# Large Language Models Meet Next-Generation Networking Technologies: A Review
## 4 Open Challenges and Opportunities
### Network Engineering Optimization for Large Language Models  
Beyond leveraging LLMs to optimize network performance, another promising and important direction is exploring how network engineering strategies can optimize the functioning and deployment of LLMs. As LLMs are integrated into various applications, several challenges arise that require innovative network engineering solutions. A significant challenge is managing high-demand LLM inference services that must handle a wide range of requests, from short chat conversations to lengthy document analysis, while ensuring fair processing for all clients. Traditional rate limiting can lead to under-utilization of resources and poor client experiences when spare capacity is available. The unpredictable request lengths and unique batching characteristics of LLMs on parallel accelerators further complicate this issue. To address this, the work in [198] introduces a new concept of LLM serving fairness based on a cost function that accounts for the number of input and output tokens processed and proposes the Virtual Token Counter scheduler to maintain fairness. Fairness is a critical aspect in network resource allocation, ensuring that all clients receive equitable access to computing resources [199; 200; 201; 202; 203]. A potential future direction is to integrate AIMD principles with scheduling algorithms to dynamically adjust resource allocation for LLMs based on real-time network conditions and usage patterns, potentially improving both fairness and resource utilization for LLMs. Efficiently running LLM inference tasks on resource-limited mobile terminals is another critical challenge. Traditional deep reinforcement learning methods used for offloading these tasks to servers face issues such as data inefficiency and insensitivity to latency requirements. In [204], the authors propose an active inference algorithm with rewardless guidance for making offloading decisions and resource allocations in cloud-edge networks. This approach helps manage the distribution of LLM inference tasks between mobile devices and servers more effectively, ensuring that LLMs can be utilized efficiently even in resource-constrained environments. Incorporating network engineering techniques, such as optimizing data transmission paths and dynamically adjusting resource allocation, can potentially enhance the efficiency and responsiveness of LLM-based applications on mobile platforms by improving data utilization efficiency and adaptability to changing task loads. Creating new LLMs or fine-tuning existing ones for domain-specific tasks requires extensive data. The term \"large\" in LLM refers to models with billions of parameters. Deploying such parameter-heavy LLMs is resource-intensive, necessitating carefully designed hardware platforms. The work in [205] introduces the GenZ analytical tool to explore the relationship between LLM inference performance and various hardware design parameters. This study provides insights into configuring platforms for different LLM workloads and projecting future hardware capabilities needed for increasingly complex models. Network engineering can play a crucial role by optimizing data flow and reducing latency between distributed hardware components, ensuring that LLMs operate at peak efficiency across diverse environments. Acquiring large, high-quality instruction data for training generative LLMs is often costly and difficult to access. The work in [206] proposes Federated Instruction Tuning (FedIIT), which leverages federated learning to harness diverse instruction data generated by end users while addressing privacy concerns. This approach allows for the training of LLMs using data stored on local devices, thus enhancing their ability to generate authentic and natural responses. Future research could explore the integration of advanced network engineering techniques to optimize data synchronization and transmission in federated learning environments, ensuring efficient and secure data handling.