Cost comparison of running web applications in the cloud using monolithic,microservice, and AWS Lambda architectures

Large Internet companies like Amazon, Netflix, and LinkedIn are using the microservice architecture pattern to deploy large applications in the cloud as a set of small services that can be independently developed, tested, deployed, scaled, operated, and upgraded. However, aside from gaining agility, independent development, and scalability, how microservices affect the infrastructure costs is a major evaluation topic for companies adopting this pattern. This paper presents a cost comparison of a web application developed and deployed using the same scalable scenarios with three different approaches: 1) a monolithic architecture, 2) a microservice architecture operated by the cloud customer, and 3) a microservice architecture operated by the cloud provider. Test results show that microservices can help reduce infrastructure costs in comparison with standard monolithic architectures. Moreover, the use of services specifically designed to deploy and scale microservices, such as AWS Lambda, reduces infrastructure costs by 70% or more, and unlike microservices operated by cloud customers, these specialized services help to guarantee the same performance and response times as the number of users increases. Lastly, we also describe the challenges we faced while implementing and deploying microservice applications, and include a discussion on how to replicate the results on other cloud providers.     

A security evaluation framework for cloud security auditing

Cloud computing is clearly one of today’s most enticing technologies due to its scalable, flexible, and cost-efficient access to infrastructure and application services. Despite these benefits, cloud service users (CSUs) have serious concerns about the data security and privacy. Currently, there are several cloud service providers (CSPs) offering a wide range of services to their customers with varying levels of security strengths. Due to the vast diversity in the available cloud services, from the customer’s perspective, it has become difficult to decide which CSP they should use and what should be the selection criteria. Presently, there is no framework that can allow CSUs to evaluate CSPs based on their ability to meet the customer’s security requirements. We propose a framework and a mechanism that evaluate the security strength of CSPs based on the customer’s security preferences. We have shown the applicability of our security evaluation framework using a case study.     

Sicheres und nachhaltiges Benchmarking in der Cloud

Cloud computing entails a novel security threat: The cloud service provider is entrusted with the data of all its customers. This may not be sustainable for highly confidential data. Encryption, or more generally cryptography, may provide a solution by computing on data encrypted by the customers. While this solution is theoretically appealing, it raises a number of research questions in information system design. Using the example of collaborative benchmarking the author presents and evaluates an exemplary design and implementation of a cloud application that operates only on encrypted data, thus protecting the confidentiality of the customer??s data against the cloud service provider. The cloud application computes common statistics for benchmarking without disclosing the individual key performance indicators. Benchmarking is an important process for companies to stay competitive in today??s markets. It allows them to evaluate their performance against the statistics of their peers and implement targeted improvement measures.     

A methodical procedure for designing consumer oriented on-demand IT service propositions

IT providers are increasingly facing the challenge to adapt their previously resource oriented service portfolios in order to offer their customers services which explicitly support business processes. Such customer centric service propositions, however, seem to contradict the demand for standardized and automated operational IT processes more than traditional IT service offers, as they are even more subject to customer individual reengineering efforts due to permanently changing business requirements. In order to reconcile increased efficiency in operational processes and effectiveness in consumer oriented service propositions, we propose (1) to predefine all service propositions in consideration of both consumer oriented commitments and operational processes, and (2) to allow for standardized customization by offering a selection of complementary service propositions that extend commitments regarding customer oriented functionality and performance. Such service propositions are aligned with a company’s entities such as workplaces. Thereby the customer organization is enabled to trace, control and adjust commitments, value and expenses of IT services per entity in its business. We introduce a procedural model for designing and on-demand requesting this kind of service propositions, and we illustrate the model’s application and impact by examples taken from two large projects with an associated IT provider.     

Strategic signaling through cloud service certifications: Comparing the relative importance of certifications’ assurances to companies and consumers

Cloud service certifications (CSCs) are assessed by practitioners to support strategic cloud adoption decisions with the aim to reduce information asymmetries. Both businesses and consumers scrutinize CSCs’ assurances as ex ante signals indicating a cloud provider’s future service quality. While some research has examined the aggregate effects of certifications on decision variables, recipients’ evaluations of certifications and their assurances before making IT-related decisions have received little attention. Furthermore, prior research has predominantly focused on privacy and security assurances in e-commerce certifications. Drawing on signaling theory, we propose that certifications are signals that recipients decompose into a set of fine-grained assurance signals that they weigh to evaluate certifications. We evaluate the responses of 113 company representatives and 317 consumers to a best-worst scaling survey to examine the relative importance these two groups attach to ten assurances from CSCs. Our results show that similar to other online contexts, security and privacy are important assurances, but additional assurances related to availability, the customer friendliness of contracts, and legal compliance are also demanded, particularly by companies. Privacy, security, and availability are most crucial to both companies and consumers, but their relative importance varies substantially between the two groups. Post-hoc subgroup analyses reveal significant differences in assurances’ relative importance for provider and user companies, adopter and non-adopter consumers as well as companies using different types of services and from different industries. Our findings indicate that recipients evaluate certifications as a bundle of signals with varying importance due to recipients’ characteristics and context. With this conceptualization, we contribute to an advanced understanding of the sense-making of certifications and lay out how it influences cloud service adoption theories. Our study has practical implications for certification authorities that design CSCs as well as for providing insights to cloud service providers on customers who draw on CSC assurances when making cloud service adoption decisions.     

Model-based services convergence and multi-clouds integration

With the development of cloud computing, IT users (individuals, enterprises and even public services providers) are transferring their jobs or businesses to public online services provided by professional information service companies. These information service companies provide applications as public resources to support the business operation of their customers. However, no cloud computing service vendor (CCSV) can satisfy the full functional information system requirements of its customers. As a result, its customers often have to simultaneously use services distributed in different clouds and do some connectivity jobs manually. Services convergence and multi-clouds integration will lead to new business models and trigger new integration technologies that provide solutions to satisfy IT users’ complicated requirements. This paper firstly reviews the development of cloud computing from business and technical viewpoints and then discusses requirements and challenges of services convergence and multi-clouds integrations. Thirdly, a model based architecture of multi-clouds integration is provided. Business logic modelling for cross-organizational collaboration, service modelling and operation modelling methods with relative model mapping technology are discussed in detail. Some key enabling technologies are also developed. At last, case studies are presented to illustrate the implementation of the technologies developed in the paper.     

Satisfaction Benchmarking and Customer Classification: An Application to the Branches of a Banking Organization

Banking services constitute a highly competitive market and indicate a representative example of customer–oriented organizations. For this reason, customer satisfaction is of vital importance, offering a quantitative measure for current and future performance of these organizations. On the other hand, classifying customers according to their satisfaction behavior may indicate different client clusters with distinctive preferences and expectations. This approach serves the development of a truly customer–focussed culture by determining a set of improvement strategies that best fit the different customer segments. In this paper, the multi–criteria method MUSA is implemented in order to measure and analyze customer satisfaction in different branches of a banking organization. These results are also used to benchmark these branches according to the provided services. Moreover, segmentation analysis is performed in order to identify the different groups of customers and estimate the homogeneity of preferences in distinct customer segments. Data are based on a pilot customer–satisfaction survey, while the most important results are focussed on the determination of the critical service dimensions.     

Flamingos on a slackline: Companies' challenges of balancing the competing demands of handling customer information and privacy

Providers of online services are under increasing pressure to leverage the value inherent in customer data to remain competitive. At the same time, however, Internet users' privacy has become more and more subject to public debate, and companies must protect this privacy if they want to attract and retain customers. It seems that companies struggle to satisfy these competing demands, and neglecting either could have detrimental effects on their success. Different streams of research underline the importance of either privacy protection or the collection and use of customer information. But they remain largely silent about the resulting challenges that companies face. In this study, we address this gap by exploring how companies perceive and handle the tensions between their organizational information needs that necessitate some degree of privacy intrusion and their need to attract and retain customers, which requires privacy protection. We develop a grounded theory that explains how companies balance these opposing demands from an organizational perspective. More specifically, we identify four challenging tensions that define the conditions under which companies must operate. We find that companies try to achieve and maintain a state of balance in the presence of these tensions, and we discover three categories of tactics that help companies in their balancing acts. Our research contributes to a provider‐centric perspective on information privacy and uncovers the management of customer information and privacy as a new but important context in which organizational ambidexterity is required. We offer a detailed perspective on the specific challenges that companies face with respect to customer information and privacy, and our results can guide managers who have to deal with these challenges.     

A historical probability based noise generation strategy for privacy protection in cloud computing

Cloud computing promises an open environment where customers can deploy IT services in pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service providers can exist. Such service providers may record service requests from a customer and then collectively deduce the customer private information. Therefore, customers need to take certain actions to protect their privacy. Obfuscation with noise injection, that mixes noise service requests with real customer service requests so that service providers will be confused about which requests are real ones, is an effective approach in this regard if those request occurrence probabilities are about the same. However, current obfuscation with noise injection uses random noise requests. Due to the randomness it needs a large number of noise requests to hide the real ones so that all of their occurrence probabilities are about the same, i.e. service providers would be confused. In pay-as-you-go cloud environment, a noise request will cost the same as a real request. Hence, with the same level of confusion, i.e. customer privacy protection, the number of noise requests should be kept as few as possible. Therefore in this paper we develop a novel historical probability based noise generation strategy. Our strategy generates noise requests based on their historical occurrence probability so that all requests including noise and real ones can reach about the same occurrence probability, and then service providers would not be able to distinguish in between. Our strategy can significantly reduce the number of noise requests over the random strategy, by more than 90% as demonstrated by simulation evaluation.     

Matrix based proactive resource provisioning in mobile cloud environment

Mobile cloud computing is a dynamic, virtually scalable and network based computing environment where mobile device acts as a thin client and applications run on remote cloud servers. Mobile cloud computing resources required by different users depend on their respective personalized applications. Therefore, efficient resource provisioning in mobile clouds is an important aspect that needs special attention in order to make the mobile cloud computing a highly optimized entity. This paper proposes an adaptive model for efficient resource provisioning in mobile clouds by predicting and storing resource usages in a two dimensional matrix termed as resource provisioning matrix. These resource provisioning matrices are further used by an independent authority to predict future required resources using artificial neural network. Independent authority also checks and verifies resource usage bill computed by cloud service provider using resource provisioning matrices. It provides cost computation reliability for mobile customers in mobile cloud environment. Proposed model is implemented on Hadoop using three different applications. Results indicate that proposed model provides better mobile cloud resources utilization as well as maintains quality of service for mobile customer. Proposed model increases battery life of mobile device and decreases data usage cost for mobile customer.     

Customer loyalty programs to sustain consumer fidelity in mobile telecommunication market

Companies realized the importance of well-managing their relationships with their customers. Customer Relationship Management (CRM) allows companies to manage their marketing strategies and deliver specific services to clients with different values. The mobile telecommunication market is a very competitive market where the customers are tended to move from one company to another easily. Mobile telecommunication companies should carry on specific programs and services to their customers in order to keep them satisfied and thus ensure their fidelity with the company. In this article our objective is to provide companies a model that facilitates to decide what kind of customer loyalty programs they should address to their clients from different segments. In order to do that we present a fuzzy based Hungarian method that allow assigning different loyalty programs to customers with different characteristics.     

Beziehungsmarketing aus Kundensicht

Relationship Marketing from a Customer’s Perspective Companies need a wealth of personal data in order to address customers in a targeted manner. Utilizing IT enables them to directly address customers in order to increase sales by improving customer retention. The use of personal data becomes problematic when customers decline the use of their data. If companies ignore their customers’ privacy preferences, their chances of retaining them with targeted offers decline. In this paper, we first identify opportunities and drawbacks of individualization measures from a customer’s perspective based on the findings of a qualitative survey. The results of a quantitative survey illustrate how people’s general attitude toward personal data transmission determines their perception of personalization measures.

Engereicht am 2006-05-12, nach fünf überarbeitungen angenommen am 2006-10-15 durch die Herausgeber des Schwerpunktthemas.     

Enhancing customer relationships by semantic consumer support systems

For enterprises to succeed in their business, customer relationships have been commonly recognized as a critical factor. Effective customer relationships help enterprises deliver services to customers based on their needs or preferences. In this context, many ideas have already been presented among which Consumer Support Systems (CSS) is the most recently introduced, and one that alleviates the shortcomings of other approaches by a 4-layer framework of collaborative mechanisms to support effective information/service provision between customers and enterprises. Since there are many integration issues to be addressed in CSS (e.g., sharing of information/requests among customers or comparison of services from enterprises), a comprehensive structure for easy sharing/comparison of this information/requests/services in the literature is therefore necessary. To meet this need, we present a specification method in this paper that takes advantage of the pristine structure for knowledge-sharing by ontology to specify these materials in respective ontologies. With such ontological semantics, CSS is particularly able to support dynamic service provision for customers where desired requests can be satisfied by suitable services in a dynamic manner through referencing any matches between request and service ontologies. The method is illustrated by a semantic CSS for travel arrangements and more discussions about the application of its constructed ontologies for enhancing customer relationships are also provided.     

Mixed-initiative synthesized learning approach for web-based CRM

The issue of customer relationship management has emerged rapidly. Customers have become one of the most important considerations to new companies being built. Accordingly, customer retention is a very important topic. In this paper, we present a mixed-initiative synthesized learning approach for better understanding of customers and the provision of clues for improving customer relationships based on different sources of web customer data. The approach is a combination of hierarchical automatic labeling SOM, decision tree, cross-class analysis, and human tacit experience. The objective of this approach is to hierarchically segment data sources into clusters, automatically label the features of the clusters, discover the characteristics of normal, defected and possibly defected clusters of customers, and provide clues for gaining customer retention.     

iValue: A knowledge-based system for estimating customer prospect value

In the past, companies changed their focus from product-oriented within marketing to demand-oriented within quality improvement. Today, they emphasize customer service, customer loyalty, and customer profitability. The significance of customer-centric services has become critical and essential. However, certain research which investigates the effect of customer lifetime value focuses only on lifetime values of existing customers. This study devises a novel model to predict customers’ prospect value. In the proposed model, we utilize the concept of finance which stands in the current status and predicts future value based on historical data. The simulated results reveal that, in a long-term simulation, customer prospect value rises when reach rate increases. Decreasing reach rate and costs result in high customer prospect value; however, the value of customer prospect value decreases in a long-term simulation. The new model complements the existing customer lifetime value model from a different perspective and provides clues to customer value for future researches.     

A recommender system to avoid customer churn: A case study

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.     

Exploring consumer preferences in cloud archiving – a student's perspective

Cloud storage has seen an increasing rise in demand and diffusion. Consequently, the cloud storage market is also becoming an increasingly commoditised market. That is, homogenous products are offered at equal prices, and this offer makes it more difficult for cloud storage providers to generate revenue and differentiate themselves from their competitors. Therefore, it is vital for providers to precisely understand customer preferences so that these can be targeted with appropriate services. To examine these preferences, we conduct a choice experiment and analyse choice decisions gathered from 340 German students by means of a conjoint analysis. We perform an individual-level analysis of preferences, which reveals significant differences and heterogeneity within the sample. By using a subsequent cluster analysis, we identify three distinct customer segments that also show significant differences in, for example, the perceptions of information privacy and risks. Our findings contribute to the literature by uncovering the preference structure and trade-offs that users make in their choice of storage services when employed for the purpose of archiving. We conclude the study with a discussion of practical implications that can aid cloud storage providers in service design decisions, and highlight the limitations associated with our research approach and drawn sample.     

Scalable risk assessment method for cloud computing using game theory (CCRAM)

Cloud computing is one of the most popular information processing concepts of today's IT world. The security of the cloud computing is complicated because each service model uses different infrastructure elements. Current security risk assessment models generally cannot be applied to cloud computing systems that change their states very rapidly. In this work, a scalable security risk assessment model has been proposed for cloud computing as a solution of this problem using game theory. Using this method, we can evaluate whether the risk in the system should be fixed by cloud provider or tenant of the system.