Performance Evaluation最新文献

{"title":"Editorial message for the special issue on IFIP performance 2025","authors":"Konstantin Avrachenkov , Hans van den Berg , Cathy Xia","doi":"10.1016/j.peva.2025.102537","DOIUrl":"10.1016/j.peva.2025.102537","url":null,"abstract":"","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"172 ","pages":"Article 102537"},"PeriodicalIF":0.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Network games, control and optimization. Selected papers following Netgcoop 2024","authors":"","doi":"10.1016/j.peva.2025.102511","DOIUrl":"10.1016/j.peva.2025.102511","url":null,"abstract":"","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"172 ","pages":"Article 102511"},"PeriodicalIF":0.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148177899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foreword to Special Issue on Performance in the Edge-to-Cloud Continuum","authors":"Daniel Sadoc Menasché (Guest Editors), Francesco De Pellegrini, Marco Ajmone Marsan","doi":"10.1016/j.peva.2026.102557","DOIUrl":"10.1016/j.peva.2026.102557","url":null,"abstract":"","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"172 ","pages":"Article 102557"},"PeriodicalIF":0.8,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Content and access networks synergies: Tradeoffs in public and private investments by content providers","authors":"Pranay Agarwal ,&nbsp;D. Manjunath","doi":"10.1016/j.peva.2025.102521","DOIUrl":"10.1016/j.peva.2025.102521","url":null,"abstract":"<div><div>The ubiquity of smartphones has fueled content consumption worldwide, leading to an ever-increasing demand for a better Internet experience. This has necessitated an upgrade of the capacity of the access network. The Internet service providers (ISPs) have been demanding that the content providers (CPs) share the cost of upgrading access network infrastructure. A <em>public investment</em> in the infrastructure of a neutral ISP will boost the profit of the CPs, and hence, seems a rational strategy. A CP can also make a <em>private investment</em> in its infrastructure and boost its profits. In this paper, we study the trade-off between public and private investments by a CP when the decision is made under different types of interaction between them. Specifically, we consider four interaction models between CPs—centralized allocation, cooperative game, non-cooperative game, and a bargaining game—and determine the public and private investment for each model. Via numerical results, we evaluate the impact of different incentive structures on the utility of the CPs. We see that the bargaining game can result in higher public investment than the non-cooperative and centralized models. However, this benefit gets reduced if the CPs are incentivized to invest in private infrastructure.</div></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"171 ","pages":"Article 102521"},"PeriodicalIF":0.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging task offloading in edge-cloud Computing systems using GI/M(L→K)/1 queueing model with dynamic service rates","authors":"Veena Goswami","doi":"10.1016/j.peva.2025.102540","DOIUrl":"10.1016/j.peva.2025.102540","url":null,"abstract":"<div><div>We consider the offloading of tasks in edge–cloud computing systems using a renewal input modified batch service queue. Tasks are processed using a modified batch service policy with a minimum batch size of <span><math><mi>L</mi></math></span> and a maximum batch size of <span><math><mi>K</mi></math></span> in an edge–cloud computing system. Bulk services combine several tasks from many Internet of Things devices and offload them to the edge or cloud for concurrent execution. The updated batch service rule allows tasks to be offloaded for variable batch sizes, smaller batches when network circumstances are favorable, and bigger batches when the network is congested to reduce transmission overhead. In addition, if the server has commenced the processing and there are fewer than <span><math><mi>K</mi></math></span> tasks, we let the tasks join. Furthermore, the batches’ processing rates are presumed to depend on the batch size. We derive the analytic results for the marginal and joint probability distribution of the number of tasks in the queue/system and with the server. We show the influence of light-tailed and heavy-tailed inter-arrival time distributions on the system model with numerical examples. Dynamic service rates adjust processing speeds at edge or cloud servers based on workload, network latency, and available resources. It reduces latency, balances computational load, and improves system adaptability to changing conditions.</div></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"171 ","pages":"Article 102540"},"PeriodicalIF":0.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Learning for Collaborative Inference Systems: The case of early exit networks","authors":"Chuan Xu ,&nbsp;Caelin Kaplan ,&nbsp;Angelo Rodio ,&nbsp;Tareq Si Salem ,&nbsp;Giovanni Neglia","doi":"10.1016/j.peva.2025.102538","DOIUrl":"10.1016/j.peva.2025.102538","url":null,"abstract":"<div><div>In today’s increasingly diverse computing landscape, end devices like sensors and smartphones are progressively equipped with AI models tailored to their local memory and computational constraints. Local inference reduces communication costs and latency; however, these smaller models typically underperform compared to more sophisticated models deployed on edge servers or in the cloud. Collaborative Inference Systems (CISs) address this performance trade-off by enabling smaller devices to offload part of their inference tasks to more capable devices. These systems often deploy hierarchical models that share numerous parameters, exemplified by deep neural networks that utilize strategies like early exits or ordered dropout. In such instances, Federated Learning (FL) may be employed to jointly train the models within a CIS. Yet, traditional training methods have overlooked the operational dynamics of CISs during inference, particularly the potential high heterogeneity in serving rates across the devices within a given CIS. To address this gap, we propose a novel FL approach that explicitly accounts for variations in serving rates within CISs. Our framework not only offers rigorous theoretical guarantees but also surpasses state-of-the-art training algorithms for CISs, especially in scenarios where end devices handle higher inference request rates and where data availability is uneven across devices.</div></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"171 ","pages":"Article 102538"},"PeriodicalIF":0.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revenue management for parallel services with fully observable queues and heterogeneous customers","authors":"Caitlin Vanden Bussche,&nbsp;Sabine Wittevrongel,&nbsp;Arnaud Devos,&nbsp;Dieter Fiems","doi":"10.1016/j.peva.2026.102541","DOIUrl":"10.1016/j.peva.2026.102541","url":null,"abstract":"<div><div>We study revenue optimisation for a Markovian queueing model with two observable parallel queues. Customers are heterogeneous in the sense that they value the service differently and strategically choose which queue to join depending on which queue offers the greatest expected utility. To join a queue, customers must pay a predetermined fee, which the provider sets to optimise revenue. We consider both a scenario where customers must always choose a queue and one where customers have the option to balk. In both scenarios, we use a power series approximation method accelerated by Wynn’s <span><math><mi>ϵ</mi></math></span>-method to efficiently solve the balance equations.</div></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"171 ","pages":"Article 102541"},"PeriodicalIF":0.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ScaleGS: Scalable distributed framework for large-scale 3D Gaussian splatting with edge communication","authors":"Yong Kou ,&nbsp;Jinlong He ,&nbsp;Xia Yuan ,&nbsp;Dening Luo ,&nbsp;Yanci Zhang","doi":"10.1016/j.peva.2025.102516","DOIUrl":"10.1016/j.peva.2025.102516","url":null,"abstract":"<div><div>3D Gaussian Splatting (3DGS) has recently demonstrated outstanding performance in 3D reconstruction and real-time rendering. However, its scalability to large scenes remains limited by single-GPU memory constraints. We propose ScaleGS, a scalable distributed training framework for large-scale 3DGS with lightweight edge-aware communication. (1) We present a spatial median-guided binary partitioning algorithm that divides the point cloud into balanced, non-overlapping, and spatially contiguous cuboid regions for efficient multi-GPU management. To ensure global view consistency, each GPU independently grows and updates only its local Gaussians, while cross-GPU Gaussians are accessed only for rendering and loss computation. (2) We design a lightweight edge communication strategy to significantly reduce cross-GPU communication overhead. A greedy GPU-Tile remapping algorithm leverages the spatial concentration of Gaussians to confine cross-GPU communication to edge regions, effectively decoupling communication complexity from GPU count, with per-GPU complexity remaining <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span>. An optimized all-to-all communication scheme is also introduced to eliminate redundant transmissions. (3) Our framework introduces an adaptive edge-refined load balancing mechanism that periodically monitors GPU workloads and selectively migrates Gaussians between neighboring GPUs to maintain balance and spatial continuity with negligible cost. Evaluations on large-scale 4K scenes show that ScaleGS consistently outperforms state-of-the-art methods, achieving up to 20% faster training and approximately 20% model size reduction on 8 T P40 GPUs without compromising reconstruction quality. Project page: <span><span>https://aicodeclub.github.io/ScaleGS</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"171 ","pages":"Article 102516"},"PeriodicalIF":0.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A control-theoretic perspective on BBR/CUBIC congestion-control competition","authors":"Simon Scherrer ,&nbsp;Adrian Perrig ,&nbsp;Stefan Schmid","doi":"10.1016/j.peva.2025.102529","DOIUrl":"10.1016/j.peva.2025.102529","url":null,"abstract":"<div><div>To understand the fairness properties of the BBR congestion-control algorithm (CCA), previous research has analyzed BBR behavior with a variety of models. However, previous model-based work suffers from a trade-off between accuracy and interpretability: While dynamic fluid models generate highly accurate predictions through simulation, the causes of their predictions cannot be easily understood. In contrast, steady-state models predict CCA behavior in a manner that is intuitively understandable, but often less accurate. This trade-off is especially consequential when analyzing the competition between BBR and traditional loss-based CCAs, as this competition often suffers from instability, i.e., sending-rate oscillation. Steady-state models cannot predict this instability at all, and fluid-model simulation cannot yield analytical results regarding preconditions and severity of the oscillation.</div><div>To overcome this trade-off, we extend the recent dynamic fluid model of BBR by means of control theory. Based on this control-theoretic analysis, we derive quantitative conditions for BBR/CUBIC oscillation, identify network settings that are susceptible to instability, and find that these conditions are frequently satisfied by practical networks. Our analysis illuminates the fairness implications of BBR/CUBIC oscillation, namely by deriving and experimentally validating fairness bounds that reflect the extreme rate distributions during oscillation. In summary, our analysis shows that BBR/CUBIC oscillation is frequent and harms BBR fairness, but can be remedied by means of our control-theoretic framework.</div></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"171 ","pages":"Article 102529"},"PeriodicalIF":0.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving nonpreemptive multiserver job scheduling with quickswap","authors":"Zhongrui Chen ,&nbsp;Adityo Anggraito ,&nbsp;Diletta Olliaro ,&nbsp;Andrea Marin ,&nbsp;Marco Ajmone Marsan ,&nbsp;Benjamin Berg ,&nbsp;Isaac Grosof","doi":"10.1016/j.peva.2025.102525","DOIUrl":"10.1016/j.peva.2025.102525","url":null,"abstract":"<div><div>Modern data center workloads are composed of <em>multiserver jobs</em>, computational jobs that require multiple servers in order to run. A data center can run many multiserver jobs in parallel, as long as it has sufficient resources to meet their individual demands. Multiserver jobs are generally <em>stateful</em>, 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 <em>scheduling policy</em> 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 First-Come First-Served (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.</div><div>One well-known non-preemptive scheduling policy, Most Servers First (MSF), prioritizes jobs with higher server needs and is known for achieving high resource utilization. However, MSF causes extreme variability in job waiting times, and can perform significantly worse than FCFS in practice. To address this issue, we propose and analyze a class of scheduling policies called <em>Most Servers First with Quickswap</em> (MSFQ) that performs well in a wide variety of cases. 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 either request one server 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.</div></div>","PeriodicalId":19964,"journal":{"name":"Performance Evaluation","volume":"171 ","pages":"Article 102525"},"PeriodicalIF":0.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145691456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}