability misuse: One is in favor of cloud client, while the other is on the semi-trusted specialist side. To direct the maltreatment, we propose the vitally mindful master and revocable CP-ABE based circulated capacity system with white-box obviousness and auditing, insinuated as CryptCloud+. We also talk about the security investigation and use of our framework in real-world situations

Emerging Technology in Business and Finance

In the globalized scenario where technologies are developing continuously with time, these novel methods are affecting the business and finance in the significant way. In this chapter we are going to discuss about the major emerging technologies in the field of entrepreneurship, application development, finance, and business.
 The authors are going to start with the introduction about the business, finance, entrepreneurship and application development, and the effect of the emerging technologies on these fields and the way in which technologies are developing from time to time, about adoption of these technologies by industries. The changes in the technologies with special reference to developed and developing country will also be the part of this chapter.
 Moving ahead we are discussing about these technologies in prevailing businesses as well as upcoming business. Some of the technologies we are going to discuss are Embedded Business Intelligence, Amplified Visual Presentation, Augmented Analytics, Cloud Management.
 Beside these technologies, we are going to cover about the growing automation in the finance sector such as Cloud banking, Robotic process automation, Blockchain, Internet of things, etc.
 This chapter will cover all the technologies while getting the complete knowledge about what, why, where, when and how it is changing in the present finance and business scenario. Just like the two opposite faces of the coin, one side these emerging technologies are boon for the business and finances then on the other side there are certain risks involved in these technologies, which can be a great threat to our business as well as in our routine life. So, we also discuss about the potential risks associated with these technologies.
 We will end our chapter by giving our conclusion, precautions, and suggestions on these technologies.

An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications

The use of smartphones and mobile devices has increased significantly, as have Mobile Cloud Applications based on cloud computing. These applications are used in various fields, including Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. While existing cloud-based frameworks provide such services on Virtual Machines, they incur problems such as overhead, lengthy boot time, and high costs. To address these issues, the paper proposes a Dynamic Decision-Based Task Scheduling Approach for Microservice-based Mobile Cloud Computing Applications (MSCMCC) that can run delay-sensitive applications and mobility with less cost than existing approaches. The study focuses on Task Offloading problems on heterogeneous Mobile Cloud servers. It proposes a Task Offloading and Microservices based Computational Offloading (TSMCO) framework to solve Task Scheduling in steps such as Resource Matching, Task Sequencing, and Task Offloading. Experimental results show that the proposed MSCMCC and TSMCO enhance Mobile Server Utilization while minimizing costs and improving boot time, resource utilization, and task arrival time for various applications. Specifically, the proposed system effectively reduces the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.

An overview of threats to data security and integrity in cloud computing

Cloud computing has become an integral part of our lives, and today it is used almost everywhere. In general, cloud computing is a concept of providing IT resources in the form of services. There are two cloud computing models: deployment models, which differ in the type of cloud management and access to the cloud and the level of security, and service models, which differ in the level of service provision, which affects, among other things, the level of responsibility of the service provider and the consumer. Cloud services began to gain popularity in 2009, and the demand for them has grown exponentially every year. They became especially popular during the pandemic in 2019, when people had to stay at home without interrupting their work processes, and now, in post-covid times, they also remain popular due to their convenience, high availability, easy scalability and cost savings. Due to the widespread use of cloud computing services, a high level of security is required. Unfortunately, the popularity of cloud computing has its drawbacks – in addition to the fact that it is more difficult to monitor the security of a remote environment than the security of a local computer, there are many other threats. In today's reality, people use cloud computing technologies in large volumes, for example, at work, for personal purposes, etc., as they have great trust in these technologies. This is the reason why it is necessary to maintain a high level of security and constantly improve it. Cloud computing security threats are usually divided into confidentiality, integrity, and availability threats. To prevent the loss of confidential information, service providers must ensure its integrity. Users want to be sure that their data will not fall into the hands of an intruder or third-party services. Therefore, this article discusses the most common threats to data security and integrity in cloud computing and the existing methods that prevent these vulnerabilities and possible problems at different levels and with the help of different tools.

Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things Applications

The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, and so on is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation models, such as fog, mist, and dew computing. As a result, the cloud and IoT have become part and parcel of smart applications that play significant roles in improving the quality of human life. In addition to the inherent advantages of advanced cloud models, to improve the performance of IoT applications further, it is essential to understand how the resources in the cloud and cloud-influenced platforms are managed to support various phases in the end-to-end IoT deployment. Considering this importance, in this article, we provide a brief description, a systematic review, and possible research directions on every aspect of resource management tasks, such as workload modeling, resource provisioning, workload scheduling, resource allocation, load balancing, energy management, and resource heterogeneity in such advanced platforms, from a cloud perspective. The primary objective of this article is to help early researchers gain insight into the underlying concepts of resource management tasks in the cloud for IoT applications.

Preserving Resource Handiness and Exigency-Based Migration Algorithm (PRH-EM) for Energy Efficient Federated Cloud Management Systems

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment.

Performance Analysis of Enterprise Cloud Computing: A Review

Cloud computing has swiftly established itself as the norm in its field as a result of the advantages described above. In an attempt to reduce the amount of time spent on infrastructure upkeep, an increasing number of businesses are moving their operations to the cloud. As a result, maintaining a cloud environment has proved to be exceedingly difficult. It is necessary to have an efficient cloud monitoring system in order to reduce the workload associated with administration and improve cloud operation. The cloud monitoring service is beneficial since it has the potential to improve performance and make administration easier. The administration of Quality of Service (QoS) parameters for cloud-hosted, virtualized, and physical services and applications is one of the most important responsibilities of cloud monitoring. As a result, cloud management software retains a record of both actions and services, and it also conducts dynamic setups of the cloud in order to increase operational effectiveness. The performance of businesses and businesses as a whole was examined in this article, as was the influence that cloud-ready programs and tools have on that performance, as well as the advantages that may be obtained from adopting such programs and products.

Growable Genetic Algorithm with Heuristic-based Local Search for multi-dimensional resources scheduling of cloud computing

Multi-Dimensional Resources Scheduling Problem (MDRSP, usually a multi-objective optimization problem) has attracted focus in the management of large-scale cloud computing systems as the collaborative operation of various devices in the cloud affects resource utilization and energy consumption. Effective management of the cloud requires a higher performance method to solve MDRSP. Considering the complex coupling between multi-dimensional resources and focusing on virtual machines allocation, we propose GGA-HLSA-RW (GHW, a novel family of genetic algorithms) to optimize the utilization and energy consumption of the cloud. In GGA-HLSA-RW, we add a growth stage to the genetic algorithm and construct a Growable Genetic Algorithm (GGA) using the Heuristic-based Local Search Algorithm (HLSA) with Random multi-Weights (RW) as the growth route. Based on the GHW, we propose GHW-NSGA II and GHW-MOEA/D by applying the sorting strategies and population regeneration mechanism of NSGA II and MOEA/D. To evaluate the performance of GHW, we carry out extensive experiments on the simulation dataset and AzureTraceforPacking2020 for the problems of minimizing the maximum utilization rate of resources for each dimension and minimizing total energy consumption. Experiment results demonstrate the advantages of growth strategy and dimensionality reduction strategy of GHW, as well as validate the applicability and optimality of GHW in realistic cloud computing. The experiments also demonstrate our proposed GHW-NSGA II and GHW-MOEA/D have better convergence rates and optimality than state-of-the-art NSGA II and MOEA/D.

Microservice architecture for a remote management platform for pastured poultry farming using Amazon Web Services and wireless mesh sensor networks

Introduction: A variety of innovative solutions known as Precision Livestock Farming (PLF) technologies have been developed for the management of animal production industries, including Wireless Sensor Networks (WSN) for poultry farming.
 Problem: Current WSN-based systems for poultry farming lack the design of robust but flexible software architectures that ensure the integrity and proper delivery of data.
 Objective: Designing a microservice-based software architecture (MSA) for a multiplatform remote environmental management system based on Wireless Mesh Sensor Networks (WMSN) to be deployed in pastured poultry farming spaces.
 Methodology: A review about MSAs designed for animal farming was conducted, to synthesize key factors considered for the design process of the system data flow, microservice definition and the environmental monitoring system technology selection.
 Results: A cloud MSA with a multi layered scheme using the Amazon Web Services (AWS) platform was developed, validating the persistence of environmental data transmitted from WMSN prototype nodes to be deployed in mobile chicken coops.
 Conclusion: Defining an End-to-End data flow facilitates the organization of tasks by domains, allowing efficient event communication between components and network reliability both at the hardware and software levels.
 Originality: This study presents a novel design for a remote environmental monitoring system based on WMSN for mobile coops used in pastured poultry and a multi layered MSA cloud management platform for this specific type of food production industry.
 Limitations: Software architecture technology selection was based only on services offered, to the date of the study, in the free tier of the Amazon Web Service platform.

Thermal prediction for energy management of clouds using a hybrid model based on CNN and stacking multi-layer bi-directional LSTM

The fast advancement of technology and developers’ utilization of data centers have dramatically increased energy usage in today’s society. Thermal control is a key issue in hyper-scale cloud data centers. Hotspots form when the temperature of the host rises, increasing cooling costs and affecting dependability. Precise estimation of host temperatures is critical for optimal resource management. Thermal changes in the data center make estimating temperature a difficult challenge. Existing temperature estimating algorithms are ineffective due to their processing complexity as well as lack of accuracy. Regarding that data-driven approaches seem promising for temperature prediction, this research offers a unique efficient temperature prediction model. The model uses a combination of convolutional neural networks (CNN) and stacking multi-layer bi-directional long-term short memory (BiLSTM) for thermal prediction. The findings of the experiments reveal that the model successfully anticipates the temperature with the highest R2 value of 97.15% and the lowest error rate of RMSE value of 0.2892, and an RMAE of 0.5003, which decreases the projection error as opposed to the other method.

Low-power multi-cloud deployment of large distributed service applications with response-time constraints

Distributed service applications make heavy use of clouds and multi-clouds, and must (i) meet service quality goals (e.g. response time) while (ii) satisfying cloud resource constraints and (iii) conserving power. Deployment algorithms must (iv) provide a solution meeting these requirements within a short time to be useful in practice. Very few existing deployment methods address the first three requirements, and those that do take too long to find a deployment. The Low-Power Multi-Cloud Application Deployment (LPD) algorithm fills this gap with a low-complexity heuristic combination of generalized graph partitioning between clouds, bin-packing within each cloud and queueing approximations to control the response time. LPD has no known competitor that quickly finds a solution that satisfies response time bounds. A host execution time approximation for contention is fundamental to achieving sufficient solution speed. LPD is intended for use by cloud managers who must simultaneously manage hosts and application deployments and plan capacity to offer services such as Serverless Computing.On 104 test scenarios deploying up to 200 processes with up to 240 replicas (for scaling), LPD always produced a feasible solution within 100 s (within 20 seconds in over three-quarters of cases). Compared to the Mixed Integer Program solution by CPLEX (which took a lot longer and was sometimes not found) LPD solutions gave power consumption equal to MIP in a third of cases and within 6% of MIP in 95% of cases. In 93% of all 104 cases the power consumption is within 20% of an (unachievable) lower bound.LPD is intended as a stand-alone heuristic to meet solution time restrictions, but could easily be adapted for use as a repair mechanism in a Genetic Algorithm.

A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management

Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating costs while also improving quality of service by using intelligent workload and resource management strategies. Such large scale and complex orchestration of software workload and hardware resources remains a difficult problem to solve efficiently. Researchers and practitioners have been trying to address this problem by proposing a variety of cloud management techniques. Mathematical optimization techniques have historically been used to address cloud management issues. But these techniques are difficult to scale to geo-distributed problem sizes and have limited applicability in dynamic heterogeneous system environments, forcing cloud service providers to explore intelligent data-driven and Machine Learning (ML) based alternatives. The characterization, prediction, control, and optimization of complex, heterogeneous, and ever-changing distributed cloud resources and workloads employing ML methodologies have received much attention in recent years. In this article, we review the state-of-the-art ML techniques for the cloud data center management problem. We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues. We also discuss advantages and disadvantages of ML techniques presented in the recent literature and make recommendations for future research directions.

A Quick Review of Data Security, Privacy in Cloud Computing

Data security has been the major issue in information technology. People from all over the world put all kinds of information like public, private and confidential information in the cloud. Also data in the cloud are from various storage devices like servers, PC, mobile devices, smart phones and wireless sensor networks. So data security becomes a serious issue in the cloud environment. Protecting the data from unauthorized access is tedious. There are a lot of issues related to data security and privacy in cloud computing technology. This paper focuses on understanding the security and privacy issues in the cloud environment and comprehending compliance management in cloud computing