scholarly journals Emulating Software Defined Network Using Mininet and OpenDaylight Controller Hosted on Amazon Web Services Cloud Platform to Demonstrate a Realistic Programmable Network

Author(s):  
Lindinkosi L. Zulu ◽  
Kingsley A. Ogudo ◽  
Patrice O. Umenne
2020 ◽  
Vol 10 (3) ◽  
pp. 1-16
Author(s):  
Sanjay P. Ahuja ◽  
Emily Czarnecki ◽  
Sean Willison

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.


2020 ◽  
Vol 27 (9) ◽  
pp. 1425-1430
Author(s):  
Inès Krissaane ◽  
Carlos De Niz ◽  
Alba Gutiérrez-Sacristán ◽  
Gabor Korodi ◽  
Nneka Ede ◽  
...  

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?


2019 ◽  
Vol 7 (1) ◽  
pp. 1-6
Author(s):  
Anne-Laure Mention ◽  
João José Pinto Ferreira ◽  
Marko Torkkeli

‘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.  (...)


2019 ◽  
Vol 214 ◽  
pp. 07016 ◽  
Author(s):  
Tian Yan ◽  
Shan Zeng ◽  
Mengyao Qi ◽  
Qingbao Hu ◽  
Fazhi Qi

To improve hardware utilization and save manpower in system maintenance, most of the web services in IHEP have been migrated to a private cloud build upon OpenStack. However, cyber security attacks becomes a serious threats to the cloud progressively. Therefore, a cyber security detection and monitoring system is deployed for this cloud platform. This system collects various security related logs as data sources, and processes them in a framework composed of open source data store, analysis and visualization tools. With this system, security incidents and events can be handled in time and rapid response can be taken to protect cloud platform against cyber security threats.


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