YUMA -An AI Planning Agent for composing IT Services from Infrastructure-as-Code Specifications

2023 | HICSS | Citations: 0

Authors: Baer, F.; Leyer, M.

Abstract: Infrastructure-as-code enables cloud architects to automate IT service delivery ... Expand

Abstract: Infrastructure-as-code enables cloud architects to automate IT service delivery by specifying IT services through machine-readable definition files. To allow for a reusability of the infrastructure-as-code specifications, cloud architects specify IT services as compositions of sub-processes. As the AI planning agents for automated IT service composition proposed by prior research fall short in the infrastructure-as-code context, we design a search-based problem-solving agent named YUMA according to a design science research process to fill this research gap. YUMA holds a search tree reflecting the state space and transition model. It includes an algorithm for building the search tree and two algorithms for determining the minimum composition plan. The underlying IT service composition problem is explicated for the infrastructure-as-code context and formulated as a search problem. The results of the demonstration and evaluation show that YUMA fulfills the requirements necessary to solve this problem and digitizes an important task of cloud architects. Collapse

Topics: cloud computing artificial intelligence problem solving programming language source code

Methods: computational algorithm design artifact simulation reinforcement learning design process

CURIE: Towards an Ontology and Enterprise Architecture of a CRM Conceptual Model

2022 | Business & Information Systems Engineering | Citations: 0

Authors: Fernández-Cejas, Miguel; Pérez-González, Carlos J.; Roda-García, José L.; Colebrook, Marcos

Abstract: Companies face the challenge of managing customer relationships (CRM) in a conte ... Expand

Abstract: Companies face the challenge of managing customer relationships (CRM) in a context marked by a drastic digital transformation and unbridled evolution of consumer behavior, exacerbated by the COVID-19 pandemic. The customer is more demanding, has access to the global market and interacts with companies through multiple digital channels, such as email, social networks, mobile apps or instant messaging. In this situation, the success of a CRM implementation highly depends on information technology and the applications used. To harmonize this new business context with the development of information systems (IS), a suitable CRM ontology and enterprise architecture (EA) is needed. While an ontology-based conceptual model provides a unifying framework, aids sharing and reusing knowledge, and facilitates communication within a domain, an EA-based model unequivocally describes, analyzes, and visualizes how an organization should operate from the perspective of business, application, and technology. The purpose of the paper is the proposal of CURIE-O, a CRM OntoUML UFO-based ontology, together with CURIE-EA, a CRM ArchiMate-based EA to serve business managers and IS specialists an updated unifying framework of reference in the CRM domain as well as a highly efficient tool to support application development and maintenance in this changing and increasingly digital context. Modeling has proven to be an essential element to achieve high-performance information systems. In order to apply the ontology and the EA proposed here, the authors developed a CRM task management application prototype that was implemented as a case study in a consulting company. The methodology followed was design science research (DSR), in order to design and validate the artifacts. Within the DSR framework, other complementary research methods have been used, in particular literature research, interviews and focus groups carried out with several hotel chains in Tenerife (Canary Islands). The main existing CRM models in the scientific literature have also been analyzed together with the leading CRM market solutions. Collapse

Topics: knowledge representation microservice architecture Kubernetes PostgreSQL database system

Methods: ontological modelling design artifact conceptual modelling design science upper ontology

Using ChatOps to Achieve Continuous Certification of Cloud Services

2022 | HICSS | Citations: 0

Authors: Ohagen, Paul; Lins, Sebastian; Thiebes, Scott; Sunyaev, Ali

Abstract: Continuous service certification (CSC) recently emerged as a promising means to ... Expand

Abstract: Continuous service certification (CSC) recently emerged as a promising means to provide ongoing assurances and disrupt pertinent certification approaches. CSC involves the consistent gathering and assessing of certification-relevant data by certification authorities about service operation to validate ongoing adherence to certification criteria. While research on CSC is increasing, practitioners still struggle in transferring researchers' suggestions and guidelines into practice. This study provides a tentative design and a prototype of a monitoring-based service certification (MSC) system based on the novel ChatOps approach. Iterative evaluations support our propositions that ChatOps' three key elements, a chat platform, chatbots, and third-party integrations, support the achievement of CSC. We contribute to research and practice by proving the technical feasibility of an MSC system, guiding future research and practitioners on achieving monitoring-based CSC, and validate the applicability and usefulness of extant guidelines on monitoring-based CSC proposed by prior research. Collapse

Topics: cloud computing social media Slack Kubernetes data security

Methods: design artifact chatbot design artifact testing design science design requirement

End-to-End Latency Prediction of Microservices Workflow on Kubernetes: A Comparative Evaluation of Machine Learning Models and Resource Metrics

2021 | HICSS | Citations: 0

Authors: Mohamed, Haytham; El-Gayar, Omar

Abstract: Application design has been revolutionized with the adoption of microservices ar ... Expand

Abstract: Application design has been revolutionized with the adoption of microservices architecture. The ability to estimate end-to-end response latency would help software practitioners to design and operate microservices applications reliably and with efficient resource capacity. The objective of this research is to examine and compare data-driven approaches and a variety of resource metrics to predict end-to-end response latency of a containerized microservices workflow running in a cloud Kubernetes platform. We implemented and evaluated the prediction using a deep neural network and various machine learning techniques while investigating the selection of resource utilization metrics. Observed characteristics and performance metrics from both microservices and platform levels were used as prediction indicators. To compare performance models, we experimented with a benchmarking open-source Sock Shop containerized application. A deep neural network technique exhibited the best prediction accuracy using all metrics, while other machine learning techniques demonstrated acceptable performance using a subset of the metrics. Collapse

Topics: microservice architecture Kubernetes shapley additive explanation database system open source

Methods: machine learning deep learning computational algorithm XGBoost linear regression analysis

Insight from a Containerized Kubernetes Workload Introspection

2021 | HICSS | Citations: 0

Authors: Watts, Thomas; Benton, Ryan; Shropshire, Jordan; Bourrie, David

Abstract: Developments in virtual containers, especially in the cloud infrastructure, have ... Expand

Abstract: Developments in virtual containers, especially in the cloud infrastructure, have led to diversification of jobs that containers are used to support, particularly in the big data and machine learning spaces. The diversification has been powered by the adoption of orchestration systems that marshal fleets of containers to accomplish complex programming tasks. The additional components in the vertical technology stack, plus the continued horizontal scaling have led to questions regarding how to forensically analyze complicated technology stacks. This paper proposed a solution through the use of introspection. Collapse

Topics: Kubernetes cloud computing Docker ease of use database system

Methods: experiment random forest classification singular value decomposition data transformation machine learning

Welcome? Investigating the reception of new contributors to organizational-communal open source software projects

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Lumbard, Kevin; Buhman, Anna; Wethor, Gabrielle E.; Hale, Matthew; Goggins, Sean; Germonprez, Matt

Abstract: ... Expand

Abstract: Collapse

Topics: open source software project Kubernetes open source Linux Python

Methods: Zephyr sentiment analysis field study ANOVA parametric test

A cloud-based Analytics-Platform for user-centric Internet of Things domains – Prototype and Performance Evaluation

2020 | HICSS | Citations: 0

Authors: Zschörnig, Theo; Windolph, Jonah; Wehlitz, Robert; Franczyk, Bogdan

Abstract: Data analytics have the potential to increase the value of data emitted from sma ... Expand

Abstract: Data analytics have the potential to increase the value of data emitted from smart devices in usercentric Internet of Things environments, such as smart home, drastically. In order to allow businesses and end-consumers alike to tap into this potential, appropriate analytics architectures must be present. Current solutions in this field do not tackle all of the diverse challenges and requirements, which were identified in previous research. Specifically, personalized, extensible analytics solutions, which still offer the means to address big data problems are scarce. In this paper, we therefore present an architectural solution, which was specifically designed to address the named challenges. Furthermore, we offer insights into the prototypical implementation of the proposed concept as well as an evaluation of its performance against traditional big data architectures. Collapse

Topics: Apache Spark big data mobile system internet of things Kubernetes

Methods: experiment design artifact descriptive statistic business process modeling literature study

Incorporating Context and Location Into Social Media Analysis: A Scalable, Cloud-Based Approach for More Powerful Data Science

2019 | HICSS | Citations: 0

Authors: Anderson, Jennings; Saez, Gerard Casas; Anderson, Kenneth M; Palen, Leysia; Morss, Rebecca

Abstract: Dominated by quantitative data science techniques, social media data analysis of ... Expand

Abstract: Dominated by quantitative data science techniques, social media data analysis often fails to incorporate the surrounding context, conversation, and metadata that allows for more complete, accurate, and informed analysis. Here we describe the development of a scalable data collection infrastructure to interrogate massive amounts of tweets—including complete user conversations—to perform contextualized social media analysis. Additionally, we discuss the nuances of location metadata and incorporate it when available to situate the user conversations within geographic context through an interactive map. The map also spatially clusters tweets to identify important locations and movement between them, illuminating specific behavior, like evacuating before a hurricane. We share performance details, the promising results of concurrent research utilizing this infrastructure, and discuss the challenges and ethics of using context-rich datasets. Collapse

Topics: Twitter social media Kubernetes hurricane cloud computing

Methods: mixed method archival research literature study

Visualizing Interfirm Collaboration in the Microservices Ecosystem

2019 | HICSS | Citations: 1

Authors: Basole, Rahul

Abstract: The shift from monolithic software solutions to a microservices architecture is ... Expand

Abstract: The shift from monolithic software solutions to a microservices architecture is fundamentally changing the way software is developed, deployed, and managed. In this paper, we aim to uncover the collaborative fabric of the microservices ecosystem using a data-driven visualization approach of 2,608 software firms. Our visual analysis reveals a core-periphery structure with several subcommunities, suggesting both complementary and competing arrangements between software vendors. Theoretically, our paper contributes to our understanding of interfirm relationships in a software context. Managerially, our results show that there are wide range of partnership strategies that shape the microservices ecosystem. Methodologically, we demonstrate how a data-driven ecosystem visualization approach can help decision makers augment their sensemaking capability of emerging software ecosystems. The paper concludes with opportunities for future research. Collapse

Topics: microservice architecture digital ecosystem sensemaking cloud computing Docker

Methods: computational algorithm personal interview literature study

WHY IS WEB-BASED SUPPLY CHAIN MANAGEMENT POPULAR IN CHINA? A FACTOR ENDOWMENT’S PERSPECTIVE

2011 | International Conference on Information Systems | Citations: 0

Authors: Xiao, Jinghua; Wang, Hongchang; Cheng, Zhaolin; Xie, Kang; Hu, Qing

Abstract: Web-based system-to-human integration and direct system-to-system integration ar ... Expand

Abstract: Web-based system-to-human integration and direct system-to-system integration are alternative information technologies for supply chain management. Although the system-to-system mode is known to be more effective than the Web-based mode for data sharing, the web-based mode is dominant in the supply chain of Chinese firms. Is it a suboptimal or the most appropriate choice? We develop an analytical model to investigate how a manufacturer selects an optimal mode from competing technologies, including manual mode, Web-based mode and system-to-system mode. The contribution of this study is three-fold. First, the expected degree of information sharing influences the manufacturer’s selection. Manual mode is the optimal selection when the expected degree of information sharing is low, whereas system-to-system mode is the optimal choice when the expected degree is high. When the expected degree of information sharing is intermediate, Web-based mode is the optimal choice. Second, based on factor endowment theory, we find that labor endowment and technology endowment can influence the adoption intervals of these three modes. And third, when labor endowment is on the decline and technology endowment is on the rise in the early stage, the Web-based mode is the optimal choice than manual mode or system-tosystem mode. Theoretical and practical implications of these findings are discussed. Collapse

Topics: information exchange supply chain management IT investment Kubernetes

Methods: mathematical model

Theories: game theory