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).

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.

Management of dog bites by frontline service providers in primary healthcare facilities in the Greater Accra Region of Ghana,2014-2015

Background:Dog bites are common in developing countries including Ghana,with the victims often being children.Although some breeds of dogs have been identified as being more aggressive than others,all dog bites carry a risk of infection.Immediate and initial assessment of the risk for tetanus and rabies infection with appropriate interventions such as wound management and subsequent selection of prophylactic antibiotics are essential in the management of dog bites.This study examined the management of patients with dog bites by frontline service providers at primary healthcare facilities in the Greater Accra Region,Ghana.Methods:We conducted a cross-sectional study in 66 public health facilities in the Greater Accra Region from July 2014 to April 2015.Up to four frontline service providers were randomly selected to participate from each facility.A structured questionnaire was administered to all consenting participants.Continuous variables were presented as means and standard deviations.The frontline service providers’knowledge was assessed as a discrete variable and values obtained presented as percentages and proportions.The chi-square test of proportions was used to determine any significant associations between the various categories of the frontline service providers and their knowledge about the management of rabies.Results:Regarding the frontline service providers’knowledge about rabies,57.8%(134/232)were correct in that the rabies virus is the causative agent of rabies,39.2%(91/232)attributed it to a dog bite,2.6%(6/232)did not know the cause,and one person(0.4%)attributed it to the herpes virus.Only 15.5%(36/232)knew the incubation period in dogs and the period required to observe for signs of a rabies infection.With respect to the administration of rabies immunoglobulin,42.2%(98/232)of the frontline service providers did not know how to administer it.Of the facilities visited,76%(50/66)did not have the rabies vaccines and 44%(102/232)of frontline service providers did not know where to get the rabies vaccines from.Most of the service providers(87.9%;204/232)had never reported either a dog bite or a suspected case of rabies.Overall,there was gross underreporting of dog bites and suspected rabies cases at public healthcare facilities in the Greater Accra Region of Ghana.Conclusions:In view of the high morbidity and mortality associated with bites from rabid dogs and the poor knowledge and practices of frontline service providers,there is an urgent need for capacity-building such as training in the management of dog bites and subsequent potential rabies infection.

How different types of financial service providers support small- and mediumenterprises under the impact of COVID-19 pandemic: From the perspective of expectancy theory

The 2019 novel coronavirus disease(COVID-19)pandemic has significantly impacted several aspects of the society and the economy.A problem that needs prompt attention in this situation is the increasing difficulties faced by small-and mediumsized enterprises(SMEs)in raising capital,which has aroused great concern from multiple stakeholders such as public administrations and regulators.As the major supply of capital,financial service providers(FSPs)play a critical role in financing SMEs.However,how FSPs deal with SME financing during shocks has not yet been fully researched.Accordingly,in this study,a theoretical framework based on expectancy theory is proposed to explore the expected strategic adjustments of FSPs in financing SMEs.Specifically,this study investigates 272 FSPs in China on their expectancy and attitude on financing to SMEs during the COVID-19 pandemic.Furthermore,this study has divided FSPs into three categories:commercial banks,non-bank financial institutions,and credit-enhanced FSPs.Differences among these categories are compared and analyzed.

Evaluating and visualizing QoS of service providers in knowledgeintensive crowdsourcing:a combined MCDM approach

Purpose-As the number of joined service providers(SPs)in knowledge-intensive crowdsourcing(KI-C)continues to rise,there is an information overload problem for KI-C platforms and consumers to identify qualified SPs to complete tasks.To this end,this paper aims to propose a quality of service(QoS)evaluation framework for SPs in KI-C to effectively and comprehensively characterize the QoS of SPs,which can aid the efficient selection of qualified SPs.Design/methodology/approach-By literature summary and discussion with the expert team,a QoS evaluation indicator system for SPs in KI-C based on the SERVQUAL model is constructed.In addition,the Decision Making Trial and Evaluation Laboratory(DEMATEL)method is used to obtain evaluation indicators’weights.The SPs are evaluated and graded by the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)and rank-sum ratio(RSR),respectively.Findings-A QoS evaluation indicator system for SPs in KI-C incorporating 13 indicators based on SERVQUAL has been constructed,and a hybrid methodology combining DEMATEL,TOPSIS and RSR is applied to quantify and visualize the QoS of SPs.Originality/value-The QoS evaluation framework for SPs in KI-C proposed in this paper can quantify and visualize the QoS of SPs,which can help the crowdsourcing platform to realize differentiated management for SPs and assist SPs to improve their shortcomings in a targeted manner.And this is the first paper to evaluate SPs in KI-C from the prospect of QoS.

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.

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.

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.

Respecting Each Other and Being Loyal to ZTE Career Providing Refined Service and Focusing on Customers' Requirements Working Hard to Create Famous ZTE Brand Improving ZTE Efficiency with Scientific Management

ZTE Corporation is China’s largest listedtelecommunications equipment provider spe-cialized in offering a full range of tailor-madesolutions for customers in high-,middle-andlow-end markets.

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.

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.

Accelerating Quantum Computing Readiness: Risk Management and Strategies for Sectors

The potential impact of quantum computing on various industries such as finance, healthcare, cryptography, and transportation is significant;therefore, sectors face challenges in understanding where to start because of the complex nature of this technology. Starting early to explore what is supposed to be done is crucial for providing sectors with the necessary knowledge, tools, and processes to keep pace with rapid advancements in quantum computing. This article emphasizes the importance of consultancy and governance solutions that aid sectors in preparing for the quantum computing revolution. The article begins by discussing the reasons why sectors need to be prepared for quantum computing and emphasizes the importance of proactive preparation. It illustrates this point by providing a real-world example of a partnership. Subsequently, the article mentioned the benefits of quantum computing readiness, including increased competitiveness, improved security, and structured data. In addition, this article discusses the steps that various sectors can take to achieve quantum readiness, considering the potential risks and opportunities in industries. The proposed solutions for achieving quantum computing readiness include establishing a quantum computing office, contracting with major quantum computing companies, and learning from quantum computing organizations. This article provides the detailed advantages and disadvantages of each of these steps and emphasizes the need to carefully evaluate their potential drawbacks to ensure that they align with the sector’s unique needs, goals, and available resources. Finally, this article proposes various solutions and recommendations for sectors to achieve quantum-computing readiness.

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.

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.

A Multi-Factor Authentication-Based Framework for Identity Management in Cloud Applications

User’s data is considered as a vital asset of several organizations.Migrating data to the cloud computing is not an easy decision for any organization due to the privacy and security concerns.Service providers must ensure that both data and applications that will be stored on the cloud should be protected in a secure environment.The data stored on the public cloud will be vulnerable to outside and inside attacks.This paper provides interactive multi-layer authentication frameworks for securing user identities on the cloud.Different access control policies are applied for verifying users on the cloud.A security mechanism is applied to the cloud application that includes user registration,granting user privileges,and generating user authentication factor.An intrusion detection system is embedded to the security mechanism to detect malicious users.The multi factor authentication,intrusion detection,and access control techniques can be used for ensuring the identity of the user.Finally,encryption techniques are used for protecting the data from being disclosed.Experimental results are carried out to verify the accuracy and efficiency of the proposed frameworks and mechanism.The results recorded high detection rate with low false positive alarms.

Baseline of the Use of Solar Irrigation Pump in the Niayes Area in Senegal

Solar irrigation pumps are considered as an alternative to the use of diesel and electric pumps due to the high cost of energy. These pumps are now increasingly used in the Niayes area. Thus, a more in-depth knowledge about the components of a solar pumping system and their characteristics available on the market as well as prices can be beneficial. This study was conducted to have an idea on the baseline of the use of solar irrigation pumps in this area. To perform this study, surveys were conducted among 12 suppliers and 10 service providers located in Dakar, Thies, Louga and Saint-Louis region and among 53 users located in Potou area which is in Louga region. Results show a wide range of pump brands and characteristics. Brands found were Lorentz, LIKOU, SHIMGE, Grundfos, Solar pump, Feili, Difful, JUQIANG, Solar tech, and Asaman. The pumps’ flow rate varies between 2.5 m3/h and 45 m3/h, the pressure ranges from 15 m to 160 m and the power is between 0.072 kW and 10 kW. The price of these pumps varies depending on the brand used and their characteristics. In addition, other materials are available, such as panels with power ranging from 80 W to 330 W and electric cables. The price of these components varies according on the type used. Thus, the investment cost for implementation varies between 640 euros (420,000 CFA) and 25,087 euros (16,455,919 CFA). The cost of installation varies between 76.3 euros (50,000 CFA) and 1219.6 euros (800,000 CFA). Concerning maintenance, it is generally free during the warranty period when the equipment is supplied and installed by the same company. However, the cost of maintenance varies between 152.5 euros and 457.4 euros/year or is estimated at 45.7 euro/maintenance after the warranty years. Investigations conducted among users show that farmers in general do not perform pump maintenance due to a lack of knowledge and financial means. Thus, according to farmers, factors that impact pumps operation are the low sunshine that occurs between December and January and the iron in the water and low well discharge.

Effect of Donor Funding on the Performance of Water Utilities in Kenya

The objective of this study was to analyse the effect of donor funding on the performance of water utilities in Kenya.The study employed the use of a census by targeting all 88 regulated Water Services Providers(WSPs)in Kenya for a period of two years,2016 and 2017.Data pertaining to the support received from the donors were obtained from the publications of the WSPs,Development Partners,Civil Society Organisations(CSOs),and Office of Auditor General(OAG).Performance data of the various WSPs were extracted from the Impact reports produced by Water Regulatory Board(WASREB),for the periods 2016/7 to 2017/8.Presentation of the data was done through the use of tables and charts with the application of SPSS.The study found that donor funding issued as Output Based Approach(OBA)leads to improved performance.This finding is expected to assist the Kenyan Government in negotiating for the Official Development Assistance(ODA)funding to be aligned to the government flagship projects under the Medium Term Plans(MTP).The study recommends OBA as the best way of issuing donor funding.

Cloud-Based Software Development Lifecycle:A Simplified Algorithm for Cloud Service Provider Evaluation with Metric Analysis

At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the market.Thus,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However,they require more time to collect data,simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage allocated.Furthermore,the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP,after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.