Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

AdaSlicing: Adaptive Online Network Slicing under Continual Network Dynamics in Open Radio Access Networks

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read AdaSlicing claims that soft-isolated virtual resource sharing combined with per-slice Bayesian learning and an ADMM coordinator cuts operating cost by 64.2% and raises normalized slice performance by 45.5% versus the Atlas baseline.

desk verdict A real O-RAN testbed with a genuinely useful sharing idea, but the ADMM separability claim in Sec IV-C does not hold as written and the evaluation needs more statistical rigor. read the letter →

arxiv 2501.06943 v1 pith:TDD5TRCX submitted 2025-01-12 cs.NI

Ming Zhao , Yuru Zhang , Qiang Liu , Ahan Kak , Nakjung Choi This is my paper · ORCID
classification cs.NI
keywords networkslicingOpenRANO-RANonlineorchestrationBayesianoptimizationADMMvirtualizationresourceisolation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

AdaSlicing is a proposed system for online network slicing in O-RAN that learns to orchestrate virtual radio resources while the set of active slices, their traffic, and their service-level requirements keep changing. The paper's central claim is that coupling two ingredients—per-slice Bayesian learning agents coordinated by an ADMM capacities coordinator, and a new soft-isolated virtualization that shares unused virtual resource blocks among slices—makes the system both cheaper and better than existing hard-isolated orchestrators. On an O-RAN-compliant testbed with OpenAirInterface, Open5GS, and FlexRIC, AdaSlicing reports 64.2% lower total operating cost and 45.5% higher normalized slice performance than the Atlas baseline, with convergence in about five coordination iterations. The paper also shows the system re-adapts within one orchestration slot when a slice disconnects or reconnects, and when SLA thresholds change. This matters because current DNN-parameterized agents have fixed input and output spaces and must be retrained to cope with such continual dynamics.

What carries the argument

The load-bearing mechanism is the combination of three designed pieces. (1) Soft-isolated RAN virtualization: a vSharing layer estimates each slice's expected vRB need from its RLC buffers, pools the unused vRBs, and redistributes them proportionally to each slice's sharing weight SW, before virtual-to-physical mapping; this creates the performance coupling $s_i=\sum_{j\ne i} w_j$ inside each slice's performance function. (2) Per-slice constrained Bayesian optimization: each slice solves subproblem P5 with a Gaussian-process surrogate, a log-barrier penalty $\phi_i=-\log(-(Q_i-f(x_i,w_i\mid s_i)))$ for the SLA constraint, and a fixed-size prioritized reply buffer that decays experience priority by age to track non-stationary functions. (3) The ADMM coordinator: it solves problem P4, a convex quadratic integer program for the auxiliary variables $z_i^{(t)}$, and updates scaled dual variables $y_i^{(t+1)}=y_i^{(t)}+(x_i^{(t+1)}-z_i^{(t+1)})$, iterating with the agents until consensus. The paper treats the per-slice subproblem as fully separable and relies on this alternation to reach a convergent optima.

What would settle it

Run AdaSlicing with, say, ten active slices where all SWs are re-optimized in every coordination round, and record the ADMM primal residual $\|x^{(t)}-z^{(t)}\|$ across rounds; if the residual fails to decrease or the converged cost is no longer below the hard-isolated baselines, the separability assumption fails. A second check: fix $s_i$ to its previous round's value, as the paper's decomposition implicitly does, and compare against a variant that updates $s_i$ within each round; if the two diverge as slice count increases, the coupling is not negligible.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that continual network dynamics in RAN slicing do not require a monolithic retrained agent; the online orchestration problem can be decomposed and solved by an interaction between per-slice constrained Bayesian optimizers and a convex ADMM coordinator, provided the virtualization layer lets unused resources be shared. Each slice agent learns its own blackbox performance function $f(x_i, w_i \mid s_i)$ with a Gaussian-process surrogate and a gp-hedge acquisition strategy, while the coordinator solves a small convex integer program for auxiliary variables and updates dual variables. The soft-isolated virtualization introduces two new resources—svRBs (soft-isolated virtual resource blocks) and SWs (sharing weights)—so that slices with spare capacity contribute proportionally to a vRB pool and slices with overflow traffic draw from it. The paper reports that this combination, implemented on an end-to-end O-RAN testbed, achieves a total operating cost of 4.3 versus 12 for three baselines, with higher normalized performance (1.79 versus 1.22–1.23), and attributes the gain to the shared-resource mechanism rather than to fewer allocated svRBs.

Load-bearing premise

The load-bearing premise is that each slice's optimization can be treated as independent of the others, even though every slice's performance depends on the sharing weights of all other slices through $s_i$; the paper does not fix $s_i$ between iterations or prove ADMM convergence under that coupling, so the claimed convergent optima rests on an unproven separability assumption.

Editorial extensions

If this is right

  • An O-RAN near-RT RIC can orchestrate slices in near-real time with only about five ADMM and agent iterations per orchestration slot.
  • Unused virtual resources that hard isolation would leave idle are reallocated proportionally to sharing weights, raising throughput for active slices without extra svRB cost.
  • Slice arrivals, departures, and SLA threshold changes are absorbed within one to a few orchestration slots, because agents keep their GP experience buffers and the coordinator only re-solves a small convex program.
  • The system's gains on the testbed, 64.2% cost reduction and 45.5% normalized performance gain over Atlas, provide evidence that the AI and ML plus optimization decomposition is competitive with exhaustive search despite the latter using pre-collected datasets.
  • Heterogeneous learning agents, such as multi-armed bandits, can replace the Bayesian agents as long as they match the same action and state interface, so the architecture is extensible.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the soft-isolation idea transfers to multi-cell or multi-domain resources, the same svRB and SW mechanism could be applied to computing or fronthaul resources, where bursty demand is equally common; the paper only demonstrates it on one 10 MHz cell.
  • The separability assumption is the fragile spot: since a slice's SLA constraint contains $s_i$, the sum of other slices' SWs, the per-slice subproblem is not truly independent; one could test whether fixing $s_i$ at its previous ADMM iterate still converges when the number of slices grows well beyond three.
  • The prioritized replay buffer effectively makes each GP a forgetful learner; a natural extension would be to tune the priority-decay rate per slice, or to add change-point detection, to handle abrupt traffic regime shifts faster than the current age-based decay allows.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes AdaSlicing, a network slicing system for O-RAN that combines per-slice Bayesian learning agents with an ADMM-based coordinator, together with a soft-isolated RAN virtualization layer (svRBs and sharing weights). The system is implemented on a testbed using OpenAirInterface, Open5GS, FlexRIC, and USRP B210, and experiments show that AdaSlicing reduces operating cost by 64.2% and improves normalized slice performance by 45.5% compared to the Atlas baseline, while adapting to time-varying slice dynamics. The central claim is that the per-slice subproblems are separable and that the coordinator and agents converge to an optimum of the global problem.

Significance. If the claimed results hold, the paper would make a valuable empirical contribution: it demonstrates a full O-RAN testbed implementation of adaptive network slicing with soft resource isolation, and it shows that combining Bayesian learning with a distributed ADMM-style coordinator can work in real hardware. The strengths are the concrete system design, the soft-isolation mechanism with sharing weights, the use of a standard O-RAN stack, and a comparative evaluation against several baselines. However, the theoretical foundation of the decomposition is not established as stated, and the experimental evaluation lacks statistical rigor, so the significance of the quantitative claims is currently uncertain.

major comments (4)
  1. [Sec. IV-C, Eq. (19)] The claim that subproblem P2 is fully separable is not supported. The SLA constraint C1 (Eq. 5) is f(x_i, w_i | s_i) >= Q_i with s_i = sum_{j != i} w_j, so the feasibility of slice i's action depends on the SW values of all other slices. Introducing z_i in Sec. IV-A decouples only the capacity constraint C4 (Eq. 8), not the coupling inside C1. Since the per-slice problem P5 retains C1, the per-slice optimization is not independent. The paper neither fixes W between ADMM iterations nor provides a fixed-point or convergence analysis for the coupled update; therefore the statement in Sec. IV that the agents and coordinator 'eventually achieve a convergent optima' is not established by the mathematics.
  2. [Sec. IV-C, P6] In P6, the minimization is stated over {x_i, w_i, s_i}, but s_i = sum_{j != i} w_j is not a free variable—it is determined by other slices' actions. Listing s_i as a minimization variable makes the problem ill-posed and obscures the coupling. The paper should clarify whether s_i is treated as a constant from the previous iteration or in some other way, and then provide a rigorous analysis of the resulting iterative procedure, or explicitly state that the algorithm is a heuristic without optimality guarantees.
  3. [Sec. VII-A, Table II and Figs. 6–9] The quantitative claims (64.2% cost reduction, 45.5% performance improvement) are based on single trials with no error bars, confidence intervals, or repeated-run statistics. Since these are real network experiments on a hardware testbed, the reported numbers could reflect noise rather than systematic gains. At minimum, the paper should report the number of runs and the variance, or provide multiple traces to establish that the improvements are reproducible.
  4. [Sec. VII-A and Sec. VII-C] The comparison conflates two distinct contributions: the soft-isolated virtualization layer and the AdaOrch algorithm. All baselines (Atlas, GBO, ExSearch) use hard isolation, while AdaSlicing uses soft isolation. Consequently, the gains of AdaSlicing over Atlas cannot be attributed solely to the ADMM coordination or the Bayesian learning agents; a component ablation (e.g., AdaSlicing with hard isolation, or Atlas with soft isolation) is needed to separate the effect of the algorithm from the effect of the virtualization scheme.
minor comments (6)
  1. [Sec. IV-C] The phrase 'optimization toolboxs' contains a typo; it should be 'optimization toolboxes'.
  2. [Sec. IV-C, P6] The log-barrier penalty term is not fully defined: the parentheses in 'ϕi = −log(−(Qi − f (x(t) i , w(t) i |s(t) i ))' are unbalanced and there is a missing closing delimiter. Please correct the formula.
  3. [Sec. VII-A] The statement 'AdaSlicing achieves a fast convergence speed with only 5 iterations' is ambiguous; clarify whether 'iterations' refers to ADMM iterations, orchestration slots, or something else, and specify the convergence criterion used.
  4. [Sec. VII-C] In the caption of Fig. 10, 'the button figure' should be corrected to 'the bottom figure'.
  5. [Table II] The definition of normalized performance is unclear when multiple metrics (throughput and FPS) are averaged. Please specify how the multi-dimensional metric is aggregated into the reported normalized value.
  6. [Sec. V, Fig. 4] The phrase 'grounded to be 2 svRBs' appears to be a typo; it should be 'rounded to 2 svRBs'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: AdaSlicing's gains are testbed measurements, and the GP/ADMM components are not used to fabricate the reported outcomes.

full rationale

AdaSlicing's headline results are measured on a real O-RAN testbed, not produced by the Gaussian-process surrogate or the ADMM coordinator as fitted outputs. The GP guides action selection online, but Table II and Figs. 6-16 report actual throughput/FPS and actual svRB/SW usage after convergence; no reported 'prediction' is the surrogate's own estimate. The soft-isolation sharing rule is implemented and then verified (e.g., SW 0.2 receiving 40% of unused svRBs), which is a mechanism sanity check rather than a derived claim. The main rigor concern is the Sec IV-C separability claim: constraint C1 couples slices through s_i = sum_{j!=i} w_j, and P6 lists s_i as a minimization variable even though it is exogenous to slice i; the paper provides no convergence proof for this coupled ADMM-like iteration. That is a correctness/omitted-proof issue, not a circularity, because it does not make any measured claim equal to an input by construction. The Atlas baseline is prior work by two of the authors [5], but it functions as an external comparison system rather than as load-bearing evidence for AdaSlicing's derivation. Hence no circular step is present.

Assumptions & free parameters 5 free parameters · 6 assumptions · 3 invented entities

The central claim depends on several unstated modeling choices and unproven convergence assumptions. The most significant are the incorrectly asserted separability of slice subproblems, the unproven ADMM convergence for nonconvex Bayesian optimization, and the soft-isolation assumption that sharing unused resources does not break SLA assurances.

free parameters (5)
  • ADMM penalty parameter rho
    Positive constant in the augmented Lagrangian (Eq. 13). Its value is not reported; it controls the convergence speed and the balance between cost and consensus in the coordinator.
  • GP kernel hyperparameters
    Matern kernel is used (Sec VII), but length-scale and variance settings are not reported; they come from scikit-optimize defaults and affect sample efficiency.
  • Reply buffer size and priority decay
    Fixed-size reply buffer with age-of-information priority decay (Sec IV-C); no sizes or decay rates are given, yet they determine how quickly the GP tracks time-varying performance.
  • Cost weights UH, US = 1, 1
    Unit costs of svRB and SW in the cost model (Eq. 3); chosen equal in experiments, which affects the cost comparison between AdaSlicing and baselines.
  • SLA thresholds Qi = 12 Mbps, 10 FPS
    Performance thresholds for all slices in Sec VII; chosen by the authors and directly influence how many resources each system needs.
assumptions (6)
  • domain assumption The slice performance function f(x_i, w_i | s_i) is a smooth blackbox that a GP with Matern kernel can model.
    Used in Sec IV-C to justify the GP surrogate and acquisition function; no validation that the Matern kernel matches actual performance surfaces.
  • ad hoc to paper The subproblem P2 is fully separable across slices.
    Sec IV-C states this, but constraint C1 includes s_i = sum_{j != i} w_j, coupling all slices; the paper does not fix W or prove convergence under coupling.
  • domain assumption The alternating ADMM updates converge to a 'convergent optima' for the nonconvex problem.
    Sec IV (first paragraph) asserts convergence without proof; ADMM convergence guarantees require convexity, which the Bayesian optimization subproblems do not satisfy.
  • domain assumption The 'expectantly needed vRBs' can be estimated from per-user RLC buffers.
    Sec V uses RLC buffer aggregation to determine unused vRBs; this assumes the buffer level predicts actual scheduling demand.
  • domain assumption Sharing unused vRBs proportionally to SW preserves SLA fulfillment for all slices.
    Sec V introduces soft isolation, which the paper admits creates 'performance interdependence' among slices; no test shows worst-case SLA preservation under contention.
  • domain assumption A single base station is representative; multi-BS extension is straightforward.
    Sec III states 'without loss of generality' but the coordinator capacity constraint (C4) becomes more complex with multiple BSs.
invented entities (3)
  • svRB (soft-isolated virtual resource block)
    purpose: A virtual resource block that can be shared with other slices when underused.
    New resource abstraction introduced in Sec V; its benefits are demonstrated only in this paper's testbed, with no external validation.
  • SW (sharing weight)
    purpose: A weight that determines the proportional share of the unused vRB pool each slice receives.
    Introduced in Sec V as a new orchestration variable; its isolation and fairness properties are asserted, not externally validated.
  • vSharing layer
    purpose: A software layer before virtual-to-physical mapping that redistributes unused vRBs among slices.
    New architectural component in Sec V; no external implementation or benchmark exists.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AdaSlicing: Adaptive Online Network Slicing under Continual Network Dynamics in Open Radio Access Networks." pith.science (2026). https://pith.science/paper/TDD5TRCX

@misc{pith2026250106943,
  author       = {Pith},
  title        = {Pith review of: AdaSlicing: Adaptive Online Network Slicing under Continual Network Dynamics in Open Radio Access Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TDD5TRCX}},
  note         = {Machine review of arXiv:2501.06943}
}
read the original abstract

Open radio access networks (e.g., O-RAN) facilitate fine-grained control (e.g., near-RT RIC) in next-generation networks, necessitating advanced AI/ML techniques in handling online resource orchestration in real-time. However, existing approaches can hardly adapt to time-evolving network dynamics in network slicing, leading to significant online performance degradation. In this paper, we propose AdaSlicing, a new adaptive network slicing system, to online learn to orchestrate virtual resources while efficiently adapting to continual network dynamics. The AdaSlicing system includes a new soft-isolated RAN virtualization framework and a novel AdaOrch algorithm. We design the AdaOrch algorithm by integrating AI/ML techniques (i.e., Bayesian learning agents) and optimization methods (i.e., the ADMM coordinator). We design the soft-isolated RAN virtualization to improve the virtual resource utilization of slices while assuring the isolation among virtual resources at runtime. We implement AdaSlicing on an O-RAN compliant network testbed by using OpenAirInterface RAN, Open5GS Core, and FlexRIC near-RT RIC, with Ettus USRP B210 SDR. With extensive network experiments, we demonstrate that AdaSlicing substantially outperforms state-of-the-art works with 64.2% cost reduction and 45.5% normalized performance improvement, which verifies its high adaptability, scalability, and assurance.

Figures

Figures reproduced from arXiv: 2501.06943 by the authors.

Figure 1
Figure 1. The overview of AdaSlicing. demonstrate that AdaSlicing can reduce 64.2% total operating cost while improving 45.5% normalized performance of slices, as compared to state-of-the-art solutions. Overall, we propose AdaSlicing, a new adaptive online network slicing system, that can flexibly adapt to diverse time￾varying network dynamics. The detailed contributions are: • We design a new soft-isolated RAN virtualization… view at source ↗
Figure 2
Figure 2. An example of runtime utilization of vRBs under different applications. Here, we run mixed applications before 400s, only watch live video in [400s, 600s], and then perform speedtest. sampled more frequently. If the reply buffer is full, the oldest experience will be removed, which helps to track the time￾evolving performance function of slices. In the meantime, the fixed size of the reply buffer ensures that the co… view at source ↗
Figure 5
Figure 5. The overview of AdaSlicing testbed. Thus, we can obtain the final number of vRBs for all slices, including 1) for slices with overflowed traffic: summing up their orchestrated svRBs and the shared unused vRBs; 2) for slices without overflowed traffic: their expectantly needed vRBs. Finally, the virtual-to-physical mapping will be invoked to map the final vRBs of slices to physical resources (i.e., PRBs and RBGs). VI… view at source ↗
Figures from the paper (5 more)
Figure 6
Figure 6. Figure 6: The convergence of total cost under systems. 0 2 4 6 8 10 12 14 Orchestration slots 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 Normalized performance GBO ExSearch Atlas AdaSlicing [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 9
Figure 9. Figure 9: The convergence of detailed variables. generate the optimal orchestration action in each orchestration slot. Moreover, we design to reuse previous experiences in individual learning agents to accelerate their convergence in later orchestration slots, which can be obser…
Figure 12
Figure 12. Figure 12: The total cost under different number of slices. Slice disconnected Slice reconnected [PITH_FULL_IMAGE:figures/full_fig_p008_12.png]
Figure 15
Figure 15. Figure 15: The total cost under changing slice demands. Request higher performance Request lower performance [PITH_FULL_IMAGE:figures/full_fig_p009_15.png]
Figure 16
Figure 16. Figure 16: The normalized performance under changing slice demands. of slices, its total cost cannot be adapted to time-varying network dynamics. In addition, [PITH_FULL_IMAGE:figures/full_fig_p009_16.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DORA: Dynamic O-RAN Resource Allocation for Multi-Slice 5G Networks

    cs.NI 2025-09 reject novelty 3.0 of 10

    DORA applies PPO reinforcement learning to slice-level PRB allocation in an OAI-based Open RAN testbed and reports balanced, not best-per-metric, performance versus simple baselines.

Reference graph

Works this paper leans on

40 extracted references · 29 canonical work pages · cited by 1 Pith paper

  1. [1]

    5g nr-v2x: Toward connected and cooperative autonomous driving,

    H. Bagheri, M. Noor-A-Rahim, Z. Liu, H. Lee, D. Pesch, K. Moessner, and P. Xiao, “5g nr-v2x: Toward connected and cooperative autonomous driving,” IEEE Communications Standards Magazine , vol. 5, no. 1, pp. 48–54, 2021

    work page 2021

  2. [2]

    Deepmix: mobility- aware, lightweight, and hybrid 3d object detection for headsets,

    Y . Guan, X. Hou, N. Wu, B. Han, and T. Han, “Deepmix: mobility- aware, lightweight, and hybrid 3d object detection for headsets,” in Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services , ser. MobiSys ’22. New York, NY , USA: Association for Computing Machinery, 2022, p. 28–41. [Online]. Available: https://...

    arXiv 2022

  3. [3]

    Edge computing in industrial internet of things: Architecture, advances and challenges,

    T. Qiu, J. Chi, X. Zhou, Z. Ning, M. Atiquzzaman, and D. O. Wu, “Edge computing in industrial internet of things: Architecture, advances and challenges,” IEEE Communications Surveys & Tutorials , vol. 22, no. 4, pp. 2462–2488, 2020

    work page 2020

  4. [4]

    OnSlicing: online end-to-end net- work slicing with reinforcement learning,

    Q. Liu, N. Choi, and T. Han, “OnSlicing: online end-to-end net- work slicing with reinforcement learning,” in Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, 2021, pp. 141–153

    work page 2021

  5. [5]

    Atlas: automate online service configuration in network slicing,

    ——, “Atlas: automate online service configuration in network slicing,” in Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies , 2022, pp. 140–155

    work page 2022

  6. [6]

    How should i slice my network? a multi-service empirical evaluation of resource sharing efficiency,

    C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Perez, “How should i slice my network? a multi-service empirical evaluation of resource sharing efficiency,” in Proceedings of the 24th Annual International Conference on Mobile Computing and Networking , 2018, pp. 191–206

    work page 2018

  7. [7]

    Adapting wireless mesh network con- figuration from simulation to reality via deep learning based domain adaptation,

    J. Shi, M. Sha, and X. Peng, “Adapting wireless mesh network con- figuration from simulation to reality via deep learning based domain adaptation,” in NSDI, 2021, pp. 887–901

    work page 2021

  8. [8]

    Overbooking network slices through yield-driven end- to-end orchestration,

    J. X. Salvat, L. Zanzi, A. Garcia-Saavedra, V . Sciancalepore, and X. Costa-Perez, “Overbooking network slices through yield-driven end- to-end orchestration,” in Proceedings of the 14th International Confer- ence on emerging Networking EXperiments and Technologies, 2018, pp. 353–365

    work page 2018

Show all 40 references
  1. [9]

    O-ran alliance,

    O.-R. ALLIANCE, “O-ran alliance,” June 2022 [Online]. [Online]. Available: https://www.o-ran.org/

    2022

  2. [10]

    Un- derstanding o-ran: Architecture, interfaces, algorithms, security, and research challenges,

    M. Polese, L. Bonati, S. D’Oro, S. Basagni, and T. Melodia, “Un- derstanding o-ran: Architecture, interfaces, algorithms, security, and research challenges,” IEEE Communications Surveys & Tutorials , 2023

    2023

  3. [11]

    UNEXT – A unified networking experience,

    S. Azimeh, V . Csaba, and G. Markus, “UNEXT – A unified networking experience,” Nokia Bell Labs, White Paper, 2023

    2023

  4. [12]

    Deep learning,

    Y . LeCun, Y . Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015

    2015

  5. [13]

    Continuous control with deep reinforcement learning,

    T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y . Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015

    2015 arXiv

  6. [14]

    Learning scheduling algorithms for data processing clusters,

    H. Mao, M. Schwarzkopf, S. B. Venkatakrishnan, Z. Meng, and M. Al- izadeh, “Learning scheduling algorithms for data processing clusters,” in Proceedings of the ACM special interest group on data communication , 2019, pp. 270–288

    2019

  7. [15]

    Distributed optimization and statistical learning via the alternating direction method of multipliers,

    S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein et al., “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine learning , vol. 3, no. 1, pp. 1–122, 2011

    2011

  8. [16]

    CVXPY: A Python-embedded modeling lan- guage for convex optimization,

    S. Diamond and S. Boyd, “CVXPY: A Python-embedded modeling lan- guage for convex optimization,” Journal of Machine Learning Research, vol. 17, no. 83, pp. 1–5, 2016

    2016

  9. [17]

    A rewriting system for convex optimization problems,

    A. Agrawal, R. Verschueren, S. Diamond, and S. Boyd, “A rewriting system for convex optimization problems,” Journal of Control and Decision, vol. 5, no. 1, pp. 42–60, 2018

    2018

  10. [18]

    Convex optimization, 25 cambridge university press,

    S. Boyd and L. Vandenberghe, “Convex optimization, 25 cambridge university press,” Cambridge, England, 2010

    2010

  11. [19]

    A tutorial on bayesian optimization,

    P. I. Frazier, “A tutorial on bayesian optimization,” arXiv preprint arXiv:1807.02811, 2018

    2018 arXiv

  12. [20]

    Practical bayesian optimiza- tion of machine learning algorithms,

    J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimiza- tion of machine learning algorithms,” Advances in neural information processing systems, vol. 25, 2012

    2012

  13. [21]

    Gaussian processes in machine learning,

    C. E. Rasmussen, “Gaussian processes in machine learning,” in Summer school on machine learning . Springer, 2003, pp. 63–71

    2003

  14. [22]

    A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning,

    E. Brochu, V . M. Cora, and N. De Freitas, “A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning,” arXiv preprint arXiv:1012.2599, 2010

    2010 arXiv

  15. [23]

    Gaussian processes for regression,

    C. Williams and C. Rasmussen, “Gaussian processes for regression,” Advances in neural information processing systems , vol. 8, 1995

    1995

  16. [24]

    No-past-bo: Normalized portfolio allocation strategy for bayesian op- timization,

    T. d. P. Vasconcelos, D. A. de Souza, C. L. Mattos, and J. P. Gomes, “No-past-bo: Normalized portfolio allocation strategy for bayesian op- timization,” in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) . IEEE, 2019, pp. 561–568

    2019

  17. [25]

    Portfolio allocation for bayesian optimization,

    M. Hoffman, E. Brochu, N. De Freitas et al. , “Portfolio allocation for bayesian optimization,” in UAI, 2011, pp. 327–336

    2011

  18. [26]

    Flexric: An sdk for next- generation sd-rans,

    R. Schmidt, M. Irazabal, and N. Nikaein, “Flexric: An sdk for next- generation sd-rans,” in Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies, 2021, pp. 411– 425

    2021

  19. [27]

    Virtualedge: Multi-domain resource orchestra- tion and virtualization in cellular edge computing,

    Q. Liu and T. Han, “Virtualedge: Multi-domain resource orchestra- tion and virtualization in cellular edge computing,” in Proceedings of IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019, pp. 1051–1060

    2019

  20. [28]

    Hexric: Building a better near-real time network controller for the open ran ecosystem,

    V .-Q. Pham, H.-T. Thieu, A. Kak, and N. Choi, “Hexric: Building a better near-real time network controller for the open ran ecosystem,” in Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications , 2023, pp. 15–21

    2023

  21. [29]

    5G; Management and orchestration; 5G Network Resource Model (NRM),

    3rd Generation Partnership Project (3GPP), “5G; Management and orchestration; 5G Network Resource Model (NRM),” Technical Speci- fication (TS) 28.541, May 2024, release 18.7.0

    2024

  22. [30]

    [Online]

    OpenAirInterface Software Alliance. [Online]. Available: https://gitlab. eurecom.fr/oai/openairinterface5g

  23. [31]

    [Online]

    Open5GS. [Online]. Available: https://github.com/open5gs/open5gs

  24. [32]

    Scikit-learn: Machine learning in Python,

    F. Pedregosa, G. Varoquaux, A. Gramfort, V . Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V . Dubourg, J. Vander- plas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duch- esnay, “Scikit-learn: Machine learning in Python,” Journal of Machine ...

    2011

  25. [33]

    Aweran: Making a case for application-aware radio access network slicing,

    A. Kak, H.-T. Thieu, V .-Q. Pham, R. K. Sheshadri, N. Choi, Y . Guan, M. Yin, and T. Han, “Aweran: Making a case for application-aware radio access network slicing,” in Proceedings of the 17th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characteriza- t...

    2023

  26. [34]

    Colo- ran: Developing machine learning-based xapps for open ran closed-loop control on programmable experimental platforms,

    M. Polese, L. Bonati, S. D’Oro, S. Basagni, and T. Melodia, “Colo- ran: Developing machine learning-based xapps for open ran closed-loop control on programmable experimental platforms,” IEEE Transactions on Mobile Computing , vol. 22, no. 10, pp. 5787–5800, 2022

    2022

  27. [35]

    Edgeslice: Slicing wireless edge computing network with decentralized deep reinforcement learning,

    Q. Liu, T. Han, and E. Moges, “Edgeslice: Slicing wireless edge computing network with decentralized deep reinforcement learning,” in Proceedings of IEEE 40th International Conference on Distributed Computing Systems (ICDCS) , 2020, pp. 234–244

    2020

  28. [36]

    Scalo-ran: Energy-aware network intelligence scaling in open ran,

    S. Maxenti, S. D’Oro, L. Bonati, M. Polese, A. Capone, and T. Melodia, “Scalo-ran: Energy-aware network intelligence scaling in open ran,” arXiv preprint arXiv:2312.05096 , 2023

    2023 arXiv

  29. [37]

    Neutran: An open ran neutral host architecture for zero-touch ran and spectrum sharing,

    L. Bonati, M. Polese, S. D’Oro, S. Basagni, and T. Melodia, “Neutran: An open ran neutral host architecture for zero-touch ran and spectrum sharing,” IEEE Transactions on Mobile Computing , 2023

    2023

  30. [38]

    Orchestran: Orchestrat- ing network intelligence in the open ran,

    S. D’Oro, L. Bonati, M. Polese, and T. Melodia, “Orchestran: Orchestrat- ing network intelligence in the open ran,” IEEE Transactions on Mobile Computing, 2023

    2023

  31. [39]

    Onrl: improving mobile video telephony via online reinforcement learning,

    H. Zhang, A. Zhou, J. Lu, R. Ma, Y . Hu, C. Li, X. Zhang, H. Ma, and X. Chen, “Onrl: improving mobile video telephony via online reinforcement learning,” in MobiCom, 2020, pp. 1–14

    2020

  32. [40]

    Fast and scalable network slicing by integrating deep learning with lagrangian methods,

    T. Hu, Q. Liao, Q. Liu, A. Massaro, and G. Carle, “Fast and scalable network slicing by integrating deep learning with lagrangian methods,” in GLOBECOM 2023-2023 IEEE Global Communications Conference . IEEE, 2023, pp. 6346–6351

    2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.