WaveLight: A Monolithic Low Latency Silicon-Photonics Communication Platform for the Next-Generation Disaggregated Cloud Data Centers

2017 ◽  
Author(s):  
Mohammad Shahanshah Akhter ◽  
Paul Somogyi ◽  
Chen Sun ◽  
Mark Wade ◽  
Roy Meade ◽  
...  
Keyword(s):  
Silicon Photonics ◽  
Data Centers ◽  
Low Latency ◽  
Next Generation ◽  
Communication Platform ◽  
Cloud Data ◽  
Cloud Data Centers

Availability analysis of design configurations to compose virtual performance‐optimized data center systems in next‐generation cloud data centers

2020 ◽  
Vol 50 (6) ◽  
pp. 805-826
Author(s):  
Daniel Rosendo ◽  
Demis Gomes ◽  
Guto Leoni Santos ◽  
Leylane Silva ◽  
Andre Moreira ◽  
...  
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Next Generation ◽  
Cloud Data ◽  
Availability Analysis ◽  
Cloud Data Centers

A Case for Elevating the Edge to be a Peer of the Cloud

2021 ◽  
Vol 24 (3) ◽  
pp. 14-19
Author(s):  
Umakishore Ramachandran ◽  
Harshit Gupta ◽  
Adam Hall ◽  
Enrique Saurez ◽  
Zhuangdi Xu
Keyword(s):  
Autonomous Vehicles ◽  
Data Centers ◽  
Response Times ◽  
Current Trend ◽  
Low Latency ◽  
Cloud Data ◽  
Latency Response ◽  
Cloud Data Centers ◽  
New Applications ◽  
The Internet Of Things

Over the last 20 years, mobile computing has evolved to encompass a wide array of increasingly data-rich applications. Many of these applications were enabled by the Cloud computing revolution, which commoditized server hardware to support vast numbers of mobile users from a few large, centralized data centers. Today, mobile's next stage of evolution is spurred by interest in emerging technologies such as Augmented and Virtual Reality (AR/VR), the Internet of Things (IoT), and Autonomous Vehicles. New applications relying on these technologies often require very low latency response times, increased bandwidth consumption, and the need to preserve privacy. Meeting all of these requirements from the Cloud alone is challenging for several reasons. First, the amount of data generated by devices can quickly saturate the bandwidth of backhaul links to the Cloud. Second, achieving low-latency responses for making decisions on sensed data becomes increasingly difficult the further mobile devices are from centralized Cloud data centers. And finally, regulatory or privacy restrictions on the data generated by devices may require that such data be kept locally. For these reasons, enabling next-generation technologies requires us to reconsider the current trend of serving applications from the Cloud alone.

An Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers

2017 ◽  
Vol 26 (1) ◽  
pp. 113-128
Author(s):  
Gamal Eldin I. Selim ◽  
Mohamed A. El-Rashidy ◽  
Nawal A. El-Fishawy
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals QiOi: Performance Isolation for Hyperledger Fabric

2021 ◽  
Vol 11 (9) ◽  
pp. 3870
Author(s):  
Jeongsu Kim ◽  
Kyungwoon Lee ◽  
Gyeongsik Yang ◽  
Kwanhoon Lee ◽  
Jaemin Im ◽  
...  
Keyword(s):  
Data Centers ◽  
Component Level ◽  
Isolation Technique ◽  
Cloud Data ◽  
Cpu Scheduling ◽  
Performance Isolation ◽  
Cloud Data Centers ◽  
A New Technique ◽  
The Impact ◽  
Level Performance

This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristics of the different components in Hyperledger Fabric and show that Fabric components have different impacts on the performance of Fabric. Then, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The key idea of QiOi is to dynamically control the CPU scheduling of Fabric components to cope with the performance interference. We implement QiOi as a user-level daemon and evaluate how QiOi mitigates the performance interference of Fabric. The evaluation results demonstrate that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services. In addition, we show that QiOi can support different ordering services and chaincodes with negligible overhead to Fabric performance.

Adaptive Workload Forecasting in Cloud Data Centers

2019 ◽  
Vol 18 (1) ◽  
pp. 149-168 ◽  
Author(s):  
Eduard Zharikov ◽  
Sergii Telenyk ◽  
Petro Bidyuk
Keyword(s):  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers
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