Throughput optimization of TCP incast congestion control in large-scale datacenter networks

2017 ◽  
Vol 124 ◽  
pp. 46-60 ◽  
Author(s):  
Lei Xu ◽  
Ke Xu ◽  
Yong Jiang ◽  
Fengyuan Ren ◽  
Haiyang Wang
Keyword(s):  
Congestion Control ◽  
Large Scale ◽  
Throughput Optimization ◽  
Datacenter Networks

scholarly journals On the Throughput Optimization in Large-Scale Batch-Processing Systems

2021 ◽  
Vol 48 (3) ◽  
pp. 128-129
Author(s):  
Sounak Kar ◽  
Robin Rehrmann ◽  
Arpan Mukhopadhyay ◽  
Bastian Alt ◽  
Florin Ciucu ◽  
...  
Keyword(s):  
Large Scale ◽  
Mean Field ◽  
Queueing Network ◽  
Processing System ◽  
Batch Size ◽  
System Throughput ◽  
Throughput Optimization ◽  
Mean Field Model ◽  
Optimal Batch Size ◽  
Globally Attractive

We analyze a data-processing system with n clients producing jobs which are processed in batches by m parallel servers; the system throughput critically depends on the batch size and a corresponding sub-additive speedup function that arises due to overhead amortization. In practice, throughput optimization relies on numerical searches for the optimal batch size which is computationally cumbersome. In this paper, we model this system in terms of a closed queueing network assuming certain forms of service speedup; a standard Markovian analysis yields the optimal throughput in w n4 time. Our main contribution is a mean-field model that has a unique, globally attractive stationary point, derivable in closed form. This point characterizes the asymptotic throughput as a function of the batch size that can be calculated in O(1) time. Numerical settings from a large commercial system reveal that this asymptotic optimum is accurate in practical finite regimes.

scholarly journals Throughput optimization strategies for large-scale wireless LANs

2015 ◽  
Author(s):  
Mostafa Pakparvar ◽  
David Plets ◽  
Jeroen Hoebeke ◽  
Dirk Deschrijver ◽  
Michael Mehari ◽  
...  
Keyword(s):  
Large Scale ◽  
Wireless Lans ◽  
Throughput Optimization

End-to-End Dataflow Parallelism for Transfer Throughput Optimization

2011 ◽  
pp. 23-39
Author(s):  
Esma Yildirim ◽  
Tevfik Kosar
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Data Transfer ◽  
Throughput Optimization ◽  
High Speed Networks ◽  
Parallel Storage ◽  
End To End ◽  
Processor Speed ◽  
Networking Technology

The emerging petascale increase in the data produced by large-scale scientific applications necessitates innovative solutions for efficient transfer of data through the advanced infrastructure provided by today’s high-speed networks and complex computer-architectures (e.g. supercomputers, parallel storage systems). Although the current optical networking technology reached transport speeds of 100Gbps, the applications still suffer from the inadequate transport protocols and end-system bottlenecks such as processor speed, disk I/O speed and network interface card limits that cause underutilization of the existing network infrastructure and let the application achieve only a small portion of the theoretical performance. Fortunately, with the parallelism provided by usage of multiple CPUs/nodes and multiple disks present in today’s systems, these bottlenecks could be eliminated. However it is necessary to understand the characteristics of the end-systems and the transport protocol used. In this book chapter, we analyze methodologies that will improve the data transfer speed of applications and provide maximal speeds that could be obtained from the available end-system resources and high-speed networks through usage of end-to-end dataflow parallelism.

scholarly journals Fine-grained load balancing with traffic-aware rerouting in datacenter networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Tao Zhang ◽  
Yasi Lei ◽  
Qianqiang Zhang ◽  
Shaojun Zou ◽  
Juan Huang ◽  
...  
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Queuing Delay ◽  
Packet Reordering ◽  
Fine Grained ◽  
Network Bandwidth ◽  
Performance Requirements ◽  
Delay Sensitive ◽  
Link Utilization ◽  
Datacenter Networks

AbstractModern datacenters provide a wide variety of application services, which generate a mix of delay-sensitive short flows and throughput-oriented long flows, transmitting in the multi-path datacenter network. Though the existing load balancing designs successfully make full use of available parallel paths and attain high bisection network bandwidth, they reroute flows regardless of their dissimilar performance requirements. The short flows suffer from the problems of large queuing delay and packet reordering, while the long flows fail to obtain high throughput due to low link utilization and packet reordering. To address these inefficiency, we design a fine-grained load balancing scheme, namely TR (Traffic-aware Rerouting), which identifies flow types and executes flexible and traffic-aware rerouting to balance the performances of both short and long flows. Besides, to avoid packet reordering, TR leverages the reverse ACKs to estimate the switch-to-switch delay, thus excluding paths that potentially cause packet reordering. Moreover, TR is only deployed on the switch without any modification on end-hosts. The experimental results of large-scale NS2 simulations show that TR reduces the average and tail flow completion time for short flows by up to 60% and 80%, as well as provides up to 3.02x gain in throughput of long flows compared to the state-of-the-art load balancing schemes.

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