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

Conference Paper · January 2010with20 Reads
DOI: 10.1145/1860093.1860099 · Source: DBLP
Conference: Proceedings of the 11th ACM Interational Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2010, Chicago, IL, USA, September 20-24, 2010
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.
• ##### Scheduling Flows with Multiple Service Frequency Constraints
• "However, none of the aforementioned works deal with the QoS requirements of users. Some scheduling policies are designed to meet the packet delay constraint for each user [20][21][3]. They all partition time into frames, and packets will run out of time before the end of their arriving frames. "
[Show abstract] [Hide abstract] ABSTRACT: With the fast development of wireless technologies, wireless applications have invaded various areas in people's lives with a wide range of capabilities. Guaranteeing Quality-of-Service (QoS) is the key to the success of those applications. One of the QoS requirements, service frequency, is very important for tasks including multimedia transmission in the Internet of Things. A service frequency constraint denotes the length of time period during which a link can transmit at least once. Unfortunately, it has not been well addressed yet. Therefore, this paper proposes a new framework to schedule multi transmitting flows in wireless networks considering service frequency constraint for each link. In our model, the constraints for flows are heterogeneous due to the diversity of users' behaviors. We first introduce a new definition for network stability with service frequency constraints and demonstrate that the novel scheduling policy is throughput-optimal in one fundamental category of network models. After that, we discuss the performance of a wireless network with service frequency constraints from the views of capacity region and total queue length. Finally, a series of evaluations indicate the proposed scheduling policy can guarantee service frequency and achieve a good performance on the aspect of queue length of each flow.
Article · Jun 2016
• ##### Application-Level Scheduling With Probabilistic Deadline Constraints
• "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. "
Full-text · Article · Jun 2016
• ##### System Intelligence: Model, Bounds and Algorithms
• "[12] and [13] develop algorithms for achieving the optimal utility-delay tradeoff in multihop networks. [14] studies the problem of scheduling delay-constrained flows over wireless systems. However, all these works focus only on causal systems, i.e., service begins only after demand enters the system. "
[Show abstract] [Hide abstract] ABSTRACT: We present a general framework for understanding system intelligence, i.e., the level of system smartness perceived by users, and propose a novel metric for measuring intelligence levels of dynamical human-in-the-loop systems, defined to be the maximum average reward obtained by proactively serving user demands, subject to a resource constraint. Our metric captures two important elements of smartness, i.e., being able to know what users want and pre-serve them, and achieving good resource management while doing so. We provide an explicit characterization of the system intelligence, and show that it is jointly determined by user demand volume (opportunity to impress), demand correlation (user predictability), and system resource and action costs (flexibility to pre-serve). We then propose an online learning-aided control algorithm called Learning-aided Budget-limited Intelligent System Control (\mtt{LBISC}). We show that \lbisc{} achieves an intelligence level that is within $O(N(T)^{-\frac{1}{2}}+\epsilon)$ of the highest level, where $N(T)$ represents the number of data samples collected within a learning period $T$ and is proportional to the user population size in the system, while guaranteeing an $O(\max( N(T)^{-\frac{1}{2}}/\epsilon, \log(1/\epsilon)^2))$ average resource deficit. Moreover, we show that \lbisc{} possesses an $O(\max( N(T)^{-\frac{1}{2}}/\epsilon$, $\log(1/\epsilon)^2)+T)$ convergence time, which is much smaller compared to the $\Theta(1/\epsilon)$ time required for non-learning based algorithms. The analysis of \lbisc{} rigorously quantifies the impacts of data and user population (captured by $N(T)$), learning (captured by our learning method), and control (captured by \lbisc) on achievable system intelligence, and provides novel insight and guideline into designing future smart systems.
Conference Paper · May 2016 · IEEE/ACM Transactions on Networking