Rajkumar Buyya

Objective: Large Language Models (LLMs) are rapidly transforming artificial intelligence applications, but their unprecedented computational, memory, communication, and data‐management demands pose significant challenges to existing computing infrastructures. Traditional cloud and distributed systems were not designed for the scale, dynamic execution patterns, heterogeneous hardware requirements, and service‐level demands of modern LLM workloads. This paper aims to examine how cloud‐native and distributed systems can evolve to support efficient, scalable, reliable, and sustainable LLM training and inference. Methods: We conduct a structured analysis of representative research advances and practical developments in distributed LLM training, inference serving, cloud‐native orchestration, resource management, heterogeneous computing, and edge‐cloud intelligence. We organize the analysis around major system challenges, including computation, distributed execution and system software, resource management and serving, security and privacy, data management, and standardization. We further examine representative resource‐management strategies and emerging paradigms, including serverless inference, federated and decentralized learning, trustworthy distributed AI, and heterogeneous computing with emerging accelerators. Results: Our analysis reveals that supporting LLMs at scale requires a transition from general‐purpose cloud infrastructure toward LLM‐aware, cross‐layer, and adaptive systems. Existing approaches have improved distributed execution, elastic resource management, multi‐tenant serving, and hardware utilization, but important gaps remain in cross‐layer coordination, workload‐aware orchestration, data and context management, interoperability, security, and system‐level optimization. Based on these gaps, we develop a research agenda covering future directions in sparse and heterogeneous computation, LLM‐enhanced system software, prompt and context management, autonomous cloud management, decentralized intelligence, edge‐cloud collaboration, personalized LLM services, and emerging computing paradigms. Conclusion: Cloud‐native and distributed systems will play a central role in enabling the next generation of LLM services. Future infrastructures should evolve toward more intelligent, elastic, interoperable, secure, and heterogeneous platforms that jointly optimize computation, memory, communication, data, and service objectives. Continued advances in system design, standardization, and cross‐disciplinary collaboration will be essential for realizing scalable and sustainable LLM infrastructures.

Stateful Serverless (Function-as-a-Service) environment are increasingly used for stream processing applications. These applications are often structured as pipelines where data flows through a sequence of distinct processing operators. Many operators are stateful and require access to the processing state during execution. To address this, stateful serverless platforms introduce state servers on worker nodes, co-locating state with computation to dramatically reduce access latency. In serverless, these data pipelines are realized by chaining function invocations, passing the processed data from one function to the next. To reduce network overhead, state-of-the-art scheduling approaches employ a distributed worker-level scheduling model, routing locally generated chained requests directly to destination workers, bypassing centralized schedulers. However, these approaches always co-locate state and execution for the same operator in the same worker, which forces most invocations to the next operator to be transferred across workers, thereby increasing both latency and I/O overhead. As a result, this paper proposes LcSched, a novel distributed scheduling strategy tailored for stream processing applications in stateful serverless environments. LcSched seeks to maximise local execution of chained function invocations while ensuring that each execution accesses its state from the local worker. It also monitors workload at runtime to maintain workload balance between workers. Experimental results show that LcSched improves throughput by up to 3.6x and reduces end-to-end latency by up to 79% compared to the state-of-the-art distributed scheduling strategy for stateful serverless environments.

The growing deployment of applications in real-world edge environments is driving an increasing demand for large language model (LLM) training in distributed systems. This demand creates significant challenges for resource management, particularly in resource-constrained edge cloud environments. Federated learning (FL) enables privacy-preserving LLM fine-tuning across multiple clients, but it also introduces dynamic computational and communication loads that can strain system resources. To address these challenges, we propose an integrated framework that combines LLM fine-tuning using federated adaptive local low-rank adaptation (FedALoRA) with forecasting-driven autoscaling policies. Differential privacy (DP) is incorporated to ensure secure aggregation, balancing privacy guarantees with system performance. Using the WikiText-2 dataset for FL-based LLM training and the Bitbrains dataset for CPU usage forecasting, we evaluate deep learning models (LSTM, GRU, BiLSTM, CNN, CNN-LSTM, Transformer). Results demonstrate that FedALoRA enables fast federated convergence, reducing perplexity from 199.3 to 85.2 without DP and from 211.6 to 92.2 with DP, incurring only a 7–13% privacy overhead. Among forecasting models, CNN-LSTM achieves the lowest MSE (0.25) and consistently requires the fewest pods (as low as 11 replicas), highlighting its superior accuracy and resource efficiency for proactive autoscaling. Comparative analysis demonstrates that proactive autoscaling consistently outperforms reactive autoscaling, with non-DP settings achieving slightly faster adaptation and DP settings offering stronger privacy guarantees. Overall, the proposed framework balances performance, adaptability, and privacy, making it well-suited for federated-LLM Autoscaling in dynamic edge environments.

Context: The rapid increase in cyber threats, coupled with increasingly sophisticated attack strategies and the exponential growth of data in recent years, has exposed significant limitations in classical machine learning, rule‐based, and signature‐based defense mechanisms. These approaches are often unable to scale or adapt effectively to evolving threat landscapes. Objective: As a result, Quantum Machine Learning (QML) has emerged as a promising alternative, leveraging computational principles from quantum mechanics. QML enables more expressive encoding and efficient processing of high‐dimensional data, offering potential advantages for complex cybersecurity tasks. Method: This survey provides a comprehensive overview of QML techniques relevant to the domain of security, such as Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Variational Quantum Circuits (VQCs), and Quantum Generative Adversarial Networks (QGANs), and discusses the contributions of this paper in relation to existing research in the field and how it improves over them. Results: It also maps these methods across supervised, unsupervised, and generative learning paradigms, as well as to core cybersecurity tasks, including intrusion and anomaly detection, malware and botnet classification, and encrypted‐traffic analytics. It also discusses their application in cloud computing security, where QML can enhance secure, scalable operations. Conclusion: Many limitations of QML in the domain of cybersecurity have also been discussed, along with the directions for addressing them.