Research Trend Of Business Startup Performance: Bibliometric Analysis

This paper has analyzed research trends regarding business startup performance in 2000-2023. The aims of this study were to identify: (1) the trend of publication of startup performance in the Google Scholar database in 2000-2023; (2) publishers that publish the most startup performances; (3) researchers who actively examine startup performance; (4) most cited article titles, (5) publication network map based on startup performance keywords. Research on startup performance was still lacking, while the phenomenon indicates the number of startups was growing, but it has not been used as an object of research, especially in strategic management. Data collection through Google Scholar uses Publish or Perish with the keyword "startup performance". Research data includes the number of publications per year, journal name, author name, year of publication, publisher, and number of citations. Furthermore, the data was analyzed using Excel. Analysis of publication trends using VOSviewer. Data analysis techniques using descriptive statistics. The results indicate: (1) publications with the theme of startup performance in the Google Scholar database for 2000-2023 totaling 172 articles; (2) Elsevier was the publisher which publishes the most articles on startup performance; (3) writers who actively research startup performance, such us: individuals (Aaron Chatterji) and collaborations (Joonkyu Choi, Nathan Goldschlag, John C. Haltiwanger, and J. Daniel Kim); (4) Ming Mao and Marty Humphrey's article entitled “A Performance Study on The VM Startup Time in The Cloud” was the most cited (675 citations); and (5) there were five clusters that have the opportunity to become gap research related to startup performance themes (business, business startup performance, entrepreneur, post startup performance, social capital, startup performance, venture, performance evaluation, role, and rapid startup performance). The research results have implications for further research that the theme of startup performance was still rarely researched, and it has the potential to be a new research in the field of strategic management

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

An Experiment on Bare-Metal BigData Provisioning

Many BigData customers use on-demand platforms in the cloud, where they can get a dedicated virtual cluster in a couple of minutes and pay only for the time they use. Increasingly, there is a demand for bare-metal bigdata solutions for applications that cannot tolerate the unpredictability and performance degradation of virtualized systems. Existing bare-metal solutions can introduce delays of 10s of minutes to provision a cluster by installing operating systems and applications on the local disks of servers. This has motivated recent research developing sophisticated mechanisms to optimize this installation. These approaches assume that using network mounted boot disks incur unacceptable run-time overhead. Our analysis suggest that while this assumption is true for application data, it is incorrect for operating systems and applications, and network mounting the boot disk and applications result in negligible run-time impact while leading to faster provisioning time.This research was supported in part by the MassTech Collaborative Research Matching Grant Program, NSF awards 1347525 and 1414119 and several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or