27,067 research outputs found
A discrete event simulation for utility accrual scheduling in uniprocessor environment
This research has focused on the proposed and the development of an event based discrete event simulator for the existing General Utility Scheduling (GUS) to facilitate the reuse of the algorithm under a common simulation environment. GUS is one of the existing TUF/UA scheduling algorithms that consider the Time/Utility Function (TUF) of the executed tasks in its scheduling decision in a uniprocessor environment. The scheduling optimality criteria are based on maximizing accrued utility accumulated from execution of all tasks in the system. These criteria are named as Utility Accrual (UA). The TUF/ UA scheduling algorithms are design for adaptive real time system environment. The developed GUS simulator has derived the set of parameter, events, performance metrics and other unique TUF/UA scheduling element according to a detailed analysis of the base model
Utility Optimal Scheduling and Admission Control for Adaptive Video Streaming in Small Cell Networks
We consider the jointly optimal design of a transmission scheduling and
admission control policy for adaptive video streaming over small cell networks.
We formulate the problem as a dynamic network utility maximization and observe
that it naturally decomposes into two subproblems: admission control and
transmission scheduling. The resulting algorithms are simple and suitable for
distributed implementation. The admission control decisions involve each user
choosing the quality of the video chunk asked for download, based on the
network congestion in its neighborhood. This form of admission control is
compatible with the current video streaming technology based on the DASH
protocol over TCP connections. Through simulations, we evaluate the performance
of the proposed algorithm under realistic assumptions for a small-cell network.Comment: 5 pages, 4 figures. Accepted and will be presented at IEEE
International Symposium on Information Theory (ISIT) 201
CSMA using the Bethe Approximation: Scheduling and Utility Maximization
CSMA (Carrier Sense Multiple Access), which resolves contentions over
wireless networks in a fully distributed fashion, has recently gained a lot of
attentions since it has been proved that appropriate control of CSMA parameters
guarantees optimality in terms of stability (i.e., scheduling) and system- wide
utility (i.e., scheduling and congestion control). Most CSMA-based algorithms
rely on the popular MCMC (Markov Chain Monte Carlo) technique, which enables
one to find optimal CSMA parameters through iterative loops of
`simulation-and-update'. However, such a simulation-based approach often
becomes a major cause of exponentially slow convergence, being poorly adaptive
to flow/topology changes. In this paper, we develop distributed iterative
algorithms which produce approximate solutions with convergence in polynomial
time for both stability and utility maximization problems. In particular, for
the stability problem, the proposed distributed algorithm requires, somewhat
surprisingly, only one iteration among links. Our approach is motivated by the
Bethe approximation (introduced by Yedidia, Freeman and Weiss in 2005) allowing
us to express approximate solutions via a certain non-linear system with
polynomial size. Our polynomial convergence guarantee comes from directly
solving the non-linear system in a distributed manner, rather than multiple
simulation-and-update loops in existing algorithms. We provide numerical
results to show that the algorithm produces highly accurate solutions and
converges much faster than the prior ones
Structure-Aware Stochastic Control for Transmission Scheduling
In this paper, we consider the problem of real-time transmission scheduling
over time-varying channels. We first formulate the transmission scheduling
problem as a Markov decision process (MDP) and systematically unravel the
structural properties (e.g. concavity in the state-value function and
monotonicity in the optimal scheduling policy) exhibited by the optimal
solutions. We then propose an online learning algorithm which preserves these
structural properties and achieves -optimal solutions for an arbitrarily small
. The advantages of the proposed online method are that: (i) it does not
require a priori knowledge of the traffic arrival and channel statistics and
(ii) it adaptively approximates the state-value functions using piece-wise
linear functions and has low storage and computation complexity. We also extend
the proposed low-complexity online learning solution to the prioritized data
transmission. The simulation results demonstrate that the proposed method
achieves significantly better utility (or delay)-energy trade-offs when
comparing to existing state-of-art online optimization methods.Comment: 41page
A Survey on QoE-oriented Wireless Resources Scheduling
Future wireless systems are expected to provide a wide range of services to
more and more users. Advanced scheduling strategies thus arise not only to
perform efficient radio resource management, but also to provide fairness among
the users. On the other hand, the users' perceived quality, i.e., Quality of
Experience (QoE), is becoming one of the main drivers within the schedulers
design. In this context, this paper starts by providing a comprehension of what
is QoE and an overview of the evolution of wireless scheduling techniques.
Afterwards, a survey on the most recent QoE-based scheduling strategies for
wireless systems is presented, highlighting the application/service of the
different approaches reported in the literature, as well as the parameters that
were taken into account for QoE optimization. Therefore, this paper aims at
helping readers interested in learning the basic concepts of QoE-oriented
wireless resources scheduling, as well as getting in touch with its current
research frontier.Comment: Revised version: updated according to the most recent related
literature; added references; corrected typo
Robust Wireless Body Area Networks Coexistence: A Game Theoretic Approach to Time-Division MAC
The enabling of wireless body area networks (WBANs) coexistence by radio
interference mitigation is very important due to a rapid growth in potential
users, and a lack of a central coordinator among WBANs that are closely
located. In this paper, we propose a TDMA based MAC layer Scheme, with a
back-off mechanism that reduces packet collision probability; and estimate
performance using a Markov chain model. Based on the MAC layer scheme, a novel
non-cooperative game is proposed to jointly adjust sensor node's transmit power
and rate. In comparison with the state-of-art, simulation that includes
empirical data shows that the proposed approach leads to higher throughput and
longer node lifespan as WBAN wearers dynamically move into each other's
vicinity. Moreover, by adaptively tuning contention windows size an alternative
game is developed, which significantly reduces the latency. Both proposed games
provide robust transmission under strong inter-WBAN interferences, but are
demonstrated to be applicable to different scenarios. The uniqueness and
existence of Nash Equilibrium (NE), as well as close-to-optimum social
efficiency, is also proven for both games.Comment: 31 pages, 17 figures, submitted for possible publication on ACM
Transactions on Sensor Networks (TOSN
Zygarde: Time-Sensitive On-Device Deep Inference and Adaptation on Intermittently-Powered Systems
We propose Zygarde -- which is an energy -- and accuracy-aware soft real-time
task scheduling framework for batteryless systems that flexibly execute deep
learning tasks1 that are suitable for running on microcontrollers. The sporadic
nature of harvested energy, resource constraints of the embedded platform, and
the computational demand of deep neural networks (DNNs) pose a unique and
challenging real-time scheduling problem for which no solutions have been
proposed in the literature. We empirically study the problem and model the
energy harvesting pattern as well as the trade-off between the accuracy and
execution of a DNN. We develop an imprecise computing-based scheduling
algorithm that improves the timeliness of DNN tasks on intermittently powered
systems. We evaluate Zygarde using four standard datasets as well as by
deploying it in six real-life applications involving audio and camera sensor
systems. Results show that Zygarde decreases the execution time by up to 26%
and schedules 9%-34% more tasks with up to 21% higher inference accuracy,
compared to traditional schedulers such as the earliest deadline first (EDF).Comment: Accepted in Proceedings of the ACM on Interactive, Mobile, Wearable
and Ubiquitous Technologies, September 2020, Vol 4, No
Adaptive Video Streaming in MU-MIMO Networks
We consider extensions and improvements on our previous work on dynamic
adaptive video streaming in a multi-cell multiuser ``small cell'' wireless
network. Previously, we treated the case of single-antenna base stations and,
starting from a network utility maximization (NUM) formulation, we devised a
``push'' scheduling policy, where users place requests to sequential video
chunks to possibly different base stations with adaptive video quality, and
base stations schedule their downlink transmissions in order to stabilize their
transmission queues. In this paper we consider a ``pull'' strategy, where every
user maintains a request queue, such that users keep track of the video chunks
that are effectively delivered. The pull scheme allows to download the chunks
in the playback order without skipping or missing them. In addition, motivated
by the recent/forthcoming progress in small cell networks (e.g., in wave-2 of
the recent IEEE 802.11ac standard), we extend our dynamic streaming approach to
the case of base stations capable of multiuser MIMO downlink, i.e., serving
multiple users on the same time-frequency slot by spatial multiplexing. By
exploiting the ``channel hardening'' effect of high dimensional MIMO channels,
we devise a low complexity user selection scheme to solve the underlying
max-weighted rate scheduling, which can be easily implemented and runs
independently at each base station. Through simulations, we show MIMO gains in
terms of video streaming QoE metrics like the pre-buffering and re-buffering
times.Comment: submitted to IEEE Intl. Symposium on Information Theory 201
TCP Reno over Adaptive CSMA
An interesting distributed adaptive CSMA MAC protocol, called adaptive CSMA,
was proposed recently to schedule any strictly feasible achievable rates inside
the capacity region. Of particular interest is the fact that the adaptive CSMA
can achieve a system utility arbitrarily close to that is achievable under a
central scheduler. However, a specially designed transport-layer rate
controller is needed for this result. An outstanding question is whether the
widely-installed TCP Reno is compatible with adaptive CSMA and can achieve the
same result. The answer to this question will determine how close to practical
deployment adaptive CSMA is. Our answer is yes and no. First, we observe that
running TCP Reno directly over adaptive CSMA results in severe starvation
problems. Effectively, its performance is no better than that of TCP Reno over
legacy CSMA (IEEE 802.11), and the potentials of adaptive CSMA cannot be
realized. Fortunately, we find that multi-connection TCP Reno over adaptive
CSMA with active queue management can materialize the advantages of adaptive
CSMA. NS-2 simulations demonstrate that our solution can alleviate starvation
and achieve fair and efficient rate allocation. Multi-connection TCP can be
implemented at either application or transport layer. Application-layer
implementation requires no kernel modification, making the solution readily
deployable in networks running adaptive CSMA
WiFlix: Adaptive Video Streaming in Massive MU-MIMO Wireless Networks
We consider the problem of simultaneous on-demand streaming of stored video
to multiple users in a multi-cell wireless network where multiple unicast
streaming sessions are run in parallel and share the same frequency band. Each
streaming session is formed by the sequential transmission of video "chunks,"
such that each chunk arrives into the corresponding user playback buffer within
its playback deadline. We formulate the problem as a Network Utility
Maximization (NUM) where the objective is to fairly maximize users' video
streaming Quality of Experience (QoE) and then derive an iterative control
policy using Lyapunov Optimization, which solves the NUM problem up to any
level of accuracy and yields an online protocol with control actions at every
iteration decomposing into two layers interconnected by the users' request
queues : i) a video streaming adaptation layer reminiscent of DASH, implemented
at each user node; ii) a transmission scheduling layer where a max-weight
scheduler is implemented at each base station. The proposed chunk request
scheme is a pull strategy where every user opportunistically requests video
chunks from the neighboring base stations and dynamically adapts the quality of
its requests based on the current size of the request queue. For the
transmission scheduling component, we first describe the general max-weight
scheduler and then particularize it to a wireless network where the base
stations have multiuser MIMO (MU-MIMO) beamforming capabilities. We exploit the
channel hardening effect of large-dimensional MIMO channels (massive MIMO) and
devise a low complexity user selection scheme to solve the underlying
combinatorial problem of selecting user subsets for downlink beamforming, which
can be easily implemented and run independently at each base station.Comment: 30 pages. arXiv admin note: text overlap with arXiv:1304.808
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