Touchdown on the Cloud: The Impact of the Super Bowl on Cloud

Wang, Chen; Kim, Hyong

doi:10.1109/bigdataservice.2019.00021

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Nowadays, Internet technology is turning to real-time applications that require high bit rates and strict delay for better quality of service (QoS) provision [1]. Real-time applications such as Voice over Internet Protocol (VoIP), video conferencing, online gaming [2,3], online transactions [4], and virtual online classroom [5] become hot research areas for real-time applications due to the rapid growth of user interest in audio and video and the availability of integrated systems that can deliver multimedia data at a lower cost [6,7]. Tese benefts have been achieved through the establishment of many multimedia institutions such as Google, Akamai, Level 3, Limelight, and Kankan [6,8].…”

Section: Introductionmentioning

confidence: 99%

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Wassie Geremew,

Ding

2023

Journal of Computer Networks and Communications

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Currently, the widespread of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspection (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real-time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay, and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the abovementioned methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmission requirements to many Internet users using SDN capability and the potential of deep learning. Specifically, DNN, CNN, LSTM, and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that do not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13. Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.

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Section: Introductionmentioning

confidence: 99%

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Wassie Geremew,

Ding

2023

Journal of Computer Networks and Communications

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Touchdown on the Cloud: The Impact of the Super Bowl on Cloud

Wang¹,

Kim

2019

2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)

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The Super Bowl is the world's biggest televised sporting event. We examine the impact of the increasing online activities during the Super Bowl of year 2015-2017 on various types of Cloud systems, including the Cloud infrastructure service, the Cloud content delivery networks for live streaming, and popular web services such as on-demand video streaming service and social network applications. We probe these systems from agents deployed around the world to compare their load variations and performance changes during and after the game. Through our studies of three consecutive years, we find that Super Bowl events have impacts on the Cloud. However, the current Cloud system is still able to ensure capacity to cope with the challenges brought by traffic changes during such massive events.

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Pricing (and Bidding) Strategies for Delay Differentiated Cloud Services

Song

Guérin

2020

ACM Trans. Econ. Comput.

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We consider a cloud provider that seeks to maximize its revenue by offering services with different trade-offs between cost and timeliness of job completion. Spot instances and preemptible instances are examples of such services, with, in both cases, possible service interruptions delaying a job’s completion. Our focus is on exploiting heterogeneity across jobs in terms of value and sensitivity to execution delay, with a joint distribution that determines their relationship across the user population. We characterize optimal (revenue maximizing) pricing strategies and, in the case of spot instances, optimal bidding strategies as well as identify conditions under which bidding at a fixed price is optimal. We show that correlation between delay sensitivity and job value needs to exceed a certain threshold for a service offering that differentiates based on speed of execution to be beneficial to the provider. We further assess the results’ robustness under more general assumptions, and we offer guidelines for users and providers.

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Touchdown on the Cloud: The Impact of the Super Bowl on Cloud

Cited by 3 publications

References 11 publications

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Touchdown on the Cloud: The Impact of the Super Bowl on Cloud

Pricing (and Bidding) Strategies for Delay Differentiated Cloud Services

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