Optimizing Streaming Server Selection for CDN-Delivered Live Streaming

2012 ◽  
pp. 57-70
Author(s):  
Zhenyun Zhuang ◽  
Shun Kwok
Keyword(s):  
Live Streaming ◽  
Streaming Server ◽  
Server Selection ◽  
Selection For

scholarly journals Mobility-Enabled Edge Server Selection for Multi-User Composite Services

2019 ◽  
Vol 11 (9) ◽  
pp. 184
Author(s):  
Wenming Zhang ◽  
Yiwen Zhang ◽  
Qilin Wu ◽  
Kai Peng
Keyword(s):  
Waiting Time ◽  
Edge Computing ◽  
Mobile Users ◽  
Mobile Edge Computing ◽  
Computation Model ◽  
Real World Data ◽  
Server Selection ◽  
Composite Services ◽  
Selection For ◽  
Traditional Approaches

In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user’s mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches.

Multiple channel queues in heavy traffic. III: random server selection

1970 ◽  
Vol 2 (2) ◽  
pp. 370-375 ◽  
Author(s):  
Ward Whitt
Keyword(s):  
Queueing System ◽  
Heavy Traffic ◽  
Multiple Channel ◽  
Server Selection ◽  
Selection For ◽  
Selection Probabilities ◽  
Service Channels

As in [4] and [5], we study service facilities with r arrival channels and s service channels. However, here we assume that customers, immediately upon arrival, randomly select one of the s service channels. Successive customers make this choice independently, choosing server i with probability pi, p1 + · · · + ps = 1. Customers are then served by the servers they select in order of their arrival without defections. The average processing rates as well as the server selection probabilities may vary from server to server, but again we assume the r arrival channels are independent and independent of the service channels. The service channels are not independent, however, because of the random server selection. For simplicity, we only consider a single queueing system; the extension to sequences follows immediately using the argument of [5].

scholarly journals Reliability-based server selection for heterogeneous VANETs

2011 ◽  
Vol 11 (7–9) ◽  
pp. e4 ◽  
Author(s):  
Seyedali Hosseininezhad ◽  
Victor C. M. Leung
Keyword(s):  
Server Selection ◽  
Selection For

scholarly journals Optimal Server Selection for Straggler Mitigation

2020 ◽  
Vol 28 (2) ◽  
pp. 709-721 ◽  
Author(s):  
Ajay Badita ◽  
Parimal Parag ◽  
Vaneet Aggarwal
Keyword(s):  
Server Selection ◽  
Selection For

QoE-Based Server Selection for Content Distribution Networks

2014 ◽  
Vol 63 (11) ◽  
pp. 2803-2815 ◽  
Author(s):  
Hai Anh Tran ◽  
Said Hoceini ◽  
Abdelhamid Mellouk ◽  
Julien Perez ◽  
Sherali Zeadally
Keyword(s):  
Content Distribution ◽  
Distribution Networks ◽  
Content Distribution Networks ◽  
Server Selection ◽  
Selection For
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