Operation Of Cloud Services Research Articles

Building Cloud Skills: Teaching Innovation and Practice of Automated Operation and Maintenance Courses for Cloud Computing Majors in Higher Vocational Colleges

In the rapid development of cloud computing technology, automated operation and maintenance as its core support is crucial to ensure the efficient and stable operation of cloud services. Given this, higher vocational education must pay attention to the training of automated operation and maintenance skills in the curriculum setting of cloud computing majors. Aiming at the problems existing in automated operation and maintenance teaching in higher vocational colleges, this paper puts forward a series of innovative teaching methods to improve students’ mastery of automated operation and maintenance technology. This paper first analyzes the development trend of cloud computing and automated operation and maintenance technology, clarifies the necessity for vocational students to master automated operation and maintenance skills, and then introduces innovative teaching strategies including case-driven, project-oriented, flipped classroom, virtual simulation experiment, and school-enterprise cooperation in detail. These methods aim to improve the interaction of teaching and students’ practical operation ability through the close combination of practice and theory. Finally, this paper proposes a mechanism to evaluate and feedback on these innovative teaching methods to ensure continuous improvement and optimization of teaching activities. Through the implementation of these innovative teaching methods, it can be expected that students will better adapt to the technical requirements of the cloud computing era and lay a solid foundation for their future careers.

RESEARCH METHODS FOR ASSESSING THE QUALITY OF CLOUD SERVICES, INCLUDING RELIABILITY, PERFORMANCE AND SECURITY METRICS

In today's world, cloud services play an important role in the success of any global business. Evaluating the quality of these services is of paramount importance for companies and individuals who use them for their operations and data management. This article is an analysis of various methods for assessing the quality of cloud services. Reliability, performance, and security metrics are discussed, and different assessment models are compared. In the introduction, the authors describe the importance of cloud services and the need to assess their quality. This article provides an overview of cloud service quality assessment methods and shows how they can be applied to different aspects of these services. In the main part of the article, the author describes different methods of assessing the quality of cloud services and compares different assessment models. The author also provides examples of how these methods are used. The methods for assessing the quality of cloud services are discussed, covering reliability, performance, and security. While these metrics provide valuable insights into the performance of cloud services, some counterarguments highlight the potential limitations of using standardized metrics for evaluation. The author also recommends choosing a cloud provider and optimizing cloud services. It is emphasized that reliability metrics, such as uptime and disaster recovery time, are important to ensure the smooth operation of cloud services. Performance metrics, such as response time and throughput, indicate how quickly and efficiently cloud services can process data and requests. Security metrics, such as data encryption and access control, are important to protect data from unauthorized access and cyber threats. The author also emphasizes the importance of considering subjective factors such as user experience and customer support when assessing the quality of cloud services. Finally, the author discusses various risk assessment models that can be used to assess the security of cloud services. The author proposes a new approach to assessing the quality of cloud services for educational institutions. This approach is based on using a combination of quantitative and qualitative indicators that consider the reliability, performance, security, and usability of cloud services. In conclusion, the author recommends choosing a cloud provider and optimizing cloud services. Assessing the quality of cloud services is a complex process that requires a comprehensive and systematic approach. You need to consider various factors such as reliability, performance, and security, as well as your organization's individual needs and requirements.

Virtual machine tree task scheduling for load balancing in cloud computing

The increasing number of publications towards cloud computing proves that much research and development has been done, especially for task scheduling. Organizations are eager to get more customized technology to run the most smoothly in the provision of visual cloud services for fruity users. As the circumstances of Covid indicate to technology that everyone should run digitally, the workload on machines increased. For workload solutions, organizations are trying to balance the situation with the successful operation of cloud services to use appropriate services/resources. Nevertheless, the issues are still to be resolved by researchers, so we respect all my friends who are putting a lot of effort into developing new techniques. A proposed paper is showing a new collation with the load balancing factor by implementing quality of service (QoS) and virtual machine tree (VMT). A CloudSim toolkit will then be used to compare them. A tree structure graph is included in the VMT algorithm to schedule tasks with the appropriate distribution on each machine. The QoS algorithm performs the task of scheduling based on the service required by the user with the best quality and satisfies the user.

Configuration Method of AWS Security Architecture That Is Applicable to the Cloud Lifecycle for Sustainable Social Network

Recently, due to the many features and advantages of cloud computing, “cloud service” is being introduced to countless industries around the world at an unbelievably rapid pace. However, with the rapid increase in the introduction of cloud computing services, security vulnerabilities are increasing and the risk of technology leakage from cloud computing services is also expected to increase in social network service. Therefore, this study will propose an AWS-based (Amazon Web Services) security architecture configuration method that can be applied for the entire life cycle (planning, establishment, and operation) of cloud services for better security in AWS Cloud Services, which is the most used cloud service in the world. The proposed AWS security guide consists of five different areas, Security Solution Selection Guide, Personal Information Safeguard Guide, Security Architecture Design Guide, Security Configuration Guide, and Operational Security Checklist, for a safe social network. The AWS Security Architecture has been designed with three reference models: Standard Security Architecture, Basic Security Architecture, and Essential Security Architecture. The AWS Security Guide and AWS Security Architecture proposed in this paper are expected to help many businesses and institutions that are hoping to establish and operate a safe and reliable AWS cloud system in the social network environment.

Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSO

Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed.

Performance assessment based on stochastic differential equation and effort data for edge computing

SummaryMany open‐source software are included in commercial software. Also, several open‐source software are used in the cloud service such as OpenStack and Eucalyptus from standpoint of the unified management, cost reduction and maintainability. In particular, the operation phase of cloud service has a unique feature with uncertainty such as big data and network connectivity, because the operation phase of cloud service changes depending on many external factors. On the other hand, the effective methods of performance assessments for cloud service have only a few presented. Recently, edge computing is the focus of attention because of the problems of connection and processing delay in case of cloud computing. It is known as that cloud computing treats big data. On the other hand, edge computing operates on instant data. We focus on the performance assessments based on the relationship between the cloud and edge services operated by using several open‐source software. Then we propose a two‐dimensional stochastic differential equation model considering the unique features with uncertainty from big data under the operation of cloud and edge services. Also, we analyse actual data to show numerical examples of performance assessments considering the network connectivity as characteristics of cloud and edge services. Moreover, we compare the noise terms of the proposed model for actual data.

Towards an understanding of cloud services under uncertainty: A possibilistic approach

With the development of Web technologies and the increasing use of the Internet, more and more web services are being deployed. This gave birth to what called cloud services, which are widely used for building distributed cloud applications. With cloud-based service delivery, it seems hard for users to find the right service for their needs. However, if a single cloud service cannot meet all user requirements, a cloud service composition is required. With the rise of Web services on the Internet, the quality of response has been considered as an important criterion to choose the most relevant answer. The performance of web services may vary depending on the dynamic Internet environment, which makes the quality of service uncertain. Then, the need to deal with uncertainty in the operation of cloud services. Over the past decade, several approaches to service composition have been proposed to cope with this challenge. In this article, we provide a flexible and efficient cloud services composition framework that can respond to user queries and improve cloud services composition results by exploiting data uncertainty. We then introduce an effective strategy that allows modeling, invocation, and composition of services by dealing with data uncertainty. The experimental evaluation of a real case study demonstrates the effectiveness of our proposed strategy.

Intelligent cryptography approach for secure distributed big data storage in cloud computing

Implementing cloud computing empowers numerous paths for Web-based service offerings to meet diverse needs. However, the data security and privacy has become a critical issue that restricts many cloud applications. One of the major concerns in security and privacy is caused by the fact that cloud operators have chances to reach the sensitive data. This concern dramatically increases users anxiety and reduces the adoptability of cloud computing in many fields, such as the financial industry and governmental agencies. This paper focuses on this issue and proposes an intelligent cryptography approach, by which the cloud service operators cannot directly reach partial data. The proposed approach divides the file and separately stores the data in the distributed cloud servers. An alternative approach is designed to determine whether the data packets need a split in order to shorten the operation time. The proposed scheme is entitled Security-Aware Efficient Distributed Storage (SA-EDS) model, which is mainly supported by our proposed algorithms, including Alternative Data Distribution (AD2) Algorithm, Secure Efficient Data Distributions (SED2) Algorithm and Efficient Data Conflation (EDCon) Algorithm. Our experimental evaluations have assessed both security and efficiency performances and the experimental results depict that our approach can effectively defend main threats from clouds and requires with an acceptable computation time.

Automated system change discovery and management in the cloud

Emerging cloud service platforms are hosting hundreds of thousands of virtual machine instances, each of which evolves differently from the time they are provisioned. As a result, cloud service operators are facing great challenges in continuously managing, monitoring, and maintaining a large number of diversely evolving systems, and discovering potential resilience and vulnerability issues in a timely manner. In this paper, we introduce an automated cloud analytics solution that is based on using machine learning for system change discovery and management. The learning-based approaches we introduce are widely used in multimedia and web content analysis, but application of these to the cloud management context is a novel aspect of our work. We first propose multiple feature extraction methods to generate condensed “fingerprints” from the comprehensive system metadata recorded during the system changes. We then build an adaptive knowledge base using all known fingerprint samples. We evaluate different machine learning algorithms as part of the proposed discovery and identification framework. Experimental results that are gathered from several real-life systems demonstrate that our approach is fast and accurate for system change discovery and management in emerging cloud services.

Elastic Power-Aware Resource Provisioning of Heterogeneous Workloads in Self-Sustainable Datacenters

While major Cloud service operators have taken various initiatives to operate their datacenters with renewable energy partially or completely, it is challenging to effectively utilize the renewable energy since its generation depends on dynamic natural conditions. In this paper, we propose and develop an elastic power-aware resource provisioning approach (ePower) for heterogeneous workloads in self-sustainable datacenters that completely rely on renewable energy. We aim to maximize the system goodput and control the system power consumption with respect to green power supply. ePower takes challenges and advantages of dynamic power supply, heterogeneous workload characteristics and QoS requirements, and automatically optimizes elastic resource allocations to workloads. The core of ePower design is a novel power-aware simulated annealing algorithm with fuzzy performance modeling for the efficient search of an optimal resource allocation. We have implemented ePower in a university cloud testbed hosting Gridmix2 and RUBiS benchmark applications. We utilize real weather data traces to simulate the green power generation and supply in the experiments. Experimental results demonstrate ePower can achieve near-to-optimal system performance while being resilient to dynamic power availability. It outperforms a representative resource provisioning approach for heterogeneous workloads by at least 24% in improving system goodput and 35 percent in reducing QoS violations.

Key dialectics in cloud services

This paper will identify three central dialectics within cloud services. These constitute defining positions regarding the nature of cloud services in terms of privacy, ethical responsibility, technical architecture and economics. These constitute the main frameworks within which ethical discussions of cloud services occur. The first dialectic concerns the question of whether it is it essential that personal privacy be reduced in order to deliver personalised cloud services. I shall evaluate the main arguments in favour of the view that it is. To contrast this, I shall review Langheinrich's Principles of Privacy-Aware Ubiquitous Systems [24]. This offers a design strategy which maintains functionality while embedding privacy protection into the architecture and operation of cloud services. The second dialectic is concerned with the degree to which people who design or operate cloud services are ethically responsible for the consequences of the actions of those systems, sometimes known as the "responsibility gap." I shall briefly review two papers which argue that no one is ethically responsible for such software, then contrast them with two papers which make strong arguments for responsibility. I shall show how claims for no responsibility rest on very narrow definitions of responsibility combined with questionable conceptions of technology itself. The current shape of cloud services is dominated by a tension between open and closed systems. I shall show how this is reflected in architecture, standards and organisational models. I will then examine alternatives to the current state of affairs, including recent developments in support of alternative business models at government level, such as the House of Lords call for the Internet to be treated as a public utility (The Select Committee on Digital Skills, 2015).

A survey on data center networking for cloud computing

Data Center Networks (DCNs) are an essential infrastructure that impact the success of cloud computing. A scalable and efficient data center is crucial in both the construction and operation of stable cloud services. In recent years, the growing importance of data center networking has drawn much attention to related issues including connective simplification and service stability. However, existing DCNs lack the necessary agility for multi-tenant demands in the cloud, creating poor responsiveness and limited scalability. In this paper, we present an overview of data center networks for cloud computing and evaluate construction prototypes based on these issues. We provide, specifically, detailed descriptions of several important aspects: the physical architecture, virtualized infrastructure, and DCN routing. Each section of this work discusses and evaluates resolution approaches, and presents the use cases for cloud computing service. In our attempt to build insight relevant to future research, we also present some open research issues. Based on experience gained in both research and industrial trials, the future of data center networking must include careful consideration of the interactions between the important aspects mentioned above.

An approach to unified cloud service access, manipulation and dynamic orchestration via semantic cloud service operation specification framework

Cloud computing offers various computational resources via convenient on-demand service provision. Currently, heterogeneous services and cloud resources are usually utilized and managed through diverse service portals. This significantly limits the effectiveness and efficiency for tasks implementation. Fundamentally, it is due to the lack of adequate specifications for service concepts, operations and interfaces from diverse cloud service models and types. This paper proposes a service management operation semantic description framework for comprehensive cloud service operation specification. Relying on ontological modelling techniques, cloud service operations are specified via entity classification, attribute assertion, relationship assertion and annotation assertion. Further, the proposed framework benefits from operation reasoning application. It enables intelligent assistance for multiple operation preparation and remote execution tasks. Based on the approach, a cloud service operation ontology and a unified service access and manipulation system prototype are implemented. Extensive experiments are conducted over different cloud service providers and for distinct service models. Obtained results demonstrate that the approach outperforms existing practices by facilitating reliable and effective service access, manipulation and interaction tasks.

Cost-aware Workload Dispatching and Server Provisioning for Distributed Cloud Data Centers

As the demand on online services and cloud computing has kept increasing in recent years, the power usage and cost associated with cloud data centers’ operation have been uprising significantly. Most existing research focuses on reducing power consumption of data centers. However, the ultimate goal of cloud service operators is to reduce the total operating cost of data centers while guaranteeing the quality of service such as service delay to the end users. This paper exploits both the workload dispatching and the service provisioning to address the total electricity cost minimization problem. This problem is formulated as a hierarchical capacitated median model based on mixed integer linear programming (MILP) technique. Extensive evaluations based on real-life electricity price data for multiple data centers show the efficiency and efficacy of our approach.