494 research outputs found
Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems
Mobile edge computing (MEC) is a promising technique for providing low-latency access to services at the network
edge. The services are hosted at various types of edge nodes
with both computation and communication capabilities. Due to
the heterogeneity of edge node characteristics and user locations,
the performance of MEC varies depending on where the service
is hosted. In this paper, we consider such a heterogeneous MEC
system, and focus on the problem of placing multiple services
in the system to maximize the total reward. We show that the
problem is NP-hard via reduction from the set cover problem,
and propose a deterministic approximation algorithm to solve
the problem, which has an approximation ratio that is not worse
than (1 − e−1)/4. The proposed algorithm is based on two subroutines that are suitable for small and arbitrarily sized services,
respectively. The algorithm is designed using a novel way of
partitioning each edge node into multiple slots, where each slot
contains one service. The approximation guarantee is obtained
via a specialization of the method of conditional expectations,
which uses a randomized procedure as an intermediate step. In
addition to theoretical guarantees, simulation results also show
that the proposed algorithm outperforms other state-of-the-art
approache
LQG Control and Sensing Co-Design
We investigate a Linear-Quadratic-Gaussian (LQG) control and sensing
co-design problem, where one jointly designs sensing and control policies. We
focus on the realistic case where the sensing design is selected among a finite
set of available sensors, where each sensor is associated with a different cost
(e.g., power consumption). We consider two dual problem instances:
sensing-constrained LQG control, where one maximizes control performance
subject to a sensor cost budget, and minimum-sensing LQG control, where one
minimizes sensor cost subject to performance constraints. We prove no
polynomial time algorithm guarantees across all problem instances a constant
approximation factor from the optimal. Nonetheless, we present the first
polynomial time algorithms with per-instance suboptimality guarantees. To this
end, we leverage a separation principle, that partially decouples the design of
sensing and control. Then, we frame LQG co-design as the optimization of
approximately supermodular set functions; we develop novel algorithms to solve
the problems; and we prove original results on the performance of the
algorithms, and establish connections between their suboptimality and
control-theoretic quantities. We conclude the paper by discussing two
applications, namely, sensing-constrained formation control and
resource-constrained robot navigation.Comment: Accepted to IEEE TAC. Includes contributions to submodular function
optimization literature, and extends conference paper arXiv:1709.0882
Hierarchical and Decentralised Federated Learning
Federated learning has shown enormous promise as a way of training ML models
in distributed environments while reducing communication costs and protecting
data privacy. However, the rise of complex cyber-physical systems, such as the
Internet-of-Things, presents new challenges that are not met with traditional
FL methods. Hierarchical Federated Learning extends the traditional FL process
to enable more efficient model aggregation based on application needs or
characteristics of the deployment environment (e.g., resource capabilities
and/or network connectivity). It illustrates the benefits of balancing
processing across the cloud-edge continuum. Hierarchical Federated Learning is
likely to be a key enabler for a wide range of applications, such as smart
farming and smart energy management, as it can improve performance and reduce
costs, whilst also enabling FL workflows to be deployed in environments that
are not well-suited to traditional FL. Model aggregation algorithms, software
frameworks, and infrastructures will need to be designed and implemented to
make such solutions accessible to researchers and engineers across a growing
set of domains.
H-FL also introduces a number of new challenges. For instance, there are
implicit infrastructural challenges. There is also a trade-off between having
generalised models and personalised models. If there exist geographical
patterns for data (e.g., soil conditions in a smart farm likely are related to
the geography of the region itself), then it is crucial that models used
locally can consider their own locality in addition to a globally-learned
model. H-FL will be crucial to future FL solutions as it can aggregate and
distribute models at multiple levels to optimally serve the trade-off between
locality dependence and global anomaly robustness.Comment: 11 pages, 6 figures, 25 reference
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