@inproceedings{29220,
  abstract     = {{Modern services often comprise several components, such as chained virtual network functions, microservices, or
machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. 
To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. 

Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.}},
  author       = {{Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}},
  booktitle    = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}},
  keywords     = {{network management, service management, AI, Monte Carlo Tree Search, model-based, QoS}},
  location     = {{Budapest}},
  publisher    = {{IEEE}},
  title        = {{{Use What You Know: Network and Service Coordination Beyond Certainty}}},
  year         = {{2022}},
}

@inproceedings{10598,
  abstract     = {{Approximate computing has become a very popular design
strategy that exploits error resilient computations to achieve higher
performance and energy efficiency. Automated synthesis of approximate
circuits is performed via functional approximation, in which various
parts of the target circuit are extensively examined with a library
of approximate components/transformations to trade off the functional
accuracy and computational budget (i.e., power). However, as the number
of possible approximate transformations increases, traditional search
techniques suffer from a combinatorial explosion due to the large
branching factor. In this work, we present a comprehensive framework
for automated synthesis of approximate circuits from either structural
or behavioral descriptions. We adapt the Monte Carlo Tree Search
(MCTS), as a stochastic search technique, to deal with the large design
space exploration, which enables a broader range of potential possible
approximations through lightweight random simulations. The proposed
framework is able to recognize the design Pareto set even with low
computational budgets. Experimental results highlight the capabilities of
the proposed synthesis framework by resulting in up to 61.69% energy
saving while maintaining the predefined quality constraints.}},
  author       = {{Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}},
  booktitle    = {{26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}},
  keywords     = {{Approximate computing, High-level synthesis, Accuracy, Monte-Carlo tree search, Circuit simulation}},
  pages        = {{219--224}},
  title        = {{{An MCTS-based Framework for Synthesis of Approximate Circuits}}},
  doi          = {{10.1109/VLSI-SoC.2018.8645026}},
  year         = {{2018}},
}