Cloud Service Environment Research Articles

Research on Parallel Task Scheduling Algorithm of SaaS Platform Based on Dynamic Adaptive Particle Swarm Optimization in Cloud Service Environment

To efficiently realize the parallel task scheduling of SaaS platform in large-scale cloud service environment, this paper studies the parallel task scheduling algorithm of SaaS platform based on dynamic adaptive particle swarm optimization in cloud service environment. Users access the cloud through the user access interface module, and issue task scheduling instructions or send task scheduling requests. After the service management module provides diversified application service support according to the scheduling requirements, the core service module determines the SaaS platform parallel scheduling objective function, and uses dynamic adaptive particle swarm optimization to solve the objective function to obtain the SaaS platform parallel task scheduling results. The test results show that the algorithm has better multi-objective solving ability and can obtain higher quality objective solutions, and the test results of the total execution time of parallel scheduling tasks and the total transmission time of task data on SaaS platform are all within 30 s. The results of virtual machine resource load balancing degree are all below 15%; the utilization rate of virtual machine resources is above 92.2%.

An analysis of performance, pricing, and coordination in a supply chain with cloud services: The impact of data security

As an important supporting technology for Industry 4.0 and smart manufacturing, cloud computing technology facilitated the sharing of digital services and reduced energy consumption. However, concerns about data security may cause firms to hesitate to adopt cloud services. How to coordinate their supply and demand and estimate the economic and environmental impacts are not well understood. To address this research gap, this study investigates a supply chain consisting of a cloud-service provider and a manufacturer and a collaboration mechanism that may coordinate both parties’ interests. We employ game theory to design stylized models of two supply chain structures (centralised and decentralised) and derive each firm’s optimal product pricing, profits, and the entire supply chain under different scenarios. The results show that considering cloud-service data security, a cloud-service supply chain cannot achieve coordination in the absence of intervention, while once coordinated it could be more environmentally friendly than the case without coordination. Furthermore, we propose a revenue-sharing contract to incentivise the cloud-service provider to cooperate with the manufacturer and identify the conditions for data security from which both can benefit. Interestingly, enhancing data security does not always help the entire supply chain, and higher data security may worsen it. Finally, numerical examples were derived to demonstrate our conclusions. Our study highlights supply chain coordination in a cloud-service environment and provides significant insights for participants who value data security and carbon–neutral innovation solutions.

ПРОГРАМА РОЗРІШУВАЧІВ SMT ДЛЯ ФОРМАЛЬНОЇ ПЕРЕВІРКИ СЕРВІСІВ НА ОСНОВІ ПОЛІТИКИ В ХМАРНОМУ СЕРЕДОВИЩІ

Formal verification of policy-based services in cloud environments using SMT (Satisfiability Modulo Theories) solvers involves encoding the policies and service behaviors as logical formulas and checking whether the formulas are satisfactory or unsatisfiable. SMT solvers can be used to verify that the policies are enforced correctly and that the service behaviors are consistent with the policies.

Hierarchal attribute based cryptographic model to handle security services in cloud environment: a new model

The sharing of information in the cloud is a unique element of the environment, but there is a risk that the information may land with the wrong people. To counterattack this problem, security-associated methodologies were used to secure the information that was readily available to clients. Despite the lack of benefits, this provides productive/adaptability and dependability in access control strategies between clients in the sharing of information. The novel hierarchal attribute-based cryptographic security model (NHACSM) is being proposed to provide adaptability, versatility, and access control in sharing information in the appropriate climate. This model allows clients to share information in a hierarchal way, allowing for a productive assessment of access control strategy and improved security. The NHACSM method is used to reduce the total time values for different user instances compared to conventional approaches, for example, attribute-set-based encryption (ASBE), key-policy attribute-based encryption (KP-ABE), and ciphertext-policy attribute-based encryption (CP-ABE). With respect to 10 instances existing methods achieve 2.7, 2.5, and 2.3 respectively, and also compared to 20, 30, 40, and 50 instances, our proposed method is low. The encryption and decryption time evaluation values and performance evaluation of different approaches, ASBE, CP-ABE, were taken into account when increasing the user instance.

A Q-learning based auto-scaling approach for provisioning big data analysis services in cloud environments

Currently, analyzing big data to capture hidden values in various fields is one of the most popular research directions. Analytics-as-a-Service (AaaS) providers typically construct a common platform by renting virtual machine (VM) resources from Infrastructure-as-a-Service (IaaS) providers instead of owning their own physical resources, to provision big data analysis services to end users. However, due to the fact that service demands from AaaS users tend to fluctuate over time in reality, how to dynamically adjust the type and quantity of VMs rented from IaaS providers has become an urgent problem to be solved. In this article, we assume that IaaS providers can offer VM resources in two charging modes, and service demand workloads arrive stochastically. We formulate the problem as a Markov decision process and propose a Q-Learning based auto-scaling method, which explores the trade-off between AaaS providers’ cost and VM resource utilization. Eventually, we evaluate our method on both real traces and simulated traces and compare it with some existing methods. Experimental results demonstrate that our method can not only achieve the goal of reducing cost, but also ensure the high utilization of VM resources in the long run.

Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows

Distributed computing services in cloud environments are easily accessible to end users. These services are delivered to end users via a subscription-based model. The “infrastructure as a service” (IaaS) cloud model is one of the best cloud environment models for running data- and computing-intensive applications. Real-world scientific applications are the best examples of data and computing intensiveness. For their implementation, scientific workflow applications need high-performance computational resources and a large volume of storage. The workflow tasks are linked based on computational and data interdependence. Considering the high volume and variety of scientific workflows (SWs), the resources of the IaaS cloud model require managing energy efficiently and without failure or loss. Therefore, in order to address the issues of power consumption and task failure for real-world SWs, this research work proposes a replication-based dynamic energy-aware resource provisioning (R-DEAR) strategy for SWs in an IaaS cloud environment. The proposed strategy, R-DEAR, is a resource- and service-provisioning strategy that implements a replication-based fault-tolerant and load-balancing mechanism. The proposed R-DEAR strategy schedules the tasks of a scientific workflow with a replication-based fault-tolerant mechanism. The proposed R-DEAR strategy also manages the power consumption of IaaS cloud resources dynamically through a load-sharing process. Simulation results show that the proposed R-DEAR strategy reduces energy consumption, execution cost, and execution time by 9%, 15%, and 18%, respectively, as compared with the existing state-of-the-art strategy.

An Energy-Aware Agent-Based Resource Allocation Using Targeted Load Balancer for Improving Quality of Service in Cloud Environment

In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity.

Evaluation and Selection of Cloud Service: A neutrosophic model

The variety of cloud-based services that are now accessible is expanding at a fast pace, and as a result, it has become much more difficult for regular customers to choose the appropriate cloud services. When selecting an appropriate service to use in a setting where there is a high degree of unpredictability, it is in the user's best interest to be able to deal with ambiguous information. This is because the Cloud service environment contains a great number of unknowns, which may prevent the user from making wise choices. In this paper, the authors propose a framework that they call the Optimal Service Choice and Priority of Cloud Computing (CC) Service. This methodology gives cloud customers the ability to evaluate the various service options available to them relying on QoS (Quality of Requirements) standards. The model employs a mixed approach to decision making that is based on many factors. The PROMETHEE is used for the purpose of ranking and prioritizing the QoS criteria, and to get the final rank of cloud services. The suggested methodology is a Multi-criteria decision making approach due to this problem contain many conflicting criteria. The PROMETHEE is integrated with neutrosophic environment to overcome uncertainty information. The application of methodology is provided.

QoS prediction for web services in cloud environments based on swarm intelligence search

In the real-world cloud computing scenarios, a user usually invokes few Web services and can provide only a small proportion of QoS (Quality of Service) records. Therefore, for a whole range of services, many records for this user are missing (i.e. incomplete QoS information). These missing QoS values make it difficult to conduct accurate service recommendations. To overcome this difficulty, it is necessary to predict the missing QoS values for the user. Most existing algorithms tackle the QoS prediction problem through aggregating the QoS values of the local similar neighbors of the user, which easily leads to local optimization. In this paper, we model it as a global search optimization problem in the distribution space of QoS values and propose a novel algorithm PSO-USRec. In the algorithm, particle swarm optimization (PSO) is customized and improved by diversifying the initial solutions and smoothing the outlier particles. To validate the effectiveness of the PSO-USRec algorithm, comparison experiments are conducted on a well-known public QoS dataset. The experimental results show that the PSO-USRec algorithm significantly outperforms the state-of-the-art collaborative filtering approaches. It reduces MAE and RMSE by at least 5.42% and 1%, and at most 14.29% and 2.25%, respectively.

Authentication methods for internet of vehicles based on trusted connection architecture

This paper proposes an authentication protocol based on a trusted connection architecture to manage the security and reliability of the cloud service environment during the communication process, improve the trust of the cloud service platform toward vehicles, and ensure that vehicle terminals have reliable access to cloud services. Compared with prior Internet of Vehicle (IoV) authentication schemes, our scheme is the first to include platform identification in the authentication process. Based on the characteristics of the trusted connection architecture, the components that constitute the platform can be assessed for security by verifying the vehicle platform identity and platform integrity metrics, thereby eliminating internal threats. In addition, the protocol proposes an authentication scheme for the IoV environment, in which the trusted authority only needs to generate the user's partial key based on the identity, thereby avoiding the key escrow problem common to identity-based cryptosystems. Finally, the scheme is proven to be highly secure using various approaches, such as Syverson-Van Oorschot (SVO) logical analysis, simulated authentication via automated validation of internet security protocols and applications (AVISPA), and informal security analysis. In the identity authentication step, our method has low computation and communication overhead when compared with other schemes according to the performance analysis results.

A resource scheduling method for reliable and trusted distributed composite services in cloud environment based on deep reinforcement learning.

With the vigorous development of Internet technology, applications are increasingly migrating to the cloud. Cloud, a distributed network environment, has been widely extended to many fields such as digital finance, supply chain management, and biomedicine. In order to meet the needs of the rapid development of the modern biomedical industry, the biological cloud platform is an inevitable choice for the integration and analysis of medical information. It improves the work efficiency of the biological information system and also realizes reliable and credible intelligent processing of biological resources. Cloud services in bioinformatics are mainly for the processing of biological data, such as the analysis and processing of genes, the testing and detection of human tissues and organs, and the storage and transportation of vaccines. Biomedical companies form a data chain on the cloud, and they provide services and transfer data to each other to create composite services. Therefore, our motivation is to improve process efficiency of biological cloud services. Users’ business requirements have become complicated and diversified, which puts forward higher requirements for service scheduling strategies in cloud computing platforms. In addition, deep reinforcement learning shows strong perception and continuous decision-making capabilities in automatic control problems, which provides a new idea and method for solving the service scheduling and resource allocation problems in the cloud computing field. Therefore, this paper designs a composite service scheduling model under the containers instance mode which hybrids reservation and on-demand. The containers in the cluster are divided into two instance modes: reservation and on-demand. A composite service is described as a three-level structure: a composite service consists of multiple services, and a service consists of multiple service instances, where the service instance is the minimum scheduling unit. In addition, an improved Deep Q-Network (DQN) algorithm is proposed and applied to the scheduling algorithm of composite services. The experimental results show that applying our improved DQN algorithm to the composite services scheduling problem in the container cloud environment can effectively reduce the completion time of the composite services. Meanwhile, the method improves Quality of Service (QoS) and resource utilization in the container cloud environment.

Retraction Note to: Efficient optimal resource allocation for profit maximization in software defined network approach to improve quality of service in cloud environments

Retraction Note to: Efficient optimal resource allocation for profit maximization in software defined network approach to improve quality of service in cloud environments

Credibility Analysis of Accounting Cloud Service Based on Complex Network

Compared with the previous accounting information system (hereinafter referred to as AIS), the dynamic and changing environment of accounting cloud service, cloud storage away from enterprise entities, service modules selected for purchase, and seamless dynamic configuration. With the emergence of new situations such as the reconstruction of accounting information processing process, the emergence of new features increases the information risk of enterprises. Therefore, taking reasonable and effective measures can enable enterprises to intuitively understand whether AIS is credible in the accounting cloud service environment. Referring to the existing research system in the field of reliability evaluation, this paper analyzes the current situation of accounting cloud service and its characteristics compared with the previous AIS and divides it into four parts: normative inspection, index calculation, and reliability calculation to illustrate the method system for measuring the reliability of accounting cloud service. This paper analyzes the reliability requirements and reliability attributes of accounting cloud services and constructs a reliability evaluation grade model combined with fuzzy comprehensive evaluation to guide the selection of users and the quality management of cloud accounting suppliers. Considering the complexity and dynamics of AIS reliability evaluation in accounting cloud service environment, the reliability of AIS is also affected by the complex call relationship between modules; combined with the complex network theory, a reliability analysis and evaluation method of accounting cloud service based on complex network are proposed.

Service-Oriented Reliability Modeling and Autonomous Optimization of Reliability for Public Cloud Computing Systems

The ubiquitous adoption of public clouds has resulted in the bloom of big data, Internet of Things, and artificial intelligence (AI) and a great capacity for applications. However, the efficient operation of these applications is challenging due to the infrastructure heterogeneity, computing hierarchy, scale distribution, and stochastic user behaviors. Among these challenges, reliability has received intense interest in the cloud community. Most existing research on the reliability of clouds is on fault discovery or fault tolerance. However, a public cloud has a wider range of failures, which makes it difficult to analyze the association between reliability and other indicators. More accurate reliability modeling methods and advanced optimization approaches are needed to ensure the reliable operation of a public cloud. To address these challenges, this article models the reliability of cloud computing from the service perspective and reasonably divides cloud services into the request processing phase and request execution phase when facing multiple users and service types. Moreover, this article combines a variety of AI-based methods, such as autonomous scheduling mechanisms, anomaly detection, and autonomous learning improvement, which can effectively adapt to the dynamic and complex cloud service environment and ensure the service reliability of a public cloud. Numerical results clearly indicate that the proposed service reliability model and autonomous optimizations are efficient for recovering the reliability of cloud computing systems in terms of multiple AI-based failure prognostics and intelligent learning ability.

Enterprise Collaborative Integrated Management System Based on IoT Cloud Technology

With the advent of the white-hot competition environment at home and abroad, enterprises must continuously improve their management level to reduce operating costs, so as to achieve the purpose of improving operating efficiency, highlighting competitive advantages, and improving core competitiveness. The purpose of this research is to realize the sharing and collaboration mechanism of government resources, enterprise resources, and other resources in the digital ecological environment, so as to realize the transformation of enterprise productivity and production mode to a higher-end mode, and enhance the innovation ability of enterprises. On the basis of cloud computing theory and service computing theory, this research has carried out service model innovation, analysis, and design and realizes a service-oriented open collaborative sharing, multiorganization, multitenant, and dynamically scalable intelligent enterprise collaborative innovation cloud service platform. By selecting 20 valid scoring data, 80% of the scores are distributed in 3-5 points, indicating that the collaborative innovation cloud service platform for smart enterprises proposed in this paper realizes collaborative sharing among multiple organizations and multiple tenants. It highlights the matchmaking between enterprises and high-quality innovation resources, alleviates the uneven distribution of innovation resources, bridges the gap between various innovation entities, and provides a scalable and sustainable evolution of collaborative innovation cloud service environment.

Sharing large data collections using data services in cloud environment

Purpose Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).Design/methodology/approach The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.Findings Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.Originality/value The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.

A resource allocation and scheduling model for hierarchical distributed services in cloud environment using game theory

SummaryIn this paper, a game theoretic‐based method has been proposed to solve the problem of allocating computing resources in a hierarchical cloud structure. The proposed method has two phases: “topology construction” and “game‐theoretic‐based resource allocation.” In the first phase, a hierarchical structure will be established to determine a reliable communication infrastructure between cloud computing resources. In the second phase, a game theoretic model is used to allocate cloud resources and schedule executing of tasks in the constructed hierarchical structure of cloud. In the proposed method, end user's tasks are collected through leaf resources in hierarchical tree structure. Then each computing resource will consider managing collected tasks using a game‐based model. The efficiency of the proposed method is evaluated in a simulated environment and the results are compared with some of the best previous studies. The results show that our proposed method reduces energy consumption and computational cost. Also, using the proposed game‐theoretic method, more computational tasks can be processed using cloud resources, so this method can be used more efficiently for real‐world scenarios.

Building a Reputation Attack Detector for Effective Trust Evaluation in a Cloud Services Environment

Cloud computing is a widely used technology that has changed the way people and organizations store and access information. This technology is versatile, and extensive amounts of data can be stored in the cloud. Businesses can access various services over the cloud without having to install applications. However, cloud computing services are provided over a public domain, which means that both trusted and non-trusted users can access the services. Although there are a number of advantages to cloud computing services, especially for business owners, various challenges are posed in terms of the privacy and security of information and online services. A threat that is widely faced in the cloud environment is the on/off attack, in which entities exhibit proper behavior for a given time period to develop a positive reputation and gather trust, after which they exhibit deception. Another threat often faced by trust management services is a collusion attack, which is also known as collusive malicious feedback behavior. This is carried out when a group of people work together to make false recommendations with the intention of damaging the reputation of another party, which is referred to as a slandering attack, or to enhance their own reputation, which is referred to as a self-promoting attack. In this paper, a viable solution is provided with the given trust model for preventing these attacks. This method works by providing effective security to cloud services by identifying malicious and inappropriate behaviors through the application of trust algorithms that can identify on/off attacks and collusion attacks by applying different security criteria. Finally, the results show that the proposed trust model system can provide high security by decreasing security risk and improving the quality of decisions of data owners and cloud operators.

Uncertainty-Aware Online Scheduling for Real-Time Workflows in Cloud Service Environment

Scheduling workflows in cloud service environment has attracted great enthusiasm, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the scheduling environment, such as the uncertain task start/execution/finish time, the uncertain data transfer time among tasks, the sudden arrival of new workflows. Ignoring these uncertain factors often leads to the violation of workflow deadlines and increases service renting costs of executing workflows. This study devotes to improving the performance for cloud service platforms by minimizing uncertainty propagation in scheduling workflow applications that have both uncertain task execution time and data transfer time. To be specific, a novel scheduling architecture is designed to control the count of workflow tasks directly waiting on each service instance (e.g., virtual machine and container). Once a task is completed, its start/execution/finish time are available, which means its uncertainties disappearing, and will not affect the subsequent waiting tasks on the same service instance. Thus, controlling the count of waiting tasks on service instances can prohibit the propagation of uncertainties. Based on this architecture, we develop an unceRtainty-aware Online Scheduling Algorithm (ROSA) to schedule dynamic and multiple workflows with deadlines. The proposed ROSA skillfully integrates both the proactive and reactive strategies. During the execution of the generated baseline schedules, the reactive strategy in ROSA will be dynamically called to produce new proactive baseline schedules for dealing with uncertainties. Then, on the basis of real-world workflow traces, five groups of simulation experiments are carried out to compare ROSA with five typical algorithms. The comparison results reveal that ROSA performs better than the five compared algorithms with respect to costs (up to 56 percent), deviation (up to 70 percent), resource utilization (up to 37 percent), and fairness (up to 37 percent).

A Cryptography and Machine Learning Based Authentication for Secure Data-Sharing in Federated Cloud Services Environment

Secure mutual authentication is an indispensable requirement to share organizational invaluable data among collaborating entities in federated cloud environment. This work presents a novel mutual authentication method which incorporates machine learning based ensemble Voting Classifier for online threat detection and Elliptic Curve Cryptography with Schnorr’s signature scheme based key agreement to ensure secure communication among the participating entities by prior detection and mitigation of security breaches. The performance evaluation by using a benchmark from Canadian Institute for Cybersecurity Datasets and ProVerif security analysis tool verifies its efficiency in terms of security features and communication cost over existing approaches.