Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

Auto-Scaling Framework for Enhancing the Quality of Service in the Mobile Cloud Environments

On-demand availability and resource elasticity features of Cloud computing have attracted the focus of various research domains.Mobile cloud computing is one of these domains where complex computation tasks are offloaded to the cloud resources to augment mobile devices’cognitive capacity.However,the flexible provisioning of cloud resources is hindered by uncertain offloading workloads and significant setup time of cloud virtual machines(VMs).Furthermore,any delays at the cloud end would further aggravate the miseries of real-time tasks.To resolve these issues,this paper proposes an auto-scaling framework(ACF)that strives to maintain the quality of service(QoS)for the end users as per the service level agreement(SLA)negotiated assurance level for service availability.In addition,it also provides an innovative solution for dealing with the VM startup overheads without truncating the running tasks.Unlike the waiting cost and service cost tradeoff-based systems or threshold-rule-based systems,it does not require strict tuning in the waiting costs or in the threshold rules for enhancing the QoS.We explored the design space of the ACF system with the CloudSim simulator.The extensive sets of experiments demonstrate the effectiveness of the ACF system in terms of good reduction in energy dissipation at the mobile devices and improvement in the QoS.At the same time,the proposed ACF system also reduces the monetary costs of the service providers.

Light-Weighted Decision Support Framework for Selecting Cloud Service Providers

Multi-criteria decision making(MCDM)is a technique used to achieve better outcomes for some complex business-related problems,whereby the selection of the best alternative can be made in as many cases as possible.This paper proposes a model,the multi-criteria decision support method,that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling.The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider.Each consumer seeks a service provider based on various preferences,such as price,service quality,and time to complete the tasks.In the literature,the problem is viewed from different perspectives,such as investigating how to enhance task scheduling and the resource allocation process,improve consumers’trust,and deal with network problems.This paper offers a novel model that considers the preferences of both service providers and consumers to find the best available service provider for each consumer.First,the model adopts the best-worst method(BWM)to gather and prioritize tasks based on consumers’and service providers’preferences.Then,the model calculates and matches similarities between the sets of tasks from the consumer’s side with the sets of tasks from the provider’s side to select the best service provider for each consumer using the two proposed algorithms.The complexity of the two algorithms is found to be O(n3).

Hybrid Chameleon and Honey Badger Optimization Algorithm for QoS-Based Cloud Service Composition Problem

Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service(SaaS).Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources.In this context,the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests,as the services stored over the cloud are too complex and scalable.To achieve better service composition,the parameters of Quality of Service(QoS)related to each service considered to be the best resource need to be selected and optimized for attaining potential services over the cloud.Thus,the cloud service composition needs to concentrate on the selection and integration of services over the cloud to satisfy the client’s requests.In this paper,a Hybrid Chameleon and Honey Badger Optimization Algorithm(HCHBOA)-based cloud service composition scheme is presented for achieving efficient services with satisfying the requirements ofQoS over the cloud.This proposed HCHBOA integrated the merits of the Chameleon Search Algorithm(CSA)and Honey Badger Optimization Algorithm(HBOA)for balancing the tradeoff between the rate of exploration and exploitation.It specifically used HBOA for tuning the parameters of CSA automatically so that CSA could adapt its performance depending on its incorporated tuning factors.The experimental results of the proposed HCHBOA with experimental datasets exhibited its predominance by improving the response time by 21.38%,availability by 20.93%and reliability by 19.31%with a minimized execution time of 23.18%,compared to the baseline cloud service composition schemes used for investigation.

QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is needed.Opti-mizing the plethora of cloud services has thus become a top priority.Cloud ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some users.To resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms.Untrusted data are assessed by an information correction method.The weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service quality.The manufacturing cloud service optimization algorithm thus provides a quantitative reference for service selection.In experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

Adaptive Butterfly Optimization Algorithm(ABOA)Based Feature Selection and Deep Neural Network(DNN)for Detection of Distributed Denial-of-Service(DDoS)Attacks in Cloud

Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.

Resilience and Stability in Organizations Employing Cloud Computing in the Financial Services Industry

The services and solutions offered by cloud computing continue to grow, just as the number of organizations relying on the service. Cloud computing has become prevalent in the financial services industry, where 83% of financial services institutions identified cloud computing as a primary element of their infrastructure. Although there are specific benefits of cloud computing that organizations rely on such as access to critical services and lower costs, there is the possibility that cloud computing will create costs for organizations. The purpose of the study was to present an intensive review of the benefits and threats by investigating resilience and stability of the organizations in financial services industry that use cloud computing as their primary element of infrastructure. Data collected from the interview was used to answer original research questions, which are: How are organizations in the financial services industry resilient when faced with threats or losses associated with the use of cloud computing in their organization? How do organizations in the financial services industry maintain stability when faced with threats or losses associated with the use of cloud computing in their organization? The study supports the advancement of knowledge by identifying the threats and losses that occur due to the use of cloud computing, and organizations understand common threats and thus implement proper mitigation.

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.

Construction of Rural Grass-roots Water Conservancy Service System in the Context of Institutional Changes

At present,it is urgent to solve problems of reforming and improving grass-roots water conservancy management mode in rural areas and improving rural water conservancy public service ability. Through analyzing institutional changes of grass-roots water conservancy management in rural areas,this paper discussed current situations and outstanding problems in grass-roots water conservancy services in rural areas of Kunming City. On the basis of current situations and problems,it came up with policy recommendations from reform of management institutions,reform of personnel system,construction of human resources,development of rural water use cooperation organizations,and improvement of fund input mechanism,to build rural grass-roots water conservancy service system.

Trustworthy Assurance of Service Interoperation in Cloud Environment

Cloud computing can be realized by service interoperation and its essence is to provide cloud services through network. The development of effective methods to assure the trustworthiness of service interoperation in cloud environment is a very important problem. The essence of cloud security is trust and trust management. Combining quality of service （QoS） with trust model, this paper constructs a QoS-aware and quantitative trust-model that consists of initial trust value, direct trust value, and recommendatory trust value of service, making the provision, discovery, and aggregation of cloud services trustworthy. Hence, it can assure trustworthiness of service interoperation between users and services or among services in cloud environment. At the same time, based on this model, service discovery method based on QoS-aware and quantitative trust-model （TQoS-WSD） is proposed, which makes a solid trust relationship among service requestor, service provider and service recommender, and users can find trustworthy service whose total evaluation value is higher. Corapared to QoS-based service discovery （QoS-WSD） method, it is proved by the experiment for TQoS-WSD method that more accurate result of service discovery will be achieved by service requestor, while reasonable time cost is increased. Meanwhile, TQoS-WSD method strongly resists the effect of service discovery by untrustworthy QoS values and improves service invocation success-rate and thus assures trustworthiness of services interoperation.

Toward Cloud Computing QoS Architecture:Analysis of Cloud Systems and Cloud Services

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service QoS by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue. © 2014 Chinese Association of Automation.

Adaptive Service Provisioning for Mobile Edge Cloud

A mobile edge cloud provides a platform to accommodate the offloaded traffic workload generated by mobile devices.It can significantly reduce the access delay for mobile application users.However,the high user mobility brings significant challenges to the service provisioning for mobile users,especially to delay-sensitive mobile applications.With the objective to maximize a profit,which positively associates with the overall admitted traffic served by the local edge cloud,and negatively associates with the access delay as well as virtual machine migration delay,we study a fundamental problem in this paper:how to update the service provisioning solution for a given group of mobile users.Such a profit-maximization problem is formulated as a nonlinear integer linear programming and linearized by absolute value manipulation techniques.Then,we propose a framework of heuristic algorithms to solve this Nondeterministic Polynomial(NP)-hard problem.The numerical simulation results demonstrate the efficiency of the devised algorithms.Some useful summaries are concluded via the analysis of evaluation results.

Research on cloud computing and services framework of marine environmental information management

Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.

Service Security Architecture and Access Control Model for Cloud Computing

Security is a key problem for the development of Cloud Computing. A common service security architecture is a basic abstract to support security research work. The authorization ability in the service security faces more complex and variable users and environment. Based on the multidimensional views, the service security architecture is described on three dimensions of service security requirement integrating security attributes and service layers. An attribute-based dynamic access control model is presented to detail the relationships among subjects, objects, roles, attributes, context and extra factors further. The model uses dynamic control policies to support the multiple roles and flexible authority. At last, access control and policies execution mechanism were studied as the implementation suggestion.

Cloud services with big data provide a solution for monitoring and tracking sustainable development goals

To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.

Cloud service performance evaluation： status, challenges, and opportunities - a survey from the system modeling perspective

With rapid advancement of Cloud computing and networking technologies, a wide spectrum of Cloud services have been developed by various providers and utilized by numerous organizations as indispensable ingredients of their information systems. Cloud service performance has a significant impact on performance of the future information infrastructure. Thorough evaluation on Cloud service performance is crucial and beneficial to both service providers and consumers; thus forming an active research area. Some key technologies for Cloud computing, such as virtualization and the Service-Oriented Architecture （SOA）, bring in special challenges to service performance evaluation. A tremendous amount of effort has been put by the research community to address these challenges and exciting progress has been made. Among the work on Cloud performance analysis, evaluation approaches developed with a system modeling perspective play an important role. However, related works have been reported in different sections of the literature; thus lacking a big picture that shows the latest status of this area. The objectives of this article is to present a survey that reflects the state of the art of Cloud service performance evaluation from the system modeling perspective. This articles also examines open issues and challenges to the surveyed evaluation approaches and identifies possible opportunities for future research in this important field.

A Unified Framework of the Cloud Computing Service Model

After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.

Context-Aware Service IVlodes in the Cloud Computing Environment

This paper discussed the differences of context-aware service between the cloud computing environment and the traditional service system.Given the above differences,the paper subsequently analyzed the changes of context-aware service during preparation,organization and delivery,as well as the resulting changes in service acceptance of consumers.Because of these changes,the context-aware service modes in the cloud computing environment change are intelligent,immersive,highly interactive,and real-time.According to active and responded service,and authorization and non-authorized service,the paper drew a case diagram of context-aware service in Unified Modeling Language(UML) and established four categories of context-aware service modes.

Research on Trust Model in Container-Based Cloud Service

Container virtual technology aims to provide program independence and resource sharing.The container enables flexible cloud service.Compared with traditional virtualization,traditional virtual machines have difficulty in resource and expense requirements.The container technology has the advantages of smaller size,faster migration,lower resource overhead,and higher utilization.Within container-based cloud environment,services can adopt multi-target nodes.This paper reports research results to improve the traditional trust model with consideration of cooperation effects.Cooperation trust means that in a container-based cloud environment,services can be divided into multiple containers for different container nodes.When multiple target nodes work for one service at the same time,these nodes are in a cooperation state.When multi-target nodes cooperate to complete the service,the target nodes evaluate each other.The calculation of cooperation trust evaluation is used to update the degree of comprehensive trust.Experimental simulation results show that the cooperation trust evaluation can help solving the trust problem in the container-based cloud environment and can improve the success rate of following cooperation.

Access Control Policy Analysis and Access Denial Method for Cloud Services

Personal cloud computing is an emerging trend in the computer industry. For a sustainable service, cloud computing services must control user access. The essential business characteristics of cloud computing are payment status and service level agreement. This work proposes a novel access control method for personal cloud service business. The proposed method sets metadata, policy analysis rules, and access denying rules. Metadata define the structure of access control policies and user requirements for cloud services. The policy analysis rules are used to compare conflicts and redundancies between access control policies. The access denying rules apply policies for inhibiting inappropriate access. The ontology is a theoretical foundation of this method. In this work, ontologies for payment status, access permission, service level, and the cloud provide semantic information needed to execute rules. A scenario of personal data backup cloud service is also provided in this work. This work potentially provides cloud service providers with a convenient method of controlling user access according to changeable business and marketing strategies.