[HTML][HTML] AI augmented Edge and Fog computing: Trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
COSCO: Container orchestration using co-simulation and gradient based optimization for fog computing environments
Intelligent task placement and management of tasks in large-scale fog platforms is
challenging due to the highly volatile nature of modern workload applications and sensitive …
challenging due to the highly volatile nature of modern workload applications and sensitive …
Online resource allocation, content placement and request routing for cost-efficient edge caching in cloud radio access networks
In this paper, we advocate edge caching in cloud radio access networks (C-RAN) to facilitate
the ever-increasing mobile multimedia services. In our framework, central offices will …
the ever-increasing mobile multimedia services. In our framework, central offices will …
Efficient decentralized multi-agent learning in asymmetric queuing systems
We study decentralized multi-agent learning in bipartite queuing systems, a standard model
for service systems. In particular, N agents request service from K servers in a fully …
for service systems. In particular, N agents request service from K servers in a fully …
Learning and information in stochastic networks and queues
We review the role of information and learning in the stability and optimization of queueing
systems. In recent years, techniques from supervised learning, online learning, and …
systems. In recent years, techniques from supervised learning, online learning, and …
GOSH: Task scheduling using deep surrogate models in fog computing environments
Recently, intelligent scheduling approaches using surrogate models have been proposed to
efficiently allocate volatile tasks in heterogeneous fog environments. Advances like …
efficiently allocate volatile tasks in heterogeneous fog environments. Advances like …
Learning while scheduling in multi-server systems with unknown statistics: Maxweight with discounted ucb
Multi-server queueing systems are widely used models for job scheduling in machine
learning, wireless networks, and crowdsourcing. This paper considers a multi-server system …
learning, wireless networks, and crowdsourcing. This paper considers a multi-server system …
Timely-throughput optimal coded computing over cloud networks
In modern distributed computing systems, unpredictable and unreliable infrastructures result
in high variability of computing resources. Meanwhile, there is significantly increasing …
in high variability of computing resources. Meanwhile, there is significantly increasing …
Decentralized scheduling with qos constraints: Achieving o (1) qos regret of multi-player bandits
We consider a decentralized multi-player multi-armed bandit (MP-MAB) problem where
players cannot observe the actions and rewards of other players and no explicit …
players cannot observe the actions and rewards of other players and no explicit …
Learning algorithms for minimizing queue length regret
T Stahlbuhk, B Shrader… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
We consider a system consisting of a single transmitter/receiver pair and N channels over
which they may communicate. Packets randomly arrive to the transmitter's queue and wait to …
which they may communicate. Packets randomly arrive to the transmitter's queue and wait to …