arXiv subjects

Andrea Baiocchi

Publications and source records attributed to Andrea Baiocchi.

7 recordsLinked to original sources

arXiv2026

Repeated Contention Scheduling: A Novel Resource Allocation Algorithm Toward 6G Vehicular Networks

Efficient decentralized resource allocation remains a fundamental challenge in NR-V2X sidelink communications, where conventional Semi-Persistent Scheduling (SPS) and Dynamic Scheduling (DS) suffer from persistent collisions and limited adaptability under dynamic and dense conditions. This paper proposes Repeated Contention Scheduling (RCS), a novel resource allocation algorithm based on multi-round, feedback-driven contention that eliminates long-term reservations and enables fully distributed operation. Simulations demonstrate that RCS outperforms SPS and DS in terms of success probability, collision and loss reduction, and timeliness metrics such as Packet Inter-Reception Delay and Age of Information, particularly under high load. The practical feasibility of the approach is validated through an SDR-based experimental testbed, which confirms robust operation under realistic hardware impairments and closely matches theoretical and simulation results. These findings establish RCS as a viable and scalable solution for resource allocation in 5G NR sidelink and a promising candidate for future 6G vehicular communication systems.

cs.NI↗

arXiv2026

Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption

Modern deployments of Large Language Models (LLMs) increasingly require serving multiple models with diverse architectures, sizes, and specialization on shared, heterogeneous hardware. This setting introduces new challenges for resource allocation, dispatching, and scheduling, particularly under GPU memory constraints where partial CPU-GPU offloading and preemption become necessary. While existing systems primarily optimize throughput for a single model, comparatively little work addresses multi-model scheduling under these conditions. In this paper, we present an empirical study of how different LLMs behave across hardware platforms, focusing on the performance implications of layer offloading and preemption. We show that offloading leads to strongly non-linear and model-dependent degradation in decode throughput, with smaller models exhibiting sharper sensitivity to reduced GPU residency. We further demonstrate that preemption incurs substantial overhead, largely dominated by model state reload rather than key-value cache transfer, and that this cost varies significantly across models and hardware platforms. Additionally, we highlight the role of sequence length and interconnect bandwidth in amplifying data movement and execution inefficiencies. Based on these findings, we identify a set of key features that future schedulers must consider, including model-specific offloading sensitivity, workload characteristics, and the cost structure of preemption and data transfer. These insights provide guidance for the design of next-generation LLM serving systems capable of efficiently managing heterogeneous, multi-model workloads with hybrid CPU-GPU execution.

cs.AI↗

arXiv2025

"Two-Stagification": Job Dispatching in Large-Scale Clusters via a Two-Stage Architecture

A continuing effort is devoted to devising effective dispatching policies for clusters of First Come First Served servers. Although the optimal solution for dispatchers aware of both job size and server state remains elusive, lower bounds and strong heuristics are known. In this paper, we introduce a two-stage cluster architecture that applies classical Round Robin, Join Idle Queue, and Least Work Left dispatching schemes, coupled with an optimized service-time threshold to separate large jobs from shorter ones. Using both synthetic (Weibull) workloads and real Google data center traces, we demonstrate that our two-stage approach greatly improves upon the corresponding single-stage policies and closely approaches the performance of advanced size- and state-aware methods. Our results highlight that careful architectural design-rather than increased complexity at the dispatcher-can yield significantly better mean response times in large-scale computing environments.

cs.DC↗

arXiv2025

Dispatching Odyssey: Exploring Performance in Computing Clusters under Real-world Workloads

Recent workload measurements in Google data centers provide an opportunity to challenge existing models and, more broadly, to enhance the understanding of dispatching policies in computing clusters. Through extensive data-driven simulations, we aim to highlight the key features of workload traffic traces that influence response time performance under simple yet representative dispatching policies. For a given computational power budget, we vary the cluster size, i.e., the number of available servers. A job-level analysis reveals that Join Idle Queue (JIQ) and Least Work Left (LWL) exhibit an optimal working point for a fixed utilization coefficient as the number of servers is varied, whereas Round Robin (RR) demonstrates monotonously worsening performance. Additionally, we explore the accuracy of simple G/G queue approximations. When decomposing jobs into tasks, interesting results emerge; notably, the simpler, non-size-based policy JIQ appears to outperform the more "powerful" size-based LWL policy. Complementing these findings, we present preliminary results on a two-stage scheduling approach that partitions tasks based on service thresholds, illustrating that modest architectural modifications can further enhance performance under realistic workload conditions. We provide insights into these results and suggest promising directions for fully explaining the observed phenomena.

cs.DC↗

arXiv2025

The Merit of Simple Policies: Buying Performance With Parallelism and System Architecture

While scheduling and dispatching of computational workloads is a well-investigated subject, only recently has Google provided publicly a vast high-resolution measurement dataset of its cloud workloads. We revisit dispatching and scheduling algorithms fed by traffic workloads derived from those measurements. The main finding is that mean job response time attains a minimum as the number of servers of the computing cluster is varied, under the constraint that the overall computational budget is kept constant. Moreover, simple policies, such as Join Idle Queue, appear to attain the same performance as more complex, size-based policies for suitably high degrees of parallelism. Further, better performance, definitely outperforming size-based dispatching policies, is obtained by using multi-stage server clusters, even using very simple policies such as Round Robin. The takeaway is that parallelism and architecture of computing systems might be powerful knobs to control performance, even more than policies, under realistic workload traffic.

cs.DC↗

arXiv2024

Analysis of Status Update in Wireless Networks with Successive Interference Cancellation

Data collection in an IoT environment requires simple and effective communication solutions to address resource constraints, ensure network efficiency, while achieving scalability. Efficiency is evaluated based on the timeliness of collected data (Age of Information), the energy spent per delivered unit of data, and the effectiveness in utilizing spectrum resources. This paper addresses a random multiple access adaptive system, in which a large number of devices send sporadic messages in non-periodic pattern. In particular, our analysis highlights the potential of Successive Interference Cancellation and identifies an adaptive parameter setting to maximize its benefits as the level of contention on the shared channel varies. An analytical model is defined, easily scalable with the number of nodes and yielding all the relevant metrics. Evidence of the accuracy of the model is given by comparing predicted results against simulations. The model is utilized to assess the trade-off between Age of Information and energy consumption, revealing a sharp relationship between the two. The considered approach lends itself to many generalizations and applications to massive machine-type communications and IoT networks.

eess.SP↗

arXiv2024

Asymptotic analysis of sum-rate under SIC

Limitation of the cost of coordination and contention among a large number of nodes calls for grant-free approaches, exploiting physical layer techniques to solve collisions. Successive Interference Cancellation (SIC) is becoming a key building block of multiple access channel receiver, in an effort to support massive Internet of Things (IoT). In this paper, we explore the large-scale performance of SIC in a theoretical framework. A general model of a SIC receiver is stated for a shared channel with $n$ transmitters. The asymptotic sum-rate performance is characterized as $n \rightarrow \infty$, for a suitably scaled target Signal to Noise Interference Ratio (SNIR). The probability distribution of the number of correctly decoded packets is shown to tend to a deterministic distribution asymptotically for large values of $n$. The asymptotic analysis is carried out for any probability distribution of the wireless channel gain, assuming that the average received power level is same for all nodes, through power control.

eess.SP↗