Tootfinder

This https://arxiv.org/abs/2501.16143 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Disruption-aware Microservice Re-orchestration for Cost-efficient Multi-cloud Deployments
Multi-cloud environments enable a cost-efficient scaling of cloud-native applications across geographically distributed virtual nodes with different pricing models. In this context, the resource fragmentation caused by frequent changes in the resource demands of deployed microservices, along with the allocation or termination of new and existing microservices, increases the deployment cost. Therefore, re-orchestrating deployed microservices on a cheaper configuration of multi-cloud nodes offers…

Adversarial Training: Enhancing Out-of-Distribution Generalization for Learning Wireless Resource Allocation
Shengjie Liu, Chenyang Yang
https://arxiv.org/abs/2506.21208

Adversarial Training: Enhancing Out-of-Distribution Generalization for Learning Wireless Resource Allocation
Deep neural networks (DNNs) have widespread applications for optimizing resource allocation. Yet, their performance is vulnerable to distribution shifts between training and test data, say channels. In this letter, we resort to adversarial training (AT) for enhancing out-of-distribution (OOD) generalizability of DNNs trained in unsupervised manner. We reformulate AT to capture the OOD degradation, and propose a one-step gradient ascent method for AT. The proposed method is validated by optimizi…

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
Gurusha Juneja, Alon Albalak, Wenyue Hua, William Yang Wang
https://arxiv.org/abs/2506.20737

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
The proliferation of LLM-based agents has led to increasing deployment of inter-agent collaboration for tasks like scheduling, negotiation, resource allocation etc. In such systems, privacy is critical, as agents often access proprietary tools and domain-specific databases requiring strict confidentiality. This paper examines whether LLM-based agents demonstrate an understanding of contextual privacy. And, if instructed, do these systems preserve inference time user privacy in non-adversarial m…

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro
https://arxiv.org/abs/2506.18748

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We demonstrate how a state-augmented graph neural network (GNN) parametrization for the resource allocation policy circumvents the drawbacks of the ubiquitous dual subgradient methods by representing the network configurations (or states) as graphs and viewing dual variables as dynamic inputs t…

Leveraging Relational Evidence: Population Size Estimation on Tree-Structured Data with the Weighted Multiplier Method
Mallory J Flynn, Paul Gustafson
https://arxiv.org/abs/2506.21020

Leveraging Relational Evidence: Population Size Estimation on Tree-Structured Data with the Weighted Multiplier Method
Populations of interest are often hidden from data for a variety of reasons, though their magnitude remains important in determining resource allocation and appropriate policy. One popular approach to population size estimation, the multiplier method, is a back-calculation tool requiring only a marginal subpopulation size and an estimate of the proportion belonging to this subgroup. Another approach is to use Bayesian methods, which are inherently well-suited to incorporating multiple data sour…

RL-Driven Semantic Compression Model Selection and Resource Allocation in Semantic Communication Systems
Xinyi Lin, Peizheng Li, Adnan Aijaz
https://arxiv.org/abs/2506.18660

RL-Driven Semantic Compression Model Selection and Resource Allocation in Semantic Communication Systems
Semantic communication (SemCom) is an emerging paradigm that leverages semantic-level understanding to improve communication efficiency, particularly in resource-constrained scenarios. However, existing SemCom systems often overlook diverse computational and communication capabilities and requirements among different users. Motivated by the need to adaptively balance semantic accuracy, latency, and energy consumption, this paper presents a reinforcement learning (RL)-driven framework for semant…

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving
Heyang Huang, Cunchen Hu, Jiaqi Zhu, Ziyuan Gao, Liangliang Xu, Yizhou Shan, Yungang Bao, Sun Ninghui, Tianwei Zhang, Sa Wang
https://arxiv.org/abs/2506.13497

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving
The Text-to-Video (T2V) model aims to generate dynamic and expressive videos from textual prompts. The generation pipeline typically involves multiple modules, such as language encoder, Diffusion Transformer (DiT), and Variational Autoencoders (VAE). Existing serving systems often rely on monolithic model deployment, while overlooking the distinct characteristics of each module, leading to inefficient GPU utilization. In addition, DiT exhibits varying performance gains across different resoluti…

A Vision for Trustworthy, Fair, and Efficient Socio-Technical Control using Karma Economies
Ezzat Elokda, Andrea Censi, Emilio Frazzoli, Florian D\"orfler, Saverio Bolognani
https://arxiv.org/abs/2506.17115

A Vision for Trustworthy, Fair, and Efficient Socio-Technical Control using Karma Economies
Control systems will play a pivotal role in addressing societal-scale challenges as they drive the development of sustainable future smart cities. At the heart of these challenges is the trustworthy, fair, and efficient allocation of scarce public resources, including renewable energy, transportation, data, computation, etc.. Historical evidence suggests that monetary control -- the prototypical mechanism for managing resource scarcity -- is not always well-accepted in socio-technical resource …

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Libo Zhang, Yang Chen, Toru Takisaka, Kaiqi Zhao, Weidong Li, Jiamou Liu
https://arxiv.org/abs/2506.14125

Situational-Constrained Sequential Resources Allocation via Reinforcement Learning
Sequential Resource Allocation with situational constraints presents a significant challenge in real-world applications, where resource demands and priorities are context-dependent. This paper introduces a novel framework, SCRL, to address this problem. We formalize situational constraints as logic implications and develop a new algorithm that dynamically penalizes constraint violations. To handle situational constraints effectively, we propose a probabilistic selection mechanism to overcome li…

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Majumder Haider, Imtiaz Ahmed, Zoheb Hassan, Kamrul Hasan, H. Vincent Poor
https://arxiv.org/abs/2506.18293

LLM-Integrated Digital Twins for Hierarchical Resource Allocation in 6G Networks
Next-generation (NextG) wireless networks are expected to require intelligent, scalable, and context-aware radio resource management (RRM) to support ultra-dense deployments, diverse service requirements, and dynamic network conditions. Digital twins (DTs) offer a powerful tool for network management by creating high-fidelity virtual replicas that model real-time network behavior, while large language models (LLMs) enhance decision-making through their advanced generalization and contextual rea…

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2506.04947

Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents
We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perceptions. To accommodate this, our model accounts for agents whose preferences deviate from traditional expected utility theory (EUT), specifically incorporating cumulative prospect theory (CPT) preferences. We develop a comprehensive analytical framework that captures human-centric aspects of decision-making and risky…

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Dejun Xu, Jijia Chen, Gary G. Yen, Min Jiang
https://arxiv.org/abs/2506.06362

CR-BLEA: Contrastive Ranking for Adaptive Resource Allocation in Bilevel Evolutionary Algorithms
Bilevel optimization poses a significant computational challenge due to its nested structure, where each upper-level candidate solution requires solving a corresponding lower-level problem. While evolutionary algorithms (EAs) are effective at navigating such complex landscapes, their high resource demands remain a key bottleneck -- particularly the redundant evaluation of numerous unpromising lower-level tasks. Despite recent advances in multitasking and transfer learning, resource waste persis…

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Rajpreet Singh, Novak Bo\v{s}kov, Aditya Gudal, Manzoor A. Khan
https://arxiv.org/abs/2506.01571

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Resource allocation and scheduling are a common problem in various distributed systems. Although widely studied, the state-of-the-art solutions either do not scale or lack the expressive power to capture the most complex instances of the problem. To that end, we present a mathematical framework for hypergraph ranking and analysis, unifying graph theory, lattice theory, and semantic analysis. In our fundamental theorem, we prove the existence of partial order on entities of hypergraphs, extendin…

This https://arxiv.org/abs/2403.16971 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOS_…

AIOS: LLM Agent Operating System
LLM-based intelligent agents face significant deployment challenges, particularly related to resource management. Allowing unrestricted access to LLM or tool resources can lead to inefficient or even potentially harmful resource allocation and utilization for agents. Furthermore, the absence of proper scheduling and resource management mechanisms in current agent designs hinders concurrent processing and limits overall system efficiency. As the diversity and complexity of agents continue to gro…

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
Jer Shyuan Ng, Aditya Pribadi Kalapaaking, Xiaoyu Xia, Dusit Niyato, Ibrahim Khalil, Iqbal Gondal
https://arxiv.org/abs/2506.18259

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data
In recent years, Federated Learning (FL) has emerged as a widely adopted privacy-preserving distributed training approach, attracting significant interest from both academia and industry. Research efforts have been dedicated to improving different aspects of FL, such as algorithm improvement, resource allocation, and client selection, to enable its deployment in distributed edge networks for practical applications. One of the reasons for the poor FL model performance is due to the worker dropou…

This https://arxiv.org/abs/2204.07330 has been replaced.
link: https://scholar.google.com/scholar?q=a

Differentially Private Distributed Mismatch Tracking Algorithm for Constraint-Coupled Resource Allocation Problems
This paper considers privacy-concerned distributed constraint-coupled resource allocation problems over an undirected network, where each agent holds a private cost function and obtains the solution via only local communication. With privacy concerns, we mask the exchanged information with independent Laplace noise against a potential attacker with potential access to all network communications. We propose a differentially private distributed mismatch tracking algorithm (diff-DMAC) to achieve c…

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
Symeon Vaidanis, Photios A. Stavrou, Marios Kountouris
https://arxiv.org/abs/2506.04952

Optimization for Semantic-Aware Resource Allocation under CPT-based Utilities
The problem of resource allocation in goal-oriented semantic communication with semantic-aware utilities and subjective risk perception is studied here. By linking information importance to risk aversion, we model agent behavior using Cumulative Prospect Theory (CPT), which incorporates risk-sensitive utility functions and nonlinear transformations of distributions, reflecting subjective perceptions of gains and losses. The objective is to maximize the aggregate utility across multiple CPT-mode…

The Secure Overview and Analysis OF 3GPP MAC CE
Jin Cao, Yuanyuan Yang, Ruhui Ma, Sheng Li, Hui Li
https://arxiv.org/abs/2506.09502 https://

The Secure Overview and Analysis OF 3GPP MAC CE
To more effectively control and allocate network resources, MAC CE has been introduced into the network protocol, which is a type of control signaling located in the MAC layer. Since MAC CE lacks encryption and integrity protection mechanisms provided by PDCP, the control signaling carried by MAC CE is vulnerable to interception or tampering by attackers during resource scheduling and allocation. Currently, the 3GPP has analyzed the security risks of Layer 1/Layer 2 Triggered Mobility (LTM), wh…

Diffusion-RL for Scalable Resource Allocation for 6G Networks
Salar Nouri, Mojdeh Karbalaee Motalleb, Vahid Shah-Mansouri
https://arxiv.org/abs/2506.07880 …

Diffusion-RL for Scalable Resource Allocation for 6G Networks
This paper presents a novel approach to resource allocation in Open Radio Access Networks (O-RAN), leveraging a Generative AI technique with network slicing to address the diverse demands of 5G and 6G service types such as Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and Massive Machine-Type Communications (mMTC). Additionally, we provide a comprehensive analysis and comparison of machine learning (ML) techniques for resource allocation within O-RAN, eval…

This https://arxiv.org/abs/2310.03475 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Fair Division with Allocator's Preference
We study the fair allocation of indivisible resources among agents. Most prior work focuses on fairness and/or efficiency among agents. However, the allocator, as the resource owner, may also be involved in many scenarios (e.g., government resource allocation, heritage division, company personnel assignment, etc). The allocator inclines to obtain a fair or efficient allocation based on her preference over the items and to whom each item is allocated. We propose a model and study two problems: 1…

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Chuanchao Gao, Niraj Kumar, Arvind Easwaran
https://arxiv.org/abs/2506.12686

Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies.…

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Gabriel Aracena, Kyle Luster, Fabio Santos, Igor Steinmacher, Marco A. Gerosa
https://arxiv.org/abs/2506.00128

Applying Large Language Models to Issue Classification: Revisiting with Extended Data and New Models
Effective prioritization of issue reports in software engineering helps to optimize resource allocation and information recovery. However, manual issue classification is laborious and lacks scalability. As an alternative, many open source software (OSS) projects employ automated processes for this task, yet this method often relies on large datasets for adequate training. Traditionally, machine learning techniques have been used for issue classification. More recently, large language models (LL…

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications
Minwoo Kim, Youngchol Choi, Yeongjun Kim, Eojin Seo, Hyun Jong Yang
https://arxiv.org/abs/2506.13008

Joint Spectrum Sensing and Resource Allocation for OFDMA-based Underwater Acoustic Communications
Underwater acoustic (UWA) communications generally rely on cognitive radio (CR)-based ad-hoc networks due to challenges such as long propagation delay, limited channel resources, and high attenuation. To address the constraints of limited frequency resources, UWA communications have recently incorporated orthogonal frequency division multiple access (OFDMA), significantly enhancing spectral efficiency (SE) through multiplexing gains. Still, {the} low propagation speed of UWA signals, combined w…

Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management
Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo
https://arxiv.org/abs/2506.05693

Resilient Auto-Scaling of Microservice Architectures with Efficient Resource Management
Horizontal Pod Auto-scalers (HPAs) are crucial for managing resource allocation in microservice architectures to handle fluctuating workloads. However, traditional HPAs fail to address resource disruptions caused by faults, cyberattacks, maintenance, and other operational challenges. These disruptions result in resource wastage, service unavailability, and HPA performance degradation. To address these challenges, we extend our prior work on Smart HPA and propose SecureSmart HPA, which offers re…

Resource Allocation for Pinching-Antenna Systems: State-of-the-Art, Key Techniques and Open Issues
Ming Zeng, Ji Wang, Octavia A. Dobre, Zhiguo Ding, George K. Karagiannidis, Robert Schober, H. Vincent Poor
https://arxiv.org/abs/2506.06156

Resource Allocation for Pinching-Antenna Systems: State-of-the-Art, Key Techniques and Open Issues
Pinching antennas have emerged as a promising technology for reconfiguring wireless propagation environments, particularly in high-frequency communication systems operating in the millimeter-wave and terahertz bands. By enabling dynamic activation at arbitrary positions along a dielectric waveguide, pinching antennas offer unprecedented channel reconfigurability and the ability to provide line-of-sight (LoS) links in scenarios with severe LoS blockages. The performance of pinching-antenna syste…

Replaced article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets
Zhiyu Shao, Qiong Wu, Pingyi Fan, Nan Cheng, Qiang Fan, Jiangzhou Wang

Epidemic dynamics in physical-information-social multilayer networks
Mengshou Wang, Liangrong Peng, Baoguo Jia, Liu Hong
https://arxiv.org/abs/2506.00104 h…

Epidemic dynamics in physical-information-social multilayer networks
During epidemic outbreaks, information dissemination enhances individual protection, while social institutions influence the transmission through measures like government interventions, media campaigns, and hospital resource allocation. Here we develop a tripartite physical-information-social epidemic model and derive the corresponding kinetic equations in different scales by using the Microscopic Markov Chain Approach and mean-field approximations. The basic reproduction number and epidemic th…

Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Anup Mishra, \v{C}edomir Stefanovi\'c, Xiuqiang Xu, Petar Popovski, Israel Leyva-Mayorga
https://arxiv.org/abs/2506.15273

Reinforcement Learning-Based Policy Optimisation For Heterogeneous Radio Access
Flexible and efficient wireless resource sharing across heterogeneous services is a key objective for future wireless networks. In this context, we investigate the performance of a system where latency-constrained internet-of-things (IoT) devices coexist with a broadband user. The base station adopts a grant-free access framework to manage resource allocation, either through orthogonal radio access network (RAN) slicing or by allowing shared access between services. For the IoT users, we propos…

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
Tao Yu, Simin Wang, Shunqing Zhang, Xiaojing Chen, Zi Xu, Xin Wang, Jiandong Li, Junyu Liu, Sihai Zhang
https://arxiv.org/abs/2506.03508

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks
The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or dat…

Refined Metrics, Sensing Limits, and Resource Allocation in OTFS-RSMA LEO ISAC
Bruno Felipe Costa, Taufik Abr\~ao
https://arxiv.org/abs/2506.02624 https://…

Refined Metrics, Sensing Limits, and Resource Allocation in OTFS-RSMA LEO ISAC
This paper develops an integrated OTFS-RSMA framework employing advanced SP techniques tailored for this demanding environment. We derive refined communication performance metrics, specifically SINR expressions capturing the practical effects of ICSI and ISIC. Moreover, fundamental sensing limits are established via CRB derivation incorporating parameter-dependent echo gain, linking waveform SP properties to estimation accuracy. The resource allocation is formulated as a non-convex optimization…

This https://arxiv.org/abs/2411.12069 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Matroid Secretary via Labeling Schemes
The Matroid Secretary Problem (MSP) is one of the most prominent settings for online resource allocation and optimal stopping. A decision-maker is presented with a ground set of elements $E$ revealed sequentially and in random order. Upon arrival, an irrevocable decision is made in a take-it-or-leave-it fashion, subject to a feasibility constraint on the set of selected elements captured by a matroid defined over $E$. The decision-maker only has ordinal access to compare the elements, and the g…

Near-feasible Fair Allocations in Two-sided Markets
Javier Cembrano, Andr\'es Moraga, Victor Verdugo
https://arxiv.org/abs/2506.01178 https://

Near-feasible Fair Allocations in Two-sided Markets
We study resource allocation in two-sided markets from a fundamental perspective and introduce a general modeling and algorithmic framework to effectively incorporate the complex and multidimensional aspects of fairness. Our main technical contribution is to show the existence of a range of near-feasible resource allocations parameterized in different model primitives to give flexibility when balancing the different policymaking requirements, allowing policy designers to fix these values accord…

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Han Zhang, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci
https://arxiv.org/abs/2506.12074

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Mobile traffic prediction is an important enabler for optimizing resource allocation and improving energy efficiency in mobile wireless networks. Building on the advanced contextual understanding and generative capabilities of large language models (LLMs), this work introduces a context-aware wireless traffic prediction framework powered by LLMs. To further enhance prediction accuracy, we leverage in-context learning (ICL) and develop a novel two-step demonstration selection strategy, optimizin…

This https://arxiv.org/abs/2501.14417 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

DeepServe: Serverless Large Language Model Serving at Scale
In this paper, we propose DEEPSERVE, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DEEPSERVE addresses key challenges such as resource allocation, serving efficiency, and cold start latencies through four main design components. First, DEEPSERVE uses a simple serverless abstraction called the request-job-task model, which helps manage diverse AI workloads across posttraining and model-serving tasks. Second, DEEPS…

Movable-Antenna Array Enhanced Downlink NOMA
Nianzu Li, Peiran Wu, Lipeng Zhu, Derrick Wing Kwan Ng
https://arxiv.org/abs/2506.11438 https://

Movable-Antenna Array Enhanced Downlink NOMA
Movable antenna (MA) has gained increasing attention in the field of wireless communications due to its exceptional capability to proactively reconfigure wireless channels via localized antenna movements. In this paper, we investigate the resource allocation design for an MA array-enabled base station serving multiple single-antenna users in a downlink non-orthogonal multiple access (NOMA) system. We aim to maximize the sum rate of all users by jointly optimizing the transmit beamforming and th…

Semantic Communications in 6G: Coexistence, Multiple Access, and Satellite Networks
Ishtiaque Ahmed, Yingzhuo Sun, Jingwen Fu, Alper Kose, Leila Musavian, Ming Xiao, Berna Ozbek
https://arxiv.org/abs/2506.11779

Semantic Communications in 6G: Coexistence, Multiple Access, and Satellite Networks
The exponential growth of wireless users and bandwidth constraints necessitates innovative communication paradigms for next-generation networks. Semantic Communication (SemCom) emerges as a promising solution by transmitting extracted meaning rather than raw bits, enhancing spectral efficiency and enabling intelligent resource allocation. This paper explores the integration of SemCom with conventional Bit-based Communication (BitCom) in heterogeneous networks, highlighting key challenges and op…

A distributed system perspective on Backscatter systems: A review
Tonghuan Xiao, Jiecheng Zhou
https://arxiv.org/abs/2506.04873 https://

A distributed system perspective on Backscatter systems: A review
This review investigates the pivotal role of distributed architectures and intelligent resource allocation in enabling robust and scalable wireless systems, with a particular emphasis on backscatter communication, indoor localization, battery-free networks, and Simultaneous Wireless Information and Power Transfer (SWIPT).

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Lei Deng, Wenhan Xu, Jingwei Li, Danny H. K. Tsang
https://arxiv.org/abs/2506.09647

Real-Time Network Traffic Forecasting with Missing Data: A Generative Model Approach
Real-time network traffic forecasting is crucial for network management and early resource allocation. Existing network traffic forecasting approaches operate under the assumption that the network traffic data is fully observed. However, in practical scenarios, the collected data are often incomplete due to various human and natural factors. In this paper, we propose a generative model approach for real-time network traffic forecasting with missing data. Firstly, we model the network traffic fo…