89,904 research outputs found
DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning
The increasing reliance upon cloud services entails more flexible networks
that are realized by virtualized network equipment and functions. When such
advanced network systems face a massive failure by natural disasters or
attacks, the recovery of the entire system may be conducted in a progressive
way due to limited repair resources. The prioritization of network equipment in
the recovery phase influences the interim computation and communication
capability of systems, since the systems are operated under partial
functionality. Hence, finding the best recovery order is a critical problem,
which is further complicated by virtualization due to dependency among network
nodes and layers. This paper deals with a progressive recovery problem under
limited resources in networks with VNFs, where some dependent network layers
exist. We prove the NP-hardness of the progressive recovery problem and
approach the optimum solution by introducing DeepPR, a progressive recovery
technique based on Deep Reinforcement Learning (Deep RL). Our simulation
results indicate that DeepPR can achieve the near-optimal solutions in certain
networks and is more robust to adversarial failures, compared to a baseline
heuristic algorithm.Comment: Technical Report, 12 page
Intra-Cluster Autonomous Coverage Optimization For Dense LTE-A Networks
Self Organizing Networks (SONs) are considered as vital deployments towards
upcoming dense cellular networks. From a mobile carrier point of view,
continuous coverage optimization is critical for better user perceptions. The
majority of SON contributions introduce novel algorithms that optimize specific
performance metrics. However, they require extensive processing delays and
advanced knowledge of network statistics that may not be available. In this
work, a progressive Autonomous Coverage Optimization (ACO) method combined with
adaptive cell dimensioning is proposed. The proposed method emphasizes the fact
that the effective cell coverage is a variant on actual user distributions. ACO
algorithm builds a generic Space-Time virtual coverage map per cell to detect
coverage holes in addition to limited or extended coverage conditions.
Progressive levels of optimization are followed to timely resolve coverage
issues with maintaining optimization stability. Proposed ACO is verified under
both simulations and practical deployment in a pilot cluster for a worldwide
mobile carrier. Key Performance Indicators show that proposed ACO method
significantly enhances system coverage and performance.Comment: conferenc
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