Cloud Computing Ecosystem Research Articles

A combined computing framework for load balancing in multi-tenant cloud eco-system

<span>Since the world is getting digitalized, cloud computing has become a core part of it. Massive data on a daily basis is processed, stored, and transferred over the internet. Cloud computing has become quite popular because of its superlative quality and enhanced capability to improvise data management, offering better computing resources and data to its user bases (UBs). However, there are many issues in the existing cloud traffic management approaches and how to manage data during service execution. The study introduces two distinct research models: data center virtualization framework under multi-tenant cloud-ecosystem (DCVF-MT) and collaborative workflow of multi-tenant load balancing (CW-MTLB) with analytical research modeling. The sequence of execution flow considers a set of algorithms for both models that address the core problem of load balancing and resource allocation in the cloud computing (CC) ecosystem. The research outcome illustrates that DCVF-MT, outperforms the one-to-one approach by approximately 24.778% performance improvement in traffic scheduling. It also yields a 40.33% performance improvement in managing cloudlet handling time. Moreover, it attains an overall 8.5133% performance improvement in resource cost optimization, which is significant to ensure the adaptability of the frameworks into futuristic cloud applications where adequate virtualization and resource mapping will be required.</span>

Cloud computing ecosystem model: evaluation and role clusters

Several significantly different attempts have been undertaken to visualize the complex and opaque cloud computing ecosystem by a descriptive model. Recently, scholars have developed a new role-based cloud ecosystem model (Passau Cloud Computing Ecosystem Model (PaCE model)) based on a systematic analysis and synthesis of the previous models. However, its scientific evaluation is still outstanding. The main goal is therefore to conduct an evaluation regarding its structural equivalence and completeness compared to the real cloud business according to the design science research paradigm. The second goal is to demonstrate the model’s usefulness by using it as an instrument for the identification of role clusters, which are frequently covered by organizations. To this end, a cross-sectional analysis is conducted, in which 758 organizations are manually analysed and matched with the model’s current roles. As a result, roles have been newly added, removed, broken down into sub-roles, abstracted to a meta-role and renamed. The cluster analysis unveiled five role clusters. Whereas some roles were predominantly found as single role, further roles were exclusively or very frequently found in combination with others. Overall, the revised model creates, compared to the common rather technically orientated market overviews by market research institutes, a detailed categorization schema helping to understand and investigate the organizations belonging to the cloud ecosystem.

A Multi-Objective Optimized Service Level Agreement Approach Applied on a Cloud Computing Ecosystem

The cloud ecosystem provides transformative advantages that allow elastically offering on-demand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients' needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.

REST Architecture Optimization in Cloud Computing Ecosystem to Support E Learning Platform

This study will present an application design process in the style of Representational State Transfer (REST) architecture to support the E-Learning platform in the cloud computing ecosystem. An application optimization process will be presented to provide E-Learning applications for schools, faculties or universities that in most cases need manual deployment and require more time for server provisioning.This process is optimized by providing application solutions that can provide speed of provisioning.The core system used Kubernetes containerization technology to provide scalability of growing E-Learning tenants. Evaluation of the core system architecture uses the Architecture Trade-off Analysis Method (ATAM) to evaluate aspect of performance and scalability as quality attributes. From the experimentalresults, the process of making new tenants for schools requires an average time of around 173.4 seconds. This meets the expectations of the set time limit of 5 minutes. The results of stress tests for 250 concurrent users show that the system has availability above 98%.Thus,education stakeholders such as schools and universities, no longer need to provide expensive e-learning infrastructure in the form of hardware or manpower to deploy the e-learning application on premise. In the future, this solution will provide a scalable E-Learning system that can spread at scale on the cloud computing ecosystem and support a Software as a Service solution in educational technology.

Бізнес-моделі хмарного надання ІТ-послуг

Бізнес-моделі хмарного надання ІТ-послуг

How Do Entrepreneurs Develop Business Models in Small High-Tech Ventures? An Exploratory Model from Australian IT Firms

AbstractThe objective of this article is to explore and explain how entrepreneurs develop new business models for new ventures. Though highly topical, there is little solid empirical knowledge of this issue. Findings from multiple case studies of firms operating in the Australian cloud-computing ecosystem reveal that developing a new business model involves three phases. In the first phase (business modelling ideation (BMI)), various ideas for a viable business model are generated and the most viable one is chosen. The strategic consensus and commitment are generated in the second phase, labelled as the “business modelling strategic commitment” (BMSC). The third phase, labelled as the “business model actualization” (BMAC) is the market-testing phase where the business model is reified or actualized. The theoretical and managerial implications of the findings are discussed and several directions for future research are suggested.

Student Privacy in the Cloud Computing Ecosystem - State of Play & Potential Paths Forward

This report draws from ongoing Privacy Initiative research as well as participant inputs from an April 2013 exploratory workshop, Student Privacy in the Cloud Computing Ecosystem, to begin to map the current landscape and connect the often-siloed perspectives of educational institutions, students, parents, and administrators as well as cloud service providers and policy makers. Following the workshop, the Berkman Center distilled a number of high-level observations that may be particularly critical in informing and catalyzing both future research and action. Some of the key points include the following: Expanding educational and knowledge sharing efforts will be integral to engage parents, students, and teachers alongside representatives from a variety of disciplines. These diverse stakeholders may have vastly different baseline knowledge of relevant issues — from cloud computing to privacy concerns to regulatory considerations — and addressing these differences may require a multi-pronged approach. In particular, it will be important to share up-to-date information about rapidly developing educational technologies so that potential users can better understand what data vendors might access and collect as well as what the vendor might do with this data. Developing a shared vocabulary and understanding of privacy and technological termswill be central to future research, outreach, policy, and educational efforts. Taxonomies and guides can help to cultivate a shared language and bolster communication and collaboration across educational, commercial, and regulatory settings. Good practices and draft standards, especially around contractual practices and terms of service, represent a fruitful area for attention. Stakeholders should enter this space in close communication with vendors and industry representatives. Ongoing normative analysis and discussion will be critical in grappling with the many dimensions of the topic and considering how core technologies, opportunities, and behaviors may evolve. In particular, the rapid emergence of data analytics technologies raise important questions about how existing legal frameworks should be interpreted or evolve. This dialogue should be grounded in real-world examples and data, and address open questions such as whether and how cloud technologies support broader educational values and if so, what specific value trade-offs may be involved. This report offers many more concrete details regarding research, policymaking, outreach, and engagement around student privacy and cloud computing.

Privacy and Children's Data - An Overview of the Children's Online Privacy Protection Act and the Family Educational Rights and Privacy Act

Privacy law in the United States is a complicated patchwork of state and federal caselaw and statutes. Harvard Law School’s Cyberlaw Clinic, based at the Berkman Center for Internet & Society, prepared this briefing document in advance of the Privacy Initiative's April 2013 workshop, Student Privacy in the Cloud Computing Ecosystem, to provide a high-level overview of two of the major federal legal regimes that govern privacy of children’s and students’ data in the United States: the Children’s Online Privacy Protection Act (COPPA) and the Family Educational Rights and Privacy Act (FERPA). This guide aims to offer schools, parents, and students alike a sense of some of the laws that may apply as schools begin to use cloud computing tools to help educate students. Both of the relevant statutes – and particularly FERPA – are complex and are the subjects of large bodies of caselaw and extensive third-party commentary, research, and scholarship. This document is not intended to provide a comprehensive summary of these statutes, nor privacy law in general, and it is not a substitute for specific legal advice. Rather, this guide highlights key provisions in these statutes and maps the legal and regulatory landscape.