PAWES: A Flow Distribution Algorithm Based on Priority and Weight Self-Production

Sun, Yi; Ge, Yuming; Yuan, Jing; Zhou, Jihua; Herborn, Stephen; Chen, Dongdong

doi:10.1109/wcnc.2009.4917884

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One can classify the existing literatures into three main categories. The first category of proposals utilize priority-based scheduling by changing the design of fabric or entire network architecture [9], [24], [35], [36]. PDQ [24] assigns higher priority to flows with closer deadline and therefore processes mice flows with high priority.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Path Isolation for Elephant and Mice Flows by Exploiting Path Diversity in Datacenters

Wang

Sun

Salamatian³

et al. 2016

IEEE Trans. Netw. Serv. Manage.

Self Cite

View full text Add to dashboard Cite

International audienceResource competition and conflicts in datacenter networks (DCNs) are frequent and intense. They become inevitable when mixing elephant and mice flows on shared transmission paths, resulting in arbitration between throughput and latency and performance degradation. We propose a novel flow scheduling scheme, Freeway, that leverages on path diversity in the DCN topology to guarantee, simultaneously, mice flow completion within deadline and high network utilization. Freeway adaptively partitions the available paths into low latency and high throughput paths and provides different transmission services for each category. A M/G/1-based model is developed to theoretically obtain the highest value of average delay over the path that will guarantee for 99% of mice flows their completion time before the deadline. Based on this bound, Freeway proposes a dynamic path partitioning algorithm to adjust dynamically with varying traffic load the number of low latency and high throughput paths. While mice flows are transmitted over low latency paths using a simple equal cost multiple path (ECMP) scheduling, Freeway load balances elephant flows on different high-throughput paths. We evaluate Freeway in a series of simulation on a large scale topology and use real traces. Our evaluation results show that Freeway significantly reduces the mice flows completion time within deadlines, while achieving remarkable throughput compared with current schemes. It is remarkable that Freeway does not need any change of DCN switch fabrics or scheduling algorithms and can be deployed easily on any generic datacenter network with switches implementing VLANs and trunking

show abstract

Section: Related Workmentioning

confidence: 99%

Adaptive Path Isolation for Elephant and Mice Flows by Exploiting Path Diversity in Datacenters

Wang

Sun

Salamatian³

et al. 2016

IEEE Trans. Netw. Serv. Manage.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method considers multiple parameters such as the spectrum conditions (channel capacity, data rate, acceptable error rate, delay, jitter), the PU's arrival probability and the QoS requirements of different applications. In order to make the different types of parameters comparable with each other, we first classify and normalize these parameters using the method described in reference [12]. The pseudo-code of the ADSD spectrum decision making method is illustrated in Fig.…”

Section: • Su Transmissionmentioning

confidence: 99%

ADSD: An Automatic Distributed Spectrum Decision method in Cognitive Radio networks

Sun

et al. 2009

2009 First International Conference on Future Information Networks

Self Cite

View full text Add to dashboard Cite

With the development of Cognitive Radio networks, in order to fully utilize the scarce spectrum resources, secondary users are capable of exploiting the available spectrum of primary users. However, when there are several spectrum options available, how to select an appropriate one for the secondary user according to the spectrum quality and the QoS requirements of different kinds of applications is a new challenge. In this paper, we propose a new Automatic Distributed Spectrum Decision (ADSD) method to solve this problem. ADSD considers multiple characterization parameters, in particular, the primary users' arrival probability, to estimate the quality of the available spectrum options. A weight self-generation mechanism is included to automatically determine the weights of different parameters, thus avoiding the difficulty and irrationality when relying on the users to specify the weights directly. In addition, in conjunction with the reconfiguration mechanism, ADSD can reduce the rate of spectrum han doffs by reconfiguring the transmission parameters rather than making a new decision for the existing transmission. Simulation results show that without any users' interference, ADSD can automatically select the appropriate spectrum for transmission and significantly improve the Cognitive Radio network performance in terms of throughput and the spectrum handoff rate.

show abstract

Automatic Flow Distribution and Management in Heterogeneous Networks

Sun

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

PAWES: A Flow Distribution Algorithm Based on Priority and Weight Self-Production

Cited by 3 publications

References 8 publications

Adaptive Path Isolation for Elephant and Mice Flows by Exploiting Path Diversity in Datacenters

Adaptive Path Isolation for Elephant and Mice Flows by Exploiting Path Diversity in Datacenters

ADSD: An Automatic Distributed Spectrum Decision method in Cognitive Radio networks

Automatic Flow Distribution and Management in Heterogeneous Networks

Contact Info

Product

Resources

About