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 Services Quality Enhancement through Control &Monitoring of Service Level Agreements at Server and Consumer Side

Several researches and case studies have been revealing a repetitive issue and that is monopolization of controlling, measuring, and metering of the consumer’s billing for the consumed Cloud Services. The Cloud Service Providers (CSPs) have been hiding, or monopolizing the controlling, metering tools and ignoring the calculation and consideration of the service defects like downtime, service outage, poor performance and client service migration delays during overloads of the servers. The main issues behind these discrepancies are the Service Level Agreement (SLA), its violation, and the monopolized Cloud Governance. Till now there is a lack of parallel monitoring and metering system of the consumed cloud services at the customer level under the provisions of SLAs. The Cloud Governance Tier has not given any flexibility or utility to monitor such consumed cloud services at the client tier in parallel to the CSP tier. This research is an effort to develop a SLA controlling/monitoring framework to solve the identified problems other than the CSP level. Further the E_Draw max was used for designing the framework. The framework consists of the components like;SLA_CSP, SLA_CSC, SLA_M&C and Cloud Services and Delivery Models. After designing the framework, a prototype was developed using CloudSim on java net beans, and MySQL database. This prototype was implemented using four scenarios to evaluate its performance and the functionalities. 1) Checking the SLA under CSP whether the resources to the consumer in the same manner with promised. 2) To check if the request from cloud consumer is less than what is expected in SLA. 3) To check by increasing the request of cloud consumer more than what is expected in SLA and 4) Using the SLA_M&C mechanism. After execution it was observed that, in the first and the second cases there is no violation, but there is violation in the third and fourth cases. This implied that there are chances of the discrepancies in resources provided to make the selves beneficiary beyond SLA under CSP and CSC was discriminated while receiving services from CSP.

An Adaptive Video-on-Demand Framework for Multimedia Cross-Platform Cloud Services

Although Video-On-Demand (VOD) has been in existence for years, its cross-platform applicability in cloud service environments is still in increasing need. In this paper, an Adaptive Video-On-Demand (AVOD) framework that is suitable for private cloud environments is proposed. Private cloud has the key advantage of satisfying the real need of both consumers and providers. Hence, demands such as reasonable benefits for provider and high quality for consumers are essential design considerations in this framework. The difficulty is that these two factors are always high in one end and low in the other, and hard to find a delicate balance. Cloud service could be an opportunity for the multimedia providers to obtain higher benefits and cost less for the consumers but with an even better quality in service. An adaptive framework for such a cloud service environment is proposed to resolve this problem. Some interesting phenomena are observed from the experimental results including CPU utilization, data reading and writing speed, memory usage, port configuration execution time, and bandwidth.

RESTlogic: Detecting Logic Vulnerabilities in Cloud REST APIs

logical testing model and resource lifecycle information,generate test cases and complete parameters,and alleviate inconsistency issues through parameter inference.Once again,we propose a method of analyzing test results using joint state codes and call stack information,which compensates for the shortcomings of traditional analysis methods.We will apply our method to testing REST services,including OpenStack,an open source cloud operating platform for experimental evaluation.We have found a series of inconsistencies,known vulnerabilities,and new unknown logical defects.

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.

Prioritization technique for learning cloud services

Purpose–The purpose of this paper is to propose a combined technique of cumulative voting and numerical assignment to prioritize the services of the learning cloud service.Design/methodology/approach–The approach starts with requirement elicitation,then analyses of the requirements in terms of prioritization and finally classifies the priority of services into groups.Findings–As a result of the case study the requirements of the College of Art Media and Technology students has been classified into three service groups.Originality/value–This combined prioritized techniques can involve learners in the decision making process about learning cloud services utilization in the organizations.

A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

Intelligent Space(IS)is widely regarded as a promising paradigm for improving quality of life through using service task processing.As the field matures,various state-of-the-art IS architectures have been proposed.Most of the IS architectures designed for service robots face the problems of fixedfunction modules and low scalability when performing service tasks.To this end,we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture(SOA).Specifically,we first use the distributed deployment of functional modules to solve the problem of high computing resource occupancy.Then,the Socket communication interface layer is designed to improve the calling efficiency of the function module.Next,the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing tasks.Finally,we design and deploy an interactive system based on Browser/Server(B/S)architecture,which aims to display the status of the robot in real-time as well as to expand and call the robot service.This system is integrated into the private cloud framework,which provides a feasible solution for improving the quality of life.Besides,it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right way.The results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service tasks.It is an efficient way to transmit data and reduce the computational burden on the robot.By using our cloud detection module,the robot system can save approximately 25% of the averageCPUusage and reduce the average detection time by 0.1 s compared to the locally deployed system,demonstrating the reliability and practicality of our proposed architecture.

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).

Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment

In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.

A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

In a cloud environment,consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost.On the other hand,Cloud Service Providers(CSPs)seek to maximize their profits by attracting and serving more consumers based on their resource capabilities.The literature has discussed the problem by considering either consumers’needs or CSPs’capabilities.A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient way.The paper proposes a model that adopts a Multi-Criteria Decision Making(MCDM)method,called Analytic Hierarchy Process(AHP),to acquire the information of consumers’preferences and service providers’capabilities to prioritize both tasks and resources.The model also provides a matching technique to assign each task to the best resource of a CSP while preserves the fairness of scheduling more tasks for resources with higher capabilities.Our experimental results prove the feasibility of the proposed model for prioritizing hundreds of tasks/services and CSPs based on a defined set of criteria,and matching each set of tasks/services to the best CSPS.

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.

Hybrid Cloud Architecture for Higher Education System

As technology improves,several modernization efforts are taken in the process of teaching and learning.An effective education system should maintain global connectivity,federate security and deliver self-access to its services.The cloud computing services transform the current education system to an advanced one.There exist several tools and services to make teaching and learning more interesting.In the higher education system,the data flow and basic operations are almost the same.These systems need to access cloud-based applications and services for their operational advancement and flexibility.Architecting a suitable cloud-based education system will leverage all the benefits of the cloud to its stakeholders.At the same time,educational institutions want to keep their sensitive information more secure.For that,they need to maintain their on-premises data center along with the cloud infrastructure.This paper proposes an advanced,flexible and secure hybrid cloud architecture to satisfy the growing demands of an education system.By sharing the proposed cloud infrastructure among several higher educational institutions,there is a possibility to implement a common education system among organizations.Moreover,this research demonstrates how a cloud-based education architecture can utilize the advantages of the cloud resources offered by several providers in a hybrid cloud environment.In addition,a reference architecture using Amazon Web Service(AWS)is proposed to implement a common university education system.

MimicCloudSim:An Environment for Modeling and Simulation of Mimic Cloud Service

In recent years,an increasing number of application services are deployed in the cloud.However,the cloud platform faces unknown security threats brought by its unknown vulnerabilities and backdoors.Many researchers have studied the Cyber Mimic Defense(CMD)technologies of the cloud services.However,there is a shortage of tools that enable researchers to evaluate their newly proposed cloud service CMD mechanisms,such as scheduling and decision mechanisms.To fill this gap,we propose MimicCloudSim as a mimic cloud service simulation system based on the basic functionalities of CloudSim.MimicCloudSim supports the simulation of dynamic heterogeneous redundancy(DHR)structure which is the core architecture of CMD technology,and provides an extensible interface to help researchers implement new scheduling and decision mechanisms.In this paper,we firstly describes the architecture and implementation of MimicCloudSim,and then discusses the simulation process.Finally,we demonstrate the capabilities of MimicCloudSim by using a decision mechanism.In addition,we tested the performance of MimicCloudSim,the conclusion shows that MimicCloudSim is highly scalable.

A rapid, low-cost deep learning system to classify strawberry disease based on cloud service

Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on. Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements. We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. This involves designing an easy-to-use cloudbased strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases. With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes. Using accuracy, precision, recall and F1 to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively. Compared with popular Convolutional Neural Networks(CNN) and five other methods, our network achieves better disease classification effect. Currently, the client(mini program) has been released on the We Chat platform. The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.

Cloud Data Center Selection Using a Modified Differential Evolution

The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector.Cloud data center selection challenges are compounded by ever-increasing users’requests and the number of data centers required to execute these requests.Cloud service broker policy defines cloud data center’s selection,which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution.Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness,and it is well suited for selecting an appropriate cloud data center.This paper presents a modified differential evolution algorithm-based cloud service broker policy for the most appropriate data center selection in the cloud computing environment.The differential evolution algorithm is modified using the proposed new mutation technique ensuring enhanced performance and providing an appropriate selection of data centers.The proposed policy’s superiority in selecting the most suitable data center is evaluated using the CloudAnalyst simulator.The results are compared with the state-of-arts cloud service broker policies.

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.

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet.The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric.In a hyperconverged cloud ecosystem environment,building high-reliability cloud applications is a challenging job.The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings.The emergence of cloud computing is significantly reshaping the digital ecosystem,and the numerous services offered by cloud service providers are playing a vital role in this transformation.Hyperconverged software-based unified utilities combine storage virtualization,compute virtualization,and network virtualization.The availability of the latter has also raised the demand for QoS.Due to the diversity of services,the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical,common,and impactful parameters.It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs.This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters:service quality,downtime of servers,and outage of cloud services.

Enhanced Trust Based Access Control for Multi-Cloud Environment

Security is an essential part of the cloud environment.For ensuring the security of the data being communicated to and from the cloud server,a significant parameter called trust was introduced.Trust-based security played a vital role in ensuring that the communication between cloud users and service providers remained unadulterated and authentic.In most cloud-based data distribution environments,emphasis is placed on accepting trusted client users’requests,but the cloud servers’integrity is seldom verified.This paper designs a trust-based access control model based on user and server characteristics in a multi-cloud environment to address this issue.The proposed methodology consists of data encryption using Cyclic Shift Transposition Algorithm and trust-based access control method.In this trust-based access control mechanism framework,trust values are assigned to cloud users using direct trust degrees.The direct trust degree is estimated based on the following metrics:success and failure rate of interactions,service satisfaction index,and dishonesty level.In addition to this,trust values are assigned to cloud servers based on the metrics:server load,service rejection rate,and service access delay.The role-Based Access control policy of each user is modified based on his trust level.If the server fails to meet the minimum trust level,then another suitable server will be selected.The proposed system is found to outperform other existing systems in a multi-cloud environment.

Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method

Many organizations apply cloud computing to store and effectively process data for various applications.The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity.In this research,an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data.Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data.Merkle Hash tree provides the efficient mapping of data and easily identifies the changesmade in the data due to proper structure.The developed model supports privacy-preserving public auditing to provide a secure cloud storage system.The data owners upload the data in the cloud and edit the data using the private key.An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches.The data files requested by the data owner are audit by a third-party auditor and the multiowner authentication method is applied during the modification process to authenticate the user.The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.

Binary Multifold Encryption Technique for Complex Cloud Systems

Data security is a major cloud computing issue due to different usertransactions in the system. The evolution of cryptography and cryptographic analysis are regarded domains of current research. deoxyribo nucleic acid (DNA) cryptography makes use of DNA as a sensing platform, which is then manipulated usinga variety of molecular methods. Many security mechanisms including knowledgebased authentication, two-factor authentication, adaptive authentication, multifactorauthentication and single password authentication have been deployed. These cryptographic techniques have been developed to ensure confidentiality, but most ofthem are based on complex mathematical calculations and equations. In the proposed approach, a novel and unique Hybrid helix scuttle-deoxy ribo nucleic acids(HHS-DNA) encryption algorithm has been proposed which is inspired by DNAcryptography and Helix scuttle. The proposed HHS-DNA is a type of multifold binary version of DNA (MF-BDNA). The major role of this paper is to present a multifold HHS-DNA algorithm to encrypt the cloud data assuring more security with lesscomplexity. The experimentation is carried out and it reduces the encryption time,cipher text size, and improves throughput. When compared with previous techniques, there is a 45% improvement in throughput, 37% fast in encryption time,54.67% cipher text size. The relevant experimental results and foregoing analysisshow that this method is of good robustness, stability, and security.