701 research outputs found
Distributed Lagrangian Methods for Network Resource Allocation
Motivated by a variety of applications in control engineering and information
sciences, we study network resource allocation problems where the goal is to
optimally allocate a fixed amount of resource over a network of nodes. In these
problems, due to the large scale of the network and complicated
inter-connections between nodes, any solution must be implemented in parallel
and based only on local data resulting in a need for distributed algorithms. In
this paper, we propose a novel distributed Lagrangian method, which requires
only local computation and communication. Our focus is to understand the
performance of this algorithm on the underlying network topology. Specifically,
we obtain an upper bound on the rate of convergence of the algorithm as a
function of the size and the topology of the underlying network. The
effectiveness and applicability of the proposed method is demonstrated by its
use in solving the important economic dispatch problem in power systems,
specifically on the benchmark IEEE-14 and IEEE-118 bus systems
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation
This paper proposes two nonlinear dynamics to solve constrained distributed
optimization problem for resource allocation over a multi-agent network. In
this setup, coupling constraint refers to resource-demand balance which is
preserved at all-times. The proposed solutions can address various model
nonlinearities, for example, due to quantization and/or saturation. Further, it
allows to reach faster convergence or to robustify the solution against
impulsive noise or uncertainties. We prove convergence over weakly connected
networks using convex analysis and Lyapunov theory. Our findings show that
convergence can be reached for general sign-preserving odd nonlinearity. We
further propose delay-tolerant mechanisms to handle general bounded
heterogeneous time-varying delays over the communication network of agents
while preserving all-time feasibility. This work finds application in CPU
scheduling and coverage control among others. This paper advances the
state-of-the-art by addressing (i) possible nonlinearity on the agents/links,
meanwhile handling (ii) resource-demand feasibility at all times, (iii)
uniform-connectivity instead of all-time connectivity, and (iv) possible
heterogeneous and time-varying delays. To our best knowledge, no existing work
addresses contributions (i)-(iv) altogether. Simulations and comparative
analysis are provided to corroborate our contributions
Distributed Energy Resource Management: All-Time Resource-Demand Feasibility, Delay-Tolerance, Nonlinearity, and Beyond
In this work, we propose distributed and networked energy management
scenarios to optimize the production and reservation of energy among a set of
distributed energy nodes. In other words, the idea is to optimally allocate the
generated and reserved powers based on nodes' local cost gradient information
while meeting the demand energy. One main concern is the all-time (or anytime)
resource-demand feasibility, implying that at all iterations of the scheduling
algorithm, the balance between the produced power and demand plus reserved
power must hold. The other concern is to design algorithms to tolerate
communication time-delays and changes in the network. Further, one can
incorporate possible model nonlinearity in the algorithm to address both
inherent (e.g., saturation and quantization) and purposefully-added (e.g.,
signum-based) nonlinearities in the model. The proposed optimal allocation
algorithm addresses all the above concerns, while it benefits from possible
features of the distributed (or networked) solutions such as
no-single-node-of-failure and distributed information processing. We show both
the all-time feasibility of the proposed scheme and its convergence under
certain bound on the step-rate using Lyapunov-type proofs.Comment: IEEE LCSS 202
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