1,521 research outputs found
Stochastic Online Shortest Path Routing: The Value of Feedback
This paper studies online shortest path routing over multi-hop networks. Link
costs or delays are time-varying and modeled by independent and identically
distributed random processes, whose parameters are initially unknown. The
parameters, and hence the optimal path, can only be estimated by routing
packets through the network and observing the realized delays. Our aim is to
find a routing policy that minimizes the regret (the cumulative difference of
expected delay) between the path chosen by the policy and the unknown optimal
path. We formulate the problem as a combinatorial bandit optimization problem
and consider several scenarios that differ in where routing decisions are made
and in the information available when making the decisions. For each scenario,
we derive a tight asymptotic lower bound on the regret that has to be satisfied
by any online routing policy. These bounds help us to understand the
performance improvements we can expect when (i) taking routing decisions at
each hop rather than at the source only, and (ii) observing per-link delays
rather than end-to-end path delays. In particular, we show that (i) is of no
use while (ii) can have a spectacular impact. Three algorithms, with a
trade-off between computational complexity and performance, are proposed. The
regret upper bounds of these algorithms improve over those of the existing
algorithms, and they significantly outperform state-of-the-art algorithms in
numerical experiments.Comment: 18 page
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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