Scalable Max-Min Fairness in Wireless Ad Hoc Networks

Zhou, Congzhou; Maxemchuk, Nicholas F.

doi:10.1007/978-3-642-11723-7_6

Cited by 4 publications

(8 citation statements)

References 15 publications

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“…A well-known fairness criterion is called max-min fairness (MMF) [15,16], which tries to allocate bandwidth equally. That is, a resource allocation is said to be max-min fair, if it is impossible to increase the bandwidth of any flow A without decreasing the bandwidth of another flow B where B's allocation was less than or equal to A's allocation.…”

Section: Related Workmentioning

confidence: 99%

“…Then it updates the transmission rate according to (16). Meanwhile, the link updates the clique price according to (15).…”

Section: Resource Allocation Algorithmmentioning

confidence: 99%

“…The mobility model is as follows: a node randomly selects a destination within the network limits and then moves towards it at a speed of 4,8,12,16, and 20 m/s. The simulation lasts 150 seconds and the delay limit is set to 5.…”

Section: Comparison Between Hra and Other Algorithmsmentioning

confidence: 99%

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Heterogeneous Resource Allocation Algorithm for Ad Hoc Networks with Utility Fairness

Han

Feng

Chen

2015

International Journal of Distributed Sensor Networks

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Resource allocation is expected to be a most important factor especially for heterogeneous applications in wireless ad hoc networks. In this paper, a novel heterogeneous resource allocation algorithm (HRA) is presented for ad hoc networks, supporting both elastic and inelastic traffic. First, by combining the first order Lagrangian method with pseudo utility, the original nonconvex problem is converted into a new convex one. Then, we successfully solve the heterogeneous problem with the dual-based decomposition approach. In addition, we integrate utility fairness into the resource allocation framework, which can adaptively manage the tradeoff between elastic and inelastic flows. Simulations show and prove that HRA converges fast and can achieve the global optimum starting from many different network conditions, such as elastic, inelastic, and hybrid scenario. With both considerations of flow rate and utility fairness, HRA improves the overall network utility and system throughput greatly.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Then it updates the transmission rate according to (16). Meanwhile, the link updates the clique price according to (15).…”

Section: Resource Allocation Algorithmmentioning

confidence: 99%

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Heterogeneous Resource Allocation Algorithm for Ad Hoc Networks with Utility Fairness

Han

Feng

Chen

2015

International Journal of Distributed Sensor Networks

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“…The prior work focuses only on rate preferences, using techniques such as weighted fair queueing (WFQ) [3] to provide weighted fairness. Such work can be found in several contexts, ranging from multihoming to wireless to wireless mesh networks [1,21].…”

Section: Introductionmentioning

confidence: 99%

Scheduling packets over multiple interfaces while respecting user preferences

Yap

Huang

Yiakoumis

et al. 2013

Proceedings of the Ninth ACM Conference on Emerging Networking Experiments and Technologies

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Now that our smartphones have multiple interfaces (WiFi, 3G, 4G, etc.), we have preferences for which interfaces an application may use. We may prefer to stream video over WiFi because it is fast, but VoIP over 3G because it gives continued connectivity. We also have relative preferences, such as giving Netflix twice as much capacity as Dropbox. This means our mobile devices need to schedule packets in keeping with our preferences while making use of all the capacity available. This is the natural domain of fair queuing, and this paper is about the design of a packet scheduler to meet these requirements. We show that traditional fair queueing schedulers cannot take into account a user's preferences for some interfaces over others. We present a novel packet scheduler called miDRR that meets our needs by generalizing DRR for multiple interfaces. We demonstrate a prototype running in Linux and show that it works correctly and can easily run at the speeds we need.

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“…Through the macro model, flow control algorithms that are designed for wired networks are applied to the wireless networks. In our previous research [10], [11], we show how to use our macro model to achieve max-min fairness [12] in MANETs. However, a centralized intelligence is used to construct our macro model.…”

mentioning

confidence: 99%

Distributed Bottleneck Flow Control in Mobile Ad Hoc Networks

Zhou

Maxemchuk

2011

NPA

Self Cite

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Flow control in mobile ad hoc networks (MANETs) encounters more challenges than flow control in conventional wired networks, such as channel bandwidth variation, medium contention, and frequent re-routing. Without proper flow control, serious network performance degradation in MANETs has been reported in recent studies. In this paper, we introduce a distributed bottleneck flow control technique in MANETs. The proposed scheme uses a distributed flow control mechanism that has been used in both bottleneck flow control and bandwidth balancing in Distributed-Queue-Dual-Bus (DQDB) in wired networks. It extends bandwidth balancing to operate in a mobile wireless environment. Extensive simulations demonstrate that our flow control scheme is effective and can provide max-min fairness as well as improve Quality of Service (QoS) for flows in MANETs.

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Scalable Max-Min Fairness in Wireless Ad Hoc Networks

Cited by 4 publications

References 15 publications

Heterogeneous Resource Allocation Algorithm for Ad Hoc Networks with Utility Fairness

Heterogeneous Resource Allocation Algorithm for Ad Hoc Networks with Utility Fairness

Scheduling packets over multiple interfaces while respecting user preferences

Distributed Bottleneck Flow Control in Mobile Ad Hoc Networks

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