arXiv subjects

Andrea Marin

Publications and source records attributed to Andrea Marin.

4 recordsLinked to original sources

arXiv2026

The Multiserver-Job Stochastic Recurrence Equation for Cloud Computing Performance Evaluation

We study the Multiserver-Job Queuing Model (MJQM) with general independent arrivals and service times under FCFS scheduling, using stochastic recurrence equations (SREs) and ergodic theory. We prove the monotonicity and separability properties of the MJQM SRE, enabling the application of the monotone-separable extension of Loynes' theorem and the formal definition of the MJQM stability condition. Based on these results, we introduce and implement two algorithms: one for drawing sub-perfect samples (SPS) of the system's workload and the second one to estimate the system's stability condition given the statistics of the jobs' input stream. The SPS algorithm allows for a massive GPU parallelization, greatly improving the efficiency of performance metrics evaluation. We also show that this approach extends to more complex systems, including MJQMs with typed resources.

cs.PF↗

arXiv2025

Improving Nonpreemptive Multiserver Job Scheduling with Quickswap

Modern data center workloads are composed of multiserver jobs, computational jobs that require multiple servers in order to run. A data center server can run many multiserver jobs in parallel, as long as it has sufficient resources to meet their demands. However, multiserver jobs are generally stateful, meaning that job preemptions incur significant overhead from saving and reloading the state associated with running jobs. Hence, most systems try to avoid these costly job preemptions altogether. Given these constraints, a scheduling policy must determine what set of jobs to run in parallel at each moment in time to minimize the mean response time across a stream of arriving jobs. Unfortunately, simple non-preemptive policies such as FCFS may leave many servers idle, resulting in high mean response times or even system instability. Our goal is to design and analyze non-preemptive scheduling policies for multiserver jobs that maintain high system utilization to achieve low mean response time. One well-known non-preemptive policy, Most Servers First (MSF), prioritizes jobs with higher server requirements and achieves high resource utilization. However, MSF causes extreme variability in job waiting times, and can perform significantly worse than FCFS in practice. To address this, we propose and analyze a class of scheduling policies called MSF-Quick Swap (MSFQ) that performs well. MSFQ reduces the variability of job waiting times by periodically granting priority to other jobs in the system. We provide both stability results and an analysis of mean response time under MSFQ to prove that our policy dramatically outperforms MSF in the case where jobs request one or all the servers. In more complex cases, we evaluate MSFQ in simulation. We show that, with some additional optimization, variants of the MSFQ policy can greatly outperform MSF and FCFS on real-world multiserver job workloads.

cs.PF↗

arXiv2024

Computational Algorithms for the Product Form Solution of Closed Queuing Networks with Finite Buffers and Skip-Over Policy

Closed queuing networks with finite capacity buffers and skip-over policies are fundamental models in the performance evaluation of computer and communication systems. This technical report presents the details of computational algorithms to derive the key performance metrics for such networks. The primary focus is on the efficient computation of the normalization constant, which is critical for determining the steady-state probabilities of the network states under investigation. A convolution algorithm is proposed, which paves the way for the computation of key performance indices, such as queue length distribution and throughput, accommodating the intricacies introduced by finite capacity constraints and skip-over mechanisms. Finally, an extension of the traditional Mean Value Analysis algorithm addressing numerical stability is provided. The approaches discussed here allow make the investigation of large-scale networks feasible and enable the development of robust implementations of these techniques for practical use.

cs.PF↗

arXiv2021

A Foreground-Background queueing model with speed or capacity modulation

The models studied in the steady state involve two queues which are served either by a single server whose speed depends on the number of jobs present, or by several parallel servers whose number may be controlled dynamically. Job service times have a two-phase Coxian distribution and the second phase is given lower priority than the first. The trade-offs between holding costs and energy consumption costs are examined by means of a suitable cost functions. Two different two-dimensional Markov process are solved exactly. The solutions are used in several numerical experiments. Some counter-intuitive results are observed.

cs.PF↗