4,136 research outputs found
Timely-Throughput Optimal Scheduling with Prediction
Motivated by the increasing importance of providing delay-guaranteed services
in general computing and communication systems, and the recent wide adoption of
learning and prediction in network control, in this work, we consider a general
stochastic single-server multi-user system and investigate the fundamental
benefit of predictive scheduling in improving timely-throughput, being the rate
of packets that are delivered to destinations before their deadlines. By
adopting an error rate-based prediction model, we first derive a Markov
decision process (MDP) solution to optimize the timely-throughput objective
subject to an average resource consumption constraint. Based on a packet-level
decomposition of the MDP, we explicitly characterize the optimal scheduling
policy and rigorously quantify the timely-throughput improvement due to
predictive-service, which scales as
,
where are constants, is the
true-positive rate in prediction, is the false-negative rate, is the
packet deadline and is the prediction window size. We also conduct
extensive simulations to validate our theoretical findings. Our results provide
novel insights into how prediction and system parameters impact performance and
provide useful guidelines for designing predictive low-latency control
algorithms.Comment: 14 pages, 7 figure
When Backpressure Meets Predictive Scheduling
Motivated by the increasing popularity of learning and predicting human user
behavior in communication and computing systems, in this paper, we investigate
the fundamental benefit of predictive scheduling, i.e., predicting and
pre-serving arrivals, in controlled queueing systems. Based on a lookahead
window prediction model, we first establish a novel equivalence between the
predictive queueing system with a \emph{fully-efficient} scheduling scheme and
an equivalent queueing system without prediction. This connection allows us to
analytically demonstrate that predictive scheduling necessarily improves system
delay performance and can drive it to zero with increasing prediction power. We
then propose the \textsf{Predictive Backpressure (PBP)} algorithm for achieving
optimal utility performance in such predictive systems. \textsf{PBP}
efficiently incorporates prediction into stochastic system control and avoids
the great complication due to the exponential state space growth in the
prediction window size. We show that \textsf{PBP} can achieve a utility
performance that is within of the optimal, for any ,
while guaranteeing that the system delay distribution is a
\emph{shifted-to-the-left} version of that under the original Backpressure
algorithm. Hence, the average packet delay under \textsf{PBP} is strictly
better than that under Backpressure, and vanishes with increasing prediction
window size. This implies that the resulting utility-delay tradeoff with
predictive scheduling beats the known optimal tradeoff for systems without prediction
On the Delay of Geographical Caching Methods in Two-Tiered Heterogeneous Networks
We consider a hierarchical network that consists of mobile users, a
two-tiered cellular network (namely small cells and macro cells) and central
routers, each of which follows a Poisson point process (PPP). In this scenario,
small cells with limited-capacity backhaul are able to cache content under a
given set of randomized caching policies and storage constraints. Moreover, we
consider three different content popularity models, namely fixed content
popularity, distance-dependent and load-dependent, in order to model the
spatio-temporal behavior of users' content request patterns. We derive
expressions for the average delay of users assuming perfect knowledge of
content popularity distributions and randomized caching policies. Although the
trend of the average delay for all three content popularity models is
essentially identical, our results show that the overall performance of
cached-enabled heterogeneous networks can be substantially improved, especially
under the load-dependent content popularity model.Comment: to be presented at IEEE SPAWC'2016, Edinburgh, U
Predictive and core-network efficient RRC signalling for active state handover in RANs with control/data separation
Frequent handovers (HOs) in dense small cell deployment scenarios could lead to a dramatic
increase in signalling overhead. This suggests a paradigm shift towards a signalling conscious cellular
architecture with intelligent mobility management. In this direction, a futuristic radio access network
with a logical separation between control and data planes has been proposed in research community. It
aims to overcome limitations of the conventional architecture by providing high data rate services under
the umbrella of a coverage layer in a dual connection mode. This approach enables signalling efficient
HO procedures, since the control plane remains unchanged when the users move within the footprint of
the same umbrella. Considering this configuration, we propose a core-network efficient radio resource
control (RRC) signalling scheme for active state HO and develop an analytical framework to evaluate its
signalling load as a function of network density, user mobility and session characteristics. In addition,
we propose an intelligent HO prediction scheme with advance resource preparation in order to minimise
the HO signalling latency. Numerical and simulation results show promising gains in terms of reduction
in HO latency and signalling load as compared with conventional approaches
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
On the Tradeoff between Energy Harvesting and Caching in Wireless Networks
Self-powered, energy harvesting small cell base stations (SBS) are expected
to be an integral part of next-generation wireless networks. However, due to
uncertainties in harvested energy, it is necessary to adopt energy efficient
power control schemes to reduce an SBSs' energy consumption and thus ensure
quality-of-service (QoS) for users. Such energy-efficient design can also be
done via the use of content caching which reduces the usage of the
capacity-limited SBS backhaul. of popular content at SBS can also prove
beneficial in this regard by reducing the backhaul usage. In this paper, an
online energy efficient power control scheme is developed for an energy
harvesting SBS equipped with a wireless backhaul and local storage. In our
model, energy arrivals are assumed to be Poisson distributed and the popularity
distribution of requested content is modeled using Zipf's law. The power
control problem is formulated as a (discounted) infinite horizon dynamic
programming problem and solved numerically using the value iteration algorithm.
Using simulations, we provide valuable insights on the impact of energy
harvesting and caching on the energy and sum-throughput performance of the SBS
as the network size is varied. Our results also show that the size of cache and
energy harvesting equipment at the SBS can be traded off, while still meeting
the desired system performance.Comment: To be presented at the IEEE International Conference on
Communications (ICC), London, U.K., 201
- …