279 research outputs found
Allowing non-submodular score functions in distributed task allocation
Submodularity is a powerful property that can be exploited for provable performance and convergence guarantees in distributed task allocation algorithms. However, some mission scenarios cannot easily be approximated as submodular a priori. This paper introduces an algorithmic extension for distributed multi-agent multi-task assignment algorithms which provides guaranteed convergence using non-submodular score functions. This algorithm utilizes non-submodular ranking of tasks within each agent's internal decision making process, while externally enforcing that shared bids appear as if they were created using submodular score functions. Provided proofs demonstrate that all convergence and performance guarantees hold with respect to this apparent submodular score function. The algorithm allows significant improvements over heuristic approaches that approximate truly non-submodular score functions.United States. Air Force Office of Scientific Research (Grant FA9550-11-1-0134
An Auction-based Coordination Strategy for Task-Constrained Multi-Agent Stochastic Planning with Submodular Rewards
In many domains such as transportation and logistics, search and rescue, or
cooperative surveillance, tasks are pending to be allocated with the
consideration of possible execution uncertainties. Existing task coordination
algorithms either ignore the stochastic process or suffer from the
computational intensity. Taking advantage of the weakly coupled feature of the
problem and the opportunity for coordination in advance, we propose a
decentralized auction-based coordination strategy using a newly formulated
score function which is generated by forming the problem into task-constrained
Markov decision processes (MDPs). The proposed method guarantees convergence
and at least 50% optimality in the premise of a submodular reward function.
Furthermore, for the implementation on large-scale applications, an approximate
variant of the proposed method, namely Deep Auction, is also suggested with the
use of neural networks, which is evasive of the troublesome for constructing
MDPs. Inspired by the well-known actor-critic architecture, two Transformers
are used to map observations to action probabilities and cumulative rewards
respectively. Finally, we demonstrate the performance of the two proposed
approaches in the context of drone deliveries, where the stochastic planning
for the drone league is cast into a stochastic price-collecting Vehicle Routing
Problem (VRP) with time windows. Simulation results are compared with
state-of-the-art methods in terms of solution quality, planning efficiency and
scalability.Comment: 17 pages, 5 figure
On distributed virtual network embedding with guarantees
To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst case efficiency of (?????) relative to the optimal solution, and that this bound is optimal, that is, no better approximation exists. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared with existing distributed VNET embedding solutions, and we show how byappropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.This work is supported in part by the National Science Foundation under grant CNS-0963974
On distributed virtual network embedding with guarantees
To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst case efficiency of (?????) relative to the optimal solution, and that this bound is optimal, that is, no better approximation exists. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared with existing distributed VNET embedding solutions, and we show how byappropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.This work is supported in part by the National Science Foundation under grant CNS-0963974
Submodularity in Action: From Machine Learning to Signal Processing Applications
Submodularity is a discrete domain functional property that can be
interpreted as mimicking the role of the well-known convexity/concavity
properties in the continuous domain. Submodular functions exhibit strong
structure that lead to efficient optimization algorithms with provable
near-optimality guarantees. These characteristics, namely, efficiency and
provable performance bounds, are of particular interest for signal processing
(SP) and machine learning (ML) practitioners as a variety of discrete
optimization problems are encountered in a wide range of applications.
Conventionally, two general approaches exist to solve discrete problems:
relaxation into the continuous domain to obtain an approximate solution, or
development of a tailored algorithm that applies directly in the
discrete domain. In both approaches, worst-case performance guarantees are
often hard to establish. Furthermore, they are often complex, thus not
practical for large-scale problems. In this paper, we show how certain
scenarios lend themselves to exploiting submodularity so as to construct
scalable solutions with provable worst-case performance guarantees. We
introduce a variety of submodular-friendly applications, and elucidate the
relation of submodularity to convexity and concavity which enables efficient
optimization. With a mixture of theory and practice, we present different
flavors of submodularity accompanying illustrative real-world case studies from
modern SP and ML. In all cases, optimization algorithms are presented, along
with hints on how optimality guarantees can be established
On distributed virtual network embedding with guarantees
To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst-case efficiency of (1-(1/e)) relative to the optimal node embedding, or VNET embedding if virtual links are mapped to exactly one physical link. This bound is optimal, that is, no better polynomial-time approximation algorithm exists, unless P=NP. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared to existing distributed VNET embedding solutions, and we show how by appropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.CNS-0963974 - National Science Foundationhttp://www.cs.bu.edu/fac/matta/Papers/ToN-CAD.pdfAccepted manuscrip
Routing optimization algorithms in integrated fronthaul/backhaul networks supporting multitenancy
Mención Internacional en el título de doctorEsta tesis pretende ayudar en la definición y el diseño de la quinta generación de
redes de telecomunicaciones (5G) a través del modelado matemático de las diferentes
cualidades que las caracterizan. En general, la ambición de estos modelos es realizar
una optimización de las redes, ensalzando sus capacidades recientemente adquiridas para
mejorar la eficiencia de los futuros despliegues tanto para los usuarios como para los
operadores. El periodo de realización de esta tesis se corresponde con el periodo de
investigación y definición de las redes 5G, y, por lo tanto, en paralelo y en el contexto
de varios proyectos europeos del programa H2020. Por lo tanto, las diferentes partes
del trabajo presentado en este documento cuadran y ofrecen una solución a diferentes
retos que han ido apareciendo durante la definición del 5G y dentro del ámbito de estos
proyectos, considerando los comentarios y problemas desde el punto de vista de todos los
usuarios finales, operadores y proveedores.
Así, el primer reto a considerar se centra en el núcleo de la red, en particular en
cómo integrar tráfico fronthaul y backhaul en el mismo estrato de transporte. La solución
propuesta es un marco de optimización para el enrutado y la colocación de recursos que
ha sido desarrollado teniendo en cuenta restricciones de retardo, capacidad y caminos,
maximizando el grado de despliegue de Unidades Distribuidas (DU) mientras se minimizan
los agregados de las Unidades Centrales (CU) que las soportan. El marco y los algoritmos
heurísticos desarrollados (para reducir la complexidad computacional) son validados y
aplicados a redes tanto a pequeña como a gran (nivel de producción) escala. Esto los
hace útiles para los operadores de redes tanto para la planificación de la red como para
el ajuste dinámico de las operaciones de red en su infraestructura (virtualizada).
Moviéndonos más cerca de los usuarios, el segundo reto considerado se centra en
la colocación de servicios en entornos de nube y borde (cloud/edge). En particular, el
problema considerado consiste en seleccionar la mejor localización para cada función
de red virtual (VNF) que compone un servicio en entornos de robots en la nube, que
implica restricciones estrictas en las cotas de retardo y fiabilidad. Los robots, vehículos y
otros dispositivos finales proveen competencias significativas como impulsores, sensores y
computación local que son esenciales para algunos servicios. Por contra, estos dispositivos
están en continuo movimiento y pueden perder la conexión con la red o quedarse sin batería, cosa que reta aún más la entrega de servicios en este entorno dinámico. Así, el
análisis realizado y la solución propuesta abordan las restricciones de movilidad y batería.
Además, también se necesita tener en cuenta los aspectos temporales y los objetivos
conflictivos de fiabilidad y baja latencia en el despliegue de servicios en una red volátil,
donde los nodos de cómputo móviles actúan como una extensión de la infraestructura
de cómputo de la nube y el borde. El problema se formula como un problema de
optimización para colocación de VNFs minimizando el coste y también se propone un
heurístico eficiente. Los algoritmos son evaluados de forma extensiva desde varios aspectos
por simulación en escenarios que reflejan la realidad de forma detallada.
Finalmente, el último reto analizado se centra en dar soporte a servicios basados en
el borde, en particular, aprendizaje automático (ML) en escenarios del Internet de las
Cosas (IoT) distribuidos. El enfoque tradicional al ML distribuido se centra en adaptar
los algoritmos de aprendizaje a la red, por ejemplo, reduciendo las actualizaciones para
frenar la sobrecarga. Las redes basadas en el borde inteligente, en cambio, hacen posible
seguir un enfoque opuesto, es decir, definir la topología de red lógica alrededor de la
tarea de aprendizaje a realizar, para así alcanzar el resultado de aprendizaje deseado.
La solución propuesta incluye un modelo de sistema que captura dichos aspectos en
el contexto de ML supervisado, teniendo en cuenta tanto nodos de aprendizaje (que
realizan las computaciones) como nodos de información (que proveen datos). El problema
se formula para seleccionar (i) qué nodos de aprendizaje e información deben cooperar
para completar la tarea de aprendizaje, y (ii) el número de iteraciones a realizar, para
minimizar el coste de aprendizaje mientras se garantizan los objetivos de error predictivo y
tiempo de ejecución. La solución también incluye un algoritmo heurístico que es evaluado
ensalzando una topología de red real y considerando tanto las tareas de clasificación
como de regresión, y cuya solución se acerca mucho al óptimo, superando las soluciones
alternativas encontradas en la literatura.This thesis aims to help in the definition and design of the 5th generation of
telecommunications networks (5G) by modelling the different features that characterize
them through several mathematical models. Overall, the aim of these models is to perform
a wide optimization of the network elements, leveraging their newly-acquired capabilities
in order to improve the efficiency of the future deployments both for the users and the
operators. The timeline of this thesis corresponds to the timeline of the research and
definition of 5G networks, and thus in parallel and in the context of several European
H2020 programs. Hence, the different parts of the work presented in this document
match and provide a solution to different challenges that have been appearing during
the definition of 5G and within the scope of those projects, considering the feedback and
problems from the point of view of all the end users, operators and providers.
Thus, the first challenge to be considered focuses on the core network, in particular
on how to integrate fronthaul and backhaul traffic over the same transport stratum.
The solution proposed is an optimization framework for routing and resource placement
that has been developed taking into account delay, capacity and path constraints,
maximizing the degree of Distributed Unit (DU) deployment while minimizing the
supporting Central Unit (CU) pools. The framework and the developed heuristics (to
reduce the computational complexity) are validated and applied to both small and largescale
(production-level) networks. They can be useful to network operators for both
network planning as well as network operation adjusting their (virtualized) infrastructure
dynamically.
Moving closer to the user side, the second challenge considered focuses on the
allocation of services in cloud/edge environments. In particular, the problem tackled
consists of selecting the best the location of each Virtual Network Function (VNF)
that compose a service in cloud robotics environments, that imply strict delay bounds
and reliability constraints. Robots, vehicles and other end-devices provide significant
capabilities such as actuators, sensors and local computation which are essential for some
services. On the negative side, these devices are continuously on the move and might
lose network connection or run out of battery, which further challenge service delivery in
this dynamic environment. Thus, the performed analysis and proposed solution tackle the mobility and battery restrictions. We further need to account for the temporal aspects and
conflicting goals of reliable, low latency service deployment over a volatile network, where
mobile compute nodes act as an extension of the cloud and edge computing infrastructure.
The problem is formulated as a cost-minimizing VNF placement optimization and an
efficient heuristic is proposed. The algorithms are extensively evaluated from various
aspects by simulation on detailed real-world scenarios.
Finally, the last challenge analyzed focuses on supporting edge-based services, in
particular, Machine Learning (ML) in distributed Internet of Things (IoT) scenarios. The
traditional approach to distributed ML is to adapt learning algorithms to the network, e.g.,
reducing updates to curb overhead. Networks based on intelligent edge, instead, make
it possible to follow the opposite approach, i.e., to define the logical network topology
around the learning task to perform, so as to meet the desired learning performance.
The proposed solution includes a system model that captures such aspects in the context
of supervised ML, accounting for both learning nodes (that perform computations) and
information nodes (that provide data). The problem is formulated to select (i) which
learning and information nodes should cooperate to complete the learning task, and (ii)
the number of iterations to perform, in order to minimize the learning cost while meeting
the target prediction error and execution time. The solution also includes an heuristic
algorithm that is evaluated leveraging a real-world network topology and considering
both classification and regression tasks, and closely matches the optimum, outperforming
state-of-the-art alternatives.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Pablo Serrano Yáñez-Mingot.- Secretario: Andrés García Saavedra.- Vocal: Luca Valcarengh
Partial Replanning for Decentralized Dynamic Task Allocation
In time-sensitive and dynamic missions, multi-UAV teams must respond quickly
to new information and objectives. This paper presents a dynamic decentralized
task allocation algorithm for allocating new tasks that appear online during
the solving of the task allocation problem. Our algorithm extends the
Consensus-Based Bundle Algorithm (CBBA), a decentralized task allocation
algorithm, allowing for the fast allocation of new tasks without a full
reallocation of existing tasks. CBBA with Partial Replanning (CBBA-PR) enables
the team to trade-off between convergence time and increased coordination by
resetting a portion of their previous allocation at every round of bidding on
tasks. By resetting the last tasks allocated by each agent, we are able to
ensure the convergence of the team to a conflict-free solution. CBBA-PR can be
further improved by reducing the team size involved in the replanning, further
reducing the communication burden of the team and runtime of CBBA-PR. Finally,
we validate the faster convergence and improved solution quality of CBBA-PR in
multi-UAV simulations.Comment: 11 pages, Accepted to AIAA GNC 201
Distributed strategy adaptation with a prediction function in multi-agent task allocation
Coordinating multiple agents to complete a set of tasks under time constraints is a complex problem. Distributed consensus-based task allocation algorithms address this problem without the need for human supervision. With such algorithms, agents add tasks to their own schedule according to specified allocation strategies. Various
factors, such as the available resources and number of tasks, may affect the efficiency of a particular allocation strategy. The novel
idea we suggest is that each individual agent can predict locally the best task inclusion strategy, based on the limited task assignment information communicated among networked agents. Using supervised
classification learning, a function is trained to predict the most appropriate strategy between two well known insertion heuristics. Using the proposed method, agents are shown to correctly predict and select the optimal insertion heuristic to achieve the overall highest number of task allocations. The adaptive agents consistently match the performances of the best non-adaptive agents across a variety of scenarios. This study aims to demonstrate the possibility and potential performance benefits of giving agents greater decision making capabilities to independently adapt the task allocation
process in line with the problem of interest
- …