scholarly journals Moonshot innovations: Wishful Thinking or Business-As-Usual?

2019 ◽  
Vol 7 (1) ◽  
pp. 1-6
Author(s):  
Anne-Laure Mention ◽  
João José Pinto Ferreira ◽  
Marko Torkkeli
Keyword(s):  
Web Services ◽  
End Users ◽  
Wishful Thinking ◽  
Cloud Platform ◽  
Wall Street ◽  
Value Capture ◽  
Amazon Web Services ◽  
Mind Set ◽  
Business As Usual ◽  
Sizeable Increase

‘Our mind-set will be to avoid the moonshot’ said Boeing CEO James McNerney at a Wall Street analysts meeting in Seattle nearly 5 years ago (see Gates, 2014). The ambitious, exploratory and risky endeavour dubbed as moonshot project of the Boeing 787 Dreamliner had sunk billions of dollars in an industry where end-users demanded more comfort and convenience for less cost. According to McNerney, moonshots do not work in a price-sensitive environment. It is argued that they also tend to take the focus away from more immediate value capture opportunities as seen through Google’s loss on its core Cloud Platform to Amazon Web Services (AWS). Google’s parent company Alphabet which oversees Google X (a semi-secret moonshot project lab) more recently reported that it had incurred a US$1.3billion in operating loss on moonshot projects with a sizeable increase in compensation of employees and executives working on these projects (Alphabet, 2018). Notably, none of the Google X lab spin-outs (e.g. Loon – a balloon-based internet project, Waymo – self-driving car project, Wing – drone delivery project) have been identified as commercially viable. Despite the uncertainties and failures, the focus on moonshot innovations continues to proliferate in academia (Kaur, Kaur and Singh, 2016; Strong and Lynch, 2018) and practice (Martinez, 2018). Yourden (1997) even wrote an interesting book on perseverance and tenacity to keep going even after failed projects. Proponents of moonshot thinking have claimed that it can help solve society’s biggest challenges (e.g. cure cancer, see Kovarik, 2018) with some suggesting to encourage such thinking by paying failure bonuses (Figueroa, 2018). Yet others remain sceptical, positing that moonshot is ‘awesome and pointless’ (Haigh, 2019, p.4). A proverbial question, thus, emerges: are moonshot innovations simply wishful thinking or can they be part of business-as-usual? In part, the answer may be two-fold – 1) understanding the value of moonshot thinking, and 2) understanding moonshot challenges.  (...)

Multi-Factor Performance Comparison of Amazon Web Services Elastic Compute Cluster and Google Cloud Platform Compute Engine

2020 ◽  
Vol 10 (3) ◽  
pp. 1-16
Author(s):  
Sanjay P. Ahuja ◽  
Emily Czarnecki ◽  
Sean Willison
Keyword(s):  
Web Services ◽  
Cluster Computing ◽  
Performance Metrics ◽  
Performance Comparison ◽  
Public Cloud ◽  
Cloud Platform ◽  
Amazon Web Services ◽  
Computing Performance ◽  
Comparative Examination ◽  
Insight Into

Cloud computing has rapidly become a viable competitor to on-premise infrastructure from both management and cost perspectives. This research provides insight into cluster computing performance and variability in cloud-provisioned infrastructure from two popular public cloud providers. A comparative examination of the two cloud platforms using synthetic benchmarks is provided. In this article, we compared the performance of Amazon Web Services Elastic Compute Cluster (EC2) to the Google Cloud Platform (GCP) Compute Engine using three benchmarks: STREAM, IOR, and NPB-EP. Experiments were conducted on clusters with increasing nodes from one to eight. We also performed experiments over the course of two weeks where benchmarks were run at similar times. The benchmarks provided performance metrics for bandwidth (STREAM), read and write performance (IOR), and operations per second (NPB-EP). We found that EC2 outperformed GCP for bandwidth. Both provided good scalability and reliability for bandwidth with GCP showing a slight deviation during the two-week trial. GCP outperformed EC2 in both the read and write tests (IOR) as well as the operations per second test. However, GCP was extremely variable during the read and write tests over the two-week trial. Overall, each platform excelled in different benchmarks and we found EC2 to be more reliable in general.

scholarly journals Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services

2020 ◽  
Vol 27 (9) ◽  
pp. 1425-1430
Author(s):  
Inès Krissaane ◽  
Carlos De Niz ◽  
Alba Gutiérrez-Sacristán ◽  
Gabor Korodi ◽  
Nneka Ede ◽  
...  
Keyword(s):  
Web Services ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Cloud Platform ◽  
Human Genomics ◽  
Genome Wide ◽  
Innovative Methodology ◽  
Amazon Web Services

Abstract Objective Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies. Methods We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset. Results Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics. Conclusions We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?

Does Congestion Always Hurt? Managing Discount Under Congestion in a Game-Theoretic Setting

2021 ◽  
Author(s):  
Rajib L. Saha ◽  
Sumanta Singha ◽  
Subodha Kumar
Keyword(s):  
Web Services ◽  
Lower Cost ◽  
End Users ◽  
Cloud Services ◽  
Key Factors ◽  
Amazon Web Services ◽  
Game Theoretic ◽  
Potential Loss ◽  
The Cost

Many firms buy cloud services from cloud vendors, such as Amazon Web Services to serve end users. One of the key factors that affect the quality of cloud services is congestion. Congestion leads to a potential loss of end users, resulting in lower demand for cloud services. Although discount can stimulate demand, its effect under congestion is ambiguous; a higher discount leads to higher demand, but it can further lead to higher congestion, thereby lowering demand. We explore how congestion moderates both cloud vendor pricing and the buyer’s fulfillment decisions. We seek to answer how the congestion sensitivity of the end users and the cost of technology impact buyer profitability and the cloud vendor’s choice of discount. We also examine how the cost of technology determines the buyer’s willingness to pass on savings to end users. Our results show that the buyer is not necessarily worse off even when the end users are more intolerant to congestion. In fact, when end users are more congestion sensitive, the demand for cloud services can sometimes increase, and the discount offered by the vendor can decrease. We also observe that a lower cost of technology can sometimes hurt the buyer, and the buyer can pass on lower benefits to end users.

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