TY - GEN
T1 - ATMoN
T2 - 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018
AU - Trihinas, Demetris
AU - Chiroque, Luis F.
AU - Pallis, George
AU - Fernández Anta, Antonio
AU - Dikaiakos, Marios D.
PY - 2018/7/19
Y1 - 2018/7/19
N2 - With the widespread adoption of temporal graphs to study fast evolving interactions in dynamic networks, attention is needed to provide graph metrics in time and at scale. In this paper, we introduce ATMoN, an open-source library developed to computationally offload graph processing engines and ease the communication overhead in dynamic networks over an unprecedented wealth of data. This is achieved, by efficiently adapting, in place and inexpensively, the temporal granularity at which graph metrics are computed based on runtime knowledge captured by a low-cost probabilistic learning model capable of approximating both the metric stream evolution and the volatility of the graph topology. After a thorough evaluation with real-world data from mobile, face-to-face and vehicular networks, results show that ATMoN is able to reduce the compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.
AB - With the widespread adoption of temporal graphs to study fast evolving interactions in dynamic networks, attention is needed to provide graph metrics in time and at scale. In this paper, we introduce ATMoN, an open-source library developed to computationally offload graph processing engines and ease the communication overhead in dynamic networks over an unprecedented wealth of data. This is achieved, by efficiently adapting, in place and inexpensively, the temporal granularity at which graph metrics are computed based on runtime knowledge captured by a low-cost probabilistic learning model capable of approximating both the metric stream evolution and the volatility of the graph topology. After a thorough evaluation with real-world data from mobile, face-to-face and vehicular networks, results show that ATMoN is able to reduce the compute overhead by at least 76%, data volume by 60% and overall cloud costs by at least 54%, while always maintaining accuracy above 88%.
KW - Adaptive Monitoring
KW - Dynamic Networks
KW - Edge Computing
KW - Temporal Graphs
UR - http://www.scopus.com/inward/record.url?scp=85050980767&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2018.00047
DO - 10.1109/ICDCS.2018.00047
M3 - Conference contribution
AN - SCOPUS:85050980767
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 400
EP - 410
BT - Proceedings - 2018 IEEE 38th International Conference on Distributed Computing Systems, ICDCS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 July 2018 through 5 July 2018
ER -