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An Adaptive Trust Model of Web Services

This paper proposes a dynamic Web service trust (WS Trust) model, and somecorresponding trust metric evaluation algorithms. The main goal is to evaluate the trustworthinessand predict the future bchaxiors of entities in oeder to help users find trustworthy Web serviceproviders and prevent users from providing unfair ratings against service providers.

Mining and Modelling the Dynamic Patterns of Service Providers in Cellular Data Network Based on Big Data Analysis

Understanding the dynamic traffic and usage characteristics of data services in cellular networks is important for optimising network resources and improving user experience.Recent studies have illustrated traffic characteristics from specific perspectives,such as user behaviour,device type,and applications.In this paper,we present the results of our study from a different perspective,namely service providers,to reveal the traffic characteristics of cellular data networks.Our study is based on traffic data collected over a five-day period from a leading mobile operator's core network in China.We propose a Zipf-like model to characterise the distributions of the traffic volume,subscribers,and requests among service providers.Nine distinct diurnal traffic patterns of service providers are identified by formulating and solving a time series clustering problem.Our work differs from previous related works in that we perform measurements on a large quantity of data covering 2.2 billion traffic records,and we first explore the traffic patterns of thousands of service providers.Results of our study present mobile Internet participants with a better understanding of the traffic and usage characteristics of service providers,which play a critical role in the mobile Internet era.

A K-Median Facility Location Agent for Low-Cost Service Selection in Digital Community Network

With the increasing number of web services, it becomes a difficult task for an ordinary user to select an appropriate service. Hence, it is conventional that users in a digital community network take part in a collaborative mechanism for the purpose of service selection. The participation usually brings unnecessary burdens for users, such as giving opinions, storing service information. Extra communication overhead hinders the performance of the network. Thus, the community administrators are facing a problem of how to obtain an overall service selection result for the whole community readily and effectively. To address this problem, we present a k-median facility location agent model. The model analyzes the procedure of service selection through five entities and six types of messages. Two algorithms are elaborated in pursuit of a global optimization concerning connection costs between users and facilities where services are deployed. To evaluate our model, we conduct extensive simulations and present a detailed analysis of the simulation results.

An Integrated Framework for Cloud Service Selection Based on BOM and TOPSIS

Many businesses have experienced difficulties in selecting a cloud service provider(CSP)due to the rapid advancement of cloud computing services and the proliferation of CSPs.Many independent criteria should be considered when evaluating the services provided by different CSPs.It is a case of multi-criteria decision-making(MCDM).This paper presents an integrated MCDM cloud service selection framework for determining the most appropriate service provider based on the best only method(BOM)and technique for order of preference by similarity to ideal solution(TOPSIS).To obtain the weights of criteria and the relative importance of CSPs based on each criterion,BOM performs pairwise comparisons of criteria and also for alternatives on each criterion,and TOPSIS uses these weights to rank cloud alternatives.An evaluation and validation of the proposed framework have been carried out through a use-case model to prove its efficiency and accuracy.Moreover,the developed framework was compared with the analytical hierarchical process(AHP),a popular MCDM approach,based on two perspectives:efficiency and consistency.According to the research results,the proposed framework only requires 25%of the comparisons needed for the AHP approach.Furthermore,the proposed framework has a CR of 0%,whereas AHP has 38%.Thus,the proposed framework performs better than AHPwhen it comes to computation complexity and consistency,implying that it is more efficient and trustworthy.

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).

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.

Selecting a Strategic Logistics Service Provider for Subsea Engineering Companies on the Tweneboa, Enyenra and Ntomme （TEN） Project in Ghana

The research dealt with assessment of logistics service providers＇ capabilities in SECs （supporting subsea engineering companies） on the Tweneboa, Enyenra and Ntomme （TEN） project in Ghana. SECs need logistics service providers who offer consolidated services for project optimization and fulfillment of local content requirements of the project. Relevant Staffof7 SECs and 10 logistics service providers in Ghana formed part of the target group. The weighted scoring model was used in selecting a logistics service provider based on certain criteria. The selection criteria included Quality, Health, Safety and Environmental （QHSE） policies [Q], Compliance to regulations （GRA, Work permit, petroleum regulation） [C], Delivery of goods on time and response rate to deliver [D], Vessel, Cargo attendance and transportation （sea freight and air freight） [V], and Logistics （Personal logistics and Logistics base facilities） [L]. The study concluded Consolidated Shipping Agency （Conship） was the best Logistics Service Provider to offer the best consolidated services for project optimization under the TEN project.

Survival Analysis of Logistics Service Providers: An Empirical Study of Chengdu, Area in China

This paper worked on a sample of 6791 logistics establishments registered in Chengdu, China over the period 1984-2016 to understand the survival status of logistics service providers (LSPs) by non-parametric Kaplan-Meier estimation, together with Cox proportional hazard regression model, to identify factors affecting the failure of LSPs. In particular, it studies the interaction effect between LSPs’ size and entry timing and location. The empirical results show that: 1) Regarding the survival time, 1365 of the 6791 sample LSPs exited from the market by 2017. The exit rate is 20.1%, and the average life of the 6791 LSPs is about 6 years. 2) The survival of LSPs depends on their typology, ownership structure. And there is no significant difference in the probability of survival for both independent LSPs and logistics branches after controlling the effects of other variables. 3) Location and entry timing also play an important role in the survival of small-scale LSPs, but these factors cannot explain large-scale LSPs’ failure.

CGWIC Will Provide Launch Services for CBERS-04A

China Great Wall Industry Corporation（CGWIC）under CASC,and the Brazil National Institute for Space Research（INPE）signed a launch service contract for the China-Brazil Earth Resources Satellite-04A（CBERS-04A）recently,marking a new step in space cooperation between the two countries.

Information Security in the Cloud: Emerging Trends and Challenges

This article explores the evolution of cloud computing, its advantages over traditional on-premises infrastructure, and its impact on information security. The study presents a comprehensive literature review covering various cloud infrastructure offerings and security models. Additionally, it deeply analyzes real-life case studies illustrating successful cloud migrations and highlights common information security threats in current cloud computing. The article concludes by offering recommendations to businesses to protect themselves from cloud data breaches and providing insights into selecting a suitable cloud services provider from an information security perspective.

A SOLUTION TO HIGH AVAILABILITY OF OPENSTOCK

High availability is a critical mission for business system. At first, an instance of business system OPENSTOCK for pharmacy is introduced including both client and server sides. Secondly, a solution to the high availability of this system is given in detail, including design and implementation. The essentiality of this solution consists of scope of system information, system parameter tables of service status, schedule strategies of load ba lance and how to acquire system parameters and detect service states. The solution proposed is scalable and application oriented and supporting load balance for high performance and fault tolerate for high reliability. This application system has been applied and verified realistically, and the features of this business system derived in this paper have been achieved.

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.

Joint resource allocation scheme based on evolutionary game for mobile edge computing

To satisfy mobile terminals ’( MTs) offloading requirements and reduce MTs’ cost,a joint cloud and wireless resource allocation scheme based on the evolutionary game( JRA-EG) is proposed for overlapping heterogeneous networks in mobile edge computing environments. MTs that have tasks offloading requirements in the same service area form a population. MTs in one population acquire different wireless and computation resources by selecting different service providers( SPs). An evolutionary game is formulated to model the SP selection and resource allocation of the MTs. The cost function of the game consists of energy consumption,time delay and monetary cost. The solutions of evolutionary equilibrium( EE) include the centralized algorithm based on replicator dynamics and the distributed algorithm based on Q-learning.Simulation results show that both algorithms can converge to the EE rapidly. The differences between them are the convergence speed and trajectory stability. Compared with the existing schemes,the JRA-EG scheme can save more energy and have a smaller time delay when the data size becomes larger. The proposed scheme can schedule the wireless and computation resources reasonably so that the offloading cost is reduced efficiently.

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.

An Integrated and Comprehensive Fuzzy Multi-Criteria Model for Electronic Wallet Selection

Electronic wallet(E-wallet),which combines software,hardware,and human interaction,is a modern platform for electronic payment.Financial services managers can create more effective strategies for advancing existing positions by evaluating the success of e-wallet services.The objective of this research is to create an integrated model for assessing E-wallet services using the Fuzzy Analytic Hierarchy Process(FAHP)in conjunction with a technique for extent analysis and the ARAS approach(Additive Ratio Assessment).In this study,E-wallet service providers are ranked and evaluated using the ARAS approach and the FAHP priority weights.In addition,a case study from Vietnam illustrates how the methodology is used.The data shows that perceived usefulness,perceived usability,perceived simplicity of use,and perceived threat have the least impact on how acceptable an Ewallet is.As a result,the suggested model improves the efficiency of E-wallet service operations and assists in overcoming the challenges associated with the evaluation of E-wallet services.The case finding demonstrates that the suggested model is a reliable and useful tool for making decisions when assessing E-wallet service providers functioning in a complex and changing environment.

Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable.Phishing websites also create traffic in the entire network.Another phishing issue is the broadening malware of the entire network,thus highlighting the demand for their detection while massive datasets(i.e.,big data)are processed.Despite the application of boosting mechanisms in phishing detection,these methods are prone to significant errors in their output,specifically due to the combination of all website features in the training state.The upcoming big data system requires MapReduce,a popular parallel programming,to process massive datasets.To address these issues,a probabilistic latent semantic and greedy levy gradient boosting(PLS-GLGB)algorithm for website phishing detection using MapReduce is proposed.A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing detection.Here,the missing data in each URL are identified and discarded for further processing to ensure data quality.Subsequently,with the preprocessed features(URLs),feature vectors are updated by the greedy levy divergence gradient(model)that selects the optimal features in the URL and accurately detects the websites.Thus,greedy levy efficiently differentiates between phishing websites and legitimate websites.Experiments are conducted using one of the largest public corpora of a website phish tank dataset.Results show that the PLS-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection methods.Significant amounts of phishing detection time and errors are also saved during the detection of website phishing.