Conference Paper

# Utility-optimal scheduling in time-varying wireless networks with delay constraints

DOI: 10.1145/1860093.1860099 Conference: Proceedings of the 11th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2010, Chicago, IL, USA, September 20-24, 2010
Source: DBLP

ABSTRACT Clients in wireless networks may have per-packet delay constraints on their traffic. Further, in contrast to wireline networks, the wireless medium is subject to fading. In such a time-varying environment, we consider the system problem of maximizing the total utility of clients, where the utilities are determined by their long-term average rates of being served within their delay constraints. We also allow for the additional fairness requirement that each client may require a certain minimum service rate. This overall model can be applied to a wide range of applications, including delay-constrained networks, mobile cellular networks, and dynamic spectrum allocation. We address this problem through convex programming. We propose an on-line scheduling policy and prove that it is utility-optimal. Surprisingly, this policy does not need to know the probability distribution of system states. We also design an auction mechanism where clients are scheduled and charged according to their bids. We prove that the auction mechanism restricts any selfish client from improving its utility by faking its utility function. We also show that the auction mechanism schedules clients in the same way as that done by the on-line scheduling policy. Thus, the auction mechanism is both truthful and utility-optimal. Finally, we design specific algorithms that implement the auction mechanism for a variety of applications.

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• "For example, [11] makes a non-causal assumption that the scheduler knows the channel states in the future, which is unrealistic in practice. [12] requires that the arrivals and deadlines follow a periodic structure. For more general systems with causal multi-state channels and without a periodic structure , however, we are not aware of a tractable methodology to find optimal scheduling policies subject to deadline constraints. "
##### Article: Application-Level Scheduling With Probabilistic Deadline Constraints
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IEEE/ACM Transactions on Networking 06/2016; 24(3). DOI:10.1109/TNET.2015.2416256 · 1.81 Impact Factor
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• "Existing works on stochastic system control either focus on systems with perfect a-prior information, e.g., [9], [10], or rely on stochastic approximation techniques that do not require such information, e.g., [11], [12]. While the proposed solutions are effective, they do not capture how information affects algorithm design and performance, and do not provide interfaces for integrating the fast-developing " data science " tools, e.g., data collecting methods and machine learning algorithms, [13], [14], into system control. "
##### Article: The Value-of-Information in Matching with Queues
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ABSTRACT: We consider the problem of \emph{optimal matching with queues} in dynamic systems and investigate the value-of-information. In such systems, the operators match tasks and resources stored in queues, with the objective of maximizing the system utility of the matching reward profile, minus the average matching cost. This problem appears in many practical systems and the main challenges are the no-underflow constraints, and the lack of matching-reward information and system dynamics statistics. We develop two online matching algorithms: Learning-aided Reward optimAl Matching ($\mathtt{LRAM}$) and Dual-$\mathtt{LRAM}$ ($\mathtt{DRAM}$) to effectively resolve both challenges. Both algorithms are equipped with a learning module for estimating the matching-reward information, while $\mathtt{DRAM}$ incorporates an additional module for learning the system dynamics. We show that both algorithms achieve an $O(\epsilon+\delta_r)$ close-to-optimal utility performance for any $\epsilon>0$, while $\mathtt{DRAM}$ achieves a faster convergence speed and a better delay compared to $\mathtt{LRAM}$, i.e., $O(\delta_{z}/\epsilon + \log(1/\epsilon)^2))$ delay and $O(\delta_z/\epsilon)$ convergence under $\mathtt{DRAM}$ compared to $O(1/\epsilon)$ delay and convergence under $\mathtt{LRAM}$ ($\delta_r$ and $\delta_z$ are maximum estimation errors for reward and system dynamics). Our results reveal that information of different system components can play very different roles in algorithm performance and provide a systematic way for designing joint learning-control algorithms for dynamic systems.
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• "References such as [9] and [10] study utility maximization strategies for the network. There are a number of interesting references focusing on QoS provisioning based on current implementations and standards . "
##### Article: A Framework for Quality of Service with a Multiple Access Strategy
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ABSTRACT: We study a problem of scheduling real-time traffic with hard delay constraints in an unreliable wireless channel. Packets arrive at a constant rate to the network and have to be delivered within a fixed number of slots in a fading wireless channel. For an infrastructure mode of traffic with a centralized scheduler, we are interested in the long time average throughput achievable for the real time traffic. In [1], the authors have stud- ied the feasible throughput vectors by identifying the necessary and sufficient conditions using work load characterization. In our work, we provide a characterization of the feasible throughput vectors using the notion of the rate region. We then discuss an extension to the network model studied in [1] by allowing multiple access during contention and propose an enhancement to the rate region of the wireless network. We characterize the feasible throughput vectors with the multiple access technique and study throughput optimal and utility maximizing strategies for the network scenario. Using simulations, we evaluate the performance of the proposed strategy and discuss its advantages.
IEEE Wireless Communication Letters 01/2013; 3(2). DOI:10.1109/WCL.2014.012914.130835