6,246 research outputs found
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Merging mobile edge computing (MEC) functionality with the dense deployment
of base stations (BSs) provides enormous benefits such as a real proximity, low
latency access to computing resources. However, the envisioned integration
creates many new challenges, among which mobility management (MM) is a critical
one. Simply applying existing radio access oriented MM schemes leads to poor
performance mainly due to the co-provisioning of radio access and computing
services of the MEC-enabled BSs. In this paper, we develop a novel user-centric
energy-aware mobility management (EMM) scheme, in order to optimize the delay
due to both radio access and computation, under the long-term energy
consumption constraint of the user. Based on Lyapunov optimization and
multi-armed bandit theories, EMM works in an online fashion without future
system state information, and effectively handles the imperfect system state
information. Theoretical analysis explicitly takes radio handover and
computation migration cost into consideration and proves a bounded deviation on
both the delay performance and energy consumption compared to the oracle
solution with exact and complete future system information. The proposed
algorithm also effectively handles the scenario in which candidate BSs randomly
switch on/off during the offloading process of a task. Simulations show that
the proposed algorithms can achieve close-to-optimal delay performance while
satisfying the user energy consumption constraint.Comment: 14 pages, 6 figures, an extended version of the paper submitted to
IEEE JSA
Spatio-temporal Edge Service Placement: A Bandit Learning Approach
Shared edge computing platforms deployed at the radio access network are
expected to significantly improve quality of service delivered by Application
Service Providers (ASPs) in a flexible and economic way. However, placing edge
service in every possible edge site by an ASP is practically infeasible due to
the ASP's prohibitive budget requirement. In this paper, we investigate the
edge service placement problem of an ASP under a limited budget, where the ASP
dynamically rents computing/storage resources in edge sites to host its
applications in close proximity to end users. Since the benefit of placing edge
service in a specific site is usually unknown to the ASP a priori, optimal
placement decisions must be made while learning this benefit. We pose this
problem as a novel combinatorial contextual bandit learning problem. It is
"combinatorial" because only a limited number of edge sites can be rented to
provide the edge service given the ASP's budget. It is "contextual" because we
utilize user context information to enable finer-grained learning and decision
making. To solve this problem and optimize the edge computing performance, we
propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore,
SEEN is extended to scenarios with overlapping service coverage by
incorporating a disjunctively constrained knapsack problem. In both cases, we
prove that our algorithm achieves a sublinear regret bound when it is compared
to an oracle algorithm that knows the exact benefit information. Simulations
are carried out on a real-world dataset, whose results show that SEEN
significantly outperforms benchmark solutions
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Predicting traffic conditions has been recently explored as a way to relieve
traffic congestion. Several pioneering approaches have been proposed based on
traffic observations of the target location as well as its adjacent regions,
but they obtain somewhat limited accuracy due to lack of mining road topology.
To address the effect attenuation problem, we propose to take account of the
traffic of surrounding locations(wider than adjacent range). We propose an
end-to-end framework called DeepTransport, in which Convolutional Neural
Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain
spatial-temporal traffic information within a transport network topology. In
addition, attention mechanism is introduced to align spatial and temporal
information. Moreover, we constructed and released a real-world large traffic
condition dataset with 5-minute resolution. Our experiments on this dataset
demonstrate our method captures the complex relationship in temporal and
spatial domain. It significantly outperforms traditional statistical methods
and a state-of-the-art deep learning method
Temperature-dependent Cross Sections for Charmonium Dissociation in Collisions with Pions and Rhos in Hadronic Matter
Meson-charmonium dissociation reactions governed by the quark interchange are
studied with temperature-dependent quark potentials. Quark-antiquark
relative-motion wave functions and masses of charmonia and charmed mesons are
determined by the central spin-independent part of the potentials or by the
central spin-independent part and a smeared spin-spin interaction. The
prominent temperature dependence of the masses is found. Based on the
potentials, the wave functions, and the meson masses, we obtain
temperature-dependent cross sections for fifteen pion-charmonium and
rho-charmonium dissociation reactions. The numerical cross sections are
parametrized for future applications in hadronic matter. The particular
temperature dependence of the J/psi bound state leads to unusual behavior of
the cross sections for endothermic J/psi dissociation reactions. The quantum
numbers of psi' and chi_c can not make their difference in mass in the
temperature region 0.6T_c < T < T_c, but can make the psi' dissociation
different from the chi_c dissociation.Comment: 52 pages, 23 figures, 6 table
The construction of -splitting map
For a geodesic ball with non-negative Ricci curvature and almost maximal
volume, without using compactness argument, we construct an
-splitting map on a concentric geodesic ball with uniformly small
radius. There are two new technical points in our proof. The first one is the
way of finding directional points by induction and stratified almost Gou-Gu
Theorem. The other one is the error estimates of projections, which guarantee
the directional points we find really determine different directions.Comment: to appear in Calculus of Variations and Partial Differential
Equation
On the Statistical Multiplexing Gain of Virtual Base Station Pools
Facing the explosion of mobile data traffic, cloud radio access network
(C-RAN) is proposed recently to overcome the efficiency and flexibility
problems with the traditional RAN architecture by centralizing baseband
processing. However, there lacks a mathematical model to analyze the
statistical multiplexing gain from the pooling of virtual base stations (VBSs)
so that the expenditure on fronthaul networks can be justified. In this paper,
we address this problem by capturing the session-level dynamics of VBS pools
with a multi-dimensional Markov model. This model reflects the constraints
imposed by both radio resources and computational resources. To evaluate the
pooling gain, we derive a product-form solution for the stationary distribution
and give a recursive method to calculate the blocking probabilities. For
comparison, we also derive the limit of resource utilization ratio as the pool
size approaches infinity. Numerical results show that VBS pools can obtain
considerable pooling gain readily at medium size, but the convergence to large
pool limit is slow because of the quickly diminishing marginal pooling gain. We
also find that parameters such as traffic load and desired Quality of Service
(QoS) have significant influence on the performance of VBS pools.Comment: Accepted by GlobeCom'1
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