The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini memperkenalkan pendekatan heuristik dan pendekatan pembelajaran pengukuhan yang mendalam untuk menyelesaikan masalah penggunaan dan penjadualan fungsi rangkaian maya bersama dalam senario dinamik. Kami merumuskan masalah sebagai masalah pengoptimuman. Berdasarkan penerangan matematik masalah pengoptimuman, kami memperkenalkan tiga pendekatan heuristik dan pendekatan pembelajaran pengukuhan mendalam untuk menyelesaikan masalah. Kami mentakrifkan objektif untuk memaksimumkan nisbah permintaan kelewatan yang berpuas hati sambil meminimumkan purata kos sumber untuk senario dinamik. Dua pendekatan tamak kami yang diperkenalkan masing-masing dinamakan tamak masa tamat dan tamak sumber pengiraan. Dalam pendekatan tamak masa penamat, kami membuat setiap permintaan diselesaikan secepat mungkin walaupun kos sumbernya; dalam pendekatan tamak sumber pengiraan, kami membuat setiap permintaan menempati sesedikit mungkin sumber walaupun masa selesai. Pendekatan penyepuhlindapan simulasi kami yang diperkenalkan menjana penyelesaian yang boleh dilaksanakan secara rawak dan menumpu kepada penyelesaian anggaran. Dalam pendekatan berasaskan pembelajaran kami, rangkaian saraf dilatih untuk membuat keputusan. Kami menggunakan persekitaran simulasi untuk menilai prestasi pendekatan kami yang diperkenalkan. Keputusan berangka menunjukkan bahawa pendekatan pembelajaran peneguhan mendalam yang diperkenalkan mempunyai prestasi terbaik dari segi manfaat dalam kes kami yang diperiksa.
Zixiao ZHANG
Kyoto University
Eiji OKI
Kyoto University
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Salinan
Zixiao ZHANG, Eiji OKI, "Joint Virtual Network Function Deployment and Scheduling via Heuristics and Deep Reinforcement Learning" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 12, pp. 1424-1440, December 2023, doi: 10.1587/transcom.2023EBP3039.
Abstract: This paper introduces heuristic approaches and a deep reinforcement learning approach to solve a joint virtual network function deployment and scheduling problem in a dynamic scenario. We formulate the problem as an optimization problem. Based on the mathematical description of the optimization problem, we introduce three heuristic approaches and a deep reinforcement learning approach to solve the problem. We define an objective to maximize the ratio of delay-satisfied requests while minimizing the average resource cost for a dynamic scenario. Our introduced two greedy approaches are named finish time greedy and computational resource greedy, respectively. In the finish time greedy approach, we make each request be finished as soon as possible despite its resource cost; in the computational resource greedy approach, we make each request occupy as few resources as possible despite its finish time. Our introduced simulated annealing approach generates feasible solutions randomly and converges to an approximate solution. In our learning-based approach, neural networks are trained to make decisions. We use a simulated environment to evaluate the performances of our introduced approaches. Numerical results show that the introduced deep reinforcement learning approach has the best performance in terms of benefit in our examined cases.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2023EBP3039/_p
Salinan
@ARTICLE{e106-b_12_1424,
author={Zixiao ZHANG, Eiji OKI, },
journal={IEICE TRANSACTIONS on Communications},
title={Joint Virtual Network Function Deployment and Scheduling via Heuristics and Deep Reinforcement Learning},
year={2023},
volume={E106-B},
number={12},
pages={1424-1440},
abstract={This paper introduces heuristic approaches and a deep reinforcement learning approach to solve a joint virtual network function deployment and scheduling problem in a dynamic scenario. We formulate the problem as an optimization problem. Based on the mathematical description of the optimization problem, we introduce three heuristic approaches and a deep reinforcement learning approach to solve the problem. We define an objective to maximize the ratio of delay-satisfied requests while minimizing the average resource cost for a dynamic scenario. Our introduced two greedy approaches are named finish time greedy and computational resource greedy, respectively. In the finish time greedy approach, we make each request be finished as soon as possible despite its resource cost; in the computational resource greedy approach, we make each request occupy as few resources as possible despite its finish time. Our introduced simulated annealing approach generates feasible solutions randomly and converges to an approximate solution. In our learning-based approach, neural networks are trained to make decisions. We use a simulated environment to evaluate the performances of our introduced approaches. Numerical results show that the introduced deep reinforcement learning approach has the best performance in terms of benefit in our examined cases.},
keywords={},
doi={10.1587/transcom.2023EBP3039},
ISSN={1745-1345},
month={December},}
Salinan
TY - JOUR
TI - Joint Virtual Network Function Deployment and Scheduling via Heuristics and Deep Reinforcement Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 1424
EP - 1440
AU - Zixiao ZHANG
AU - Eiji OKI
PY - 2023
DO - 10.1587/transcom.2023EBP3039
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2023
AB - This paper introduces heuristic approaches and a deep reinforcement learning approach to solve a joint virtual network function deployment and scheduling problem in a dynamic scenario. We formulate the problem as an optimization problem. Based on the mathematical description of the optimization problem, we introduce three heuristic approaches and a deep reinforcement learning approach to solve the problem. We define an objective to maximize the ratio of delay-satisfied requests while minimizing the average resource cost for a dynamic scenario. Our introduced two greedy approaches are named finish time greedy and computational resource greedy, respectively. In the finish time greedy approach, we make each request be finished as soon as possible despite its resource cost; in the computational resource greedy approach, we make each request occupy as few resources as possible despite its finish time. Our introduced simulated annealing approach generates feasible solutions randomly and converges to an approximate solution. In our learning-based approach, neural networks are trained to make decisions. We use a simulated environment to evaluate the performances of our introduced approaches. Numerical results show that the introduced deep reinforcement learning approach has the best performance in terms of benefit in our examined cases.
ER -