86 research outputs found
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
Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks
Mobile Edge Computing (MEC) pushes computing functionalities away from the
centralized cloud to the network edge, thereby meeting the latency requirements
of many emerging mobile applications and saving backhaul network bandwidth.
Although many existing works have studied computation offloading policies,
service caching is an equally, if not more important, design topic of MEC, yet
receives much less attention. Service caching refers to caching application
services and their related databases/libraries in the edge server (e.g.
MEC-enabled BS), thereby enabling corresponding computation tasks to be
executed. Because only a small number of application services can be cached in
resource-limited edge server at the same time, which services to cache has to
be judiciously decided to maximize the edge computing performance. In this
paper, we investigate the extremely compelling but much less studied problem of
dynamic service caching in MEC-enabled dense cellular networks. We propose an
efficient online algorithm, called OREO, which jointly optimizes dynamic
service caching and task offloading to address a number of key challenges in
MEC systems, including service heterogeneity, unknown system dynamics, spatial
demand coupling and decentralized coordination. Our algorithm is developed
based on Lyapunov optimization and Gibbs sampling, works online without
requiring future information, and achieves provable close-to-optimal
performance. Simulation results show that our algorithm can effectively reduce
computation latency for end users while keeping energy consumption low
Adaptive Fog Configuration for the Industrial Internet of Things
Industrial Fog computing deploys various industrial services, such as
automatic monitoring/control and imminent failure detection, at the Fog Nodes
(FNs) to improve the performance of industrial systems. Much effort has been
made in the literature on the design of fog network architecture and
computation offloading. This paper studies an equally important but much less
investigated problem of service hosting where FNs are adaptively configured to
host services for Sensor Nodes (SNs), thereby enabling corresponding tasks to
be executed by the FNs. The problem of service hosting emerges because of the
limited computational and storage resources at FNs, which limit the number of
different types of services that can be hosted by an FN at the same time.
Considering the variability of service demand in both temporal and spatial
dimensions, when, where, and which services to host have to be judiciously
decided to maximize the utility of the Fog computing network. Our proposed Fog
configuration strategies are tailored to battery-powered FNs. The limited
battery capacity of FNs creates a long-term energy budget constraint that
significantly complicates the Fog configuration problem as it introduces
temporal coupling of decision making across the timeline. To address all these
challenges, we propose an online distributed algorithm, called Adaptive Fog
Configuration (AFC), based on Lyapunov optimization and parallel Gibbs
sampling. AFC jointly optimizes service hosting and task admission decisions,
requiring only currently available system information while guaranteeing
close-to-optimal performance compared to an oracle algorithm with full future
information
Experimental preparation and verification of quantum money
A quantum money scheme enables a trusted bank to provide untrusted users with
verifiable quantum banknotes that cannot be forged. In this work, we report an
experimental demonstration of the preparation and verification of unforgeable
quantum banknotes. We employ a security analysis that takes experimental
imperfections fully into account. We measure a total of states
in one verification round, limiting the forging probability to based
on the security analysis. Our results demonstrate the feasibility of preparing
and verifying quantum banknotes using currently available experimental
techniques.Comment: 12 pages, 4 figure
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