848 research outputs found
Resilient Distributed Optimization Algorithms for Resource Allocation
Distributed algorithms provide flexibility over centralized algorithms for
resource allocation problems, e.g., cyber-physical systems. However, the
distributed nature of these algorithms often makes the systems susceptible to
man-in-the-middle attacks, especially when messages are transmitted between
price-taking agents and a central coordinator. We propose a resilient strategy
for distributed algorithms under the framework of primal-dual distributed
optimization. We formulate a robust optimization model that accounts for
Byzantine attacks on the communication channels between agents and coordinator.
We propose a resilient primal-dual algorithm using state-of-the-art robust
statistics methods. The proposed algorithm is shown to converge to a
neighborhood of the robust optimization model, where the neighborhood's radius
is proportional to the fraction of attacked channels.Comment: 15 pages, 1 figure, accepted to CDC 201
Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms with Directed Gossip Communication
We study distributed optimization in networked systems, where nodes cooperate
to find the optimal quantity of common interest, x=x^\star. The objective
function of the corresponding optimization problem is the sum of private (known
only by a node,) convex, nodes' objectives and each node imposes a private
convex constraint on the allowed values of x. We solve this problem for generic
connected network topologies with asymmetric random link failures with a novel
distributed, decentralized algorithm. We refer to this algorithm as AL-G
(augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented
Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast
gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual
function. Dual variables are updated by the standard method of multipliers, at
a slow time scale. To update the primal variables, we propose a novel,
Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses
unidirectional gossip communication, only between immediate neighbors in the
network and is resilient to random link failures. For networks with reliable
communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian
broadcast gossiping) algorithm reduces communication, computation and data
storage cost. We prove convergence for all proposed algorithms and demonstrate
by simulations the effectiveness on two applications: l_1-regularized logistic
regression for classification and cooperative spectrum sensing for cognitive
radio networks.Comment: 28 pages, journal; revise
Robust-to-Noise Algorithms for Distributed Resource Allocation and Scheduling
Efficient resource allocation and scheduling algorithms are essential for
various distributed applications, ranging from wireless networks and cloud
computing platforms to autonomous multi-agent systems and swarm robotic
networks. However, real-world environments are often plagued by uncertainties
and noise, leading to sub-optimal performance and increased vulnerability of
traditional algorithms. This paper addresses the challenge of robust resource
allocation and scheduling in the presence of noise and disturbances. The
proposed study introduces a novel sign-based dynamics for developing
robust-to-noise algorithms distributed over a multi-agent network that can
adaptively handle external disturbances. Leveraging concepts from convex
optimization theory, control theory, and network science the framework
establishes a principled approach to design algorithms that can maintain key
properties such as resource-demand balance and constraint feasibility.
Meanwhile, notions of uniform-connectivity and versatile networking conditions
are also addressed.Comment: IEEE/RSI ICRoM202
Consensus-based approach to peer-to-peer electricity markets with product differentiation
With the sustained deployment of distributed generation capacities and the
more proactive role of consumers, power systems and their operation are
drifting away from a conventional top-down hierarchical structure. Electricity
market structures, however, have not yet embraced that evolution. Respecting
the high-dimensional, distributed and dynamic nature of modern power systems
would translate to designing peer-to-peer markets or, at least, to using such
an underlying decentralized structure to enable a bottom-up approach to future
electricity markets. A peer-to-peer market structure based on a Multi-Bilateral
Economic Dispatch (MBED) formulation is introduced, allowing for
multi-bilateral trading with product differentiation, for instance based on
consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is
described to solve the MBED in fully decentralized manner. A set of realistic
case studies and their analysis allow us showing that such peer-to-peer market
structures can effectively yield market outcomes that are different from
centralized market structures and optimal in terms of respecting consumers
preferences while maximizing social welfare. Additionally, the RCI solving
approach allows for a fully decentralized market clearing which converges with
a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System
A Primal-Dual Based Power Control Approach for Capacitated Edge Servers
The intensity of radio waves decays rapidly with increasing propagation
distance, and an edge server's antenna needs more power to form a larger signal
coverage area. Therefore, the power of the edge server should be controlled to
reduce energy consumption. In addition, edge servers with capacitated resources
provide services for only a limited number of users to ensure the quality of
service (QoS). We set the signal transmission power for the antenna of each
edge server and formed a signal disk, ensuring that all users were covered by
the edge server signal and minimizing the total power of the system. This
scenario is a typical geometric set covering problem, and even simple cases
without capacity limits are NP-hard problems. In this paper, we propose a
primal-dual-based algorithm and obtain an -approximation result. We compare
our algorithm with two other algorithms through simulation experiments. The
results show that our algorithm obtains a result close to the optimal value in
polynomial time
- …