Edge intelligence enables the execution of AI inference tasks on computing platforms at the network edge, typically co-located with or near the radio access network rather than in centralized clouds or on mobile devices. This approach is particularly well suited for data analytics of low-latency and resource-constrained applications, where large data volumes and stringent latency constraints require tight integration of wireless access and on-site computational resources. However, the performance and cost-efficiency of such systems fundamentally depend on the joint dimensioning of wireless and computational resources prior to deployment, specially amid spatial and temporal uncertainties. Prior works largely emphasize run-time resource allocation or employ simplified network models that decouple radio access from computing infrastructure, overlooking end-to-end correlations in large-scale deployments. This paper introduces a unified stochastic framework for dimensioning multi-cellular edge-intelligent systems. We model network topology via a Poisson point process to capture randomness in user and base-station locations, incorporating inter-cell interference, distance-proportional fractional power control, and peak-power constraints. Integrating this with queueing theory and empirical profiling of AI inference workloads, we derive tractable expressions for the end-to-end offloading delay. These enable a non-convex joint optimization problem for minimizing deployment costs while enforcing statistical quality-of-service guarantees, defined not merely by averages, but by strict tail-latency and inference accuracy constraints. We prove decomposability into convex sub-problems, ensuring global optimality with zero gap. Through numerical evaluations in noise-limited and interference-limited regimes, we identify parameter regions that yield cost-efficient designs versus those that lead to severe under-utilization or unfairness across users. Key insights include the following: smaller cells reduce transmission delay but cause higher per-request computing cost due to reduced multiplexing at the servers, while larger cells exhibit the opposite trend. Moreover, network densification reduces computational costs only when frequency reuse scales with base-station density; otherwise, sparse deployments enhance fairness and efficiency in interference-limited scenarios. Overall, our analysis provides system designers with principled guidelines for scalable, QoS-aware provisioning of edge-intelligent video analytics in next-generation cellular networks.
With the expansion of applications and areas of blockchains, there is an increasing need for faster transaction validation platforms. The Practical Byzantine Fault Tolerance (PBFT) algorithm is one of the best validation processes to meet the need for velocity and security of successful consensus. Despite the implementations in several platforms such as Hyperledger Fabric, the performance does not reach what was hoped for their consensuses. The objective of this article is to present a performance evaluation model based on an Open Queuing Network to study the limits of PBFT and pipelined PBFT and to succeed in presenting a new parallelization approach in the form of parallel clusters. The advantage of this model is that it closely evaluates the different stages of PBFT execution according to the load submitted by the clients. This model is validated with the ns-3 simulator and has the advantage of quickly reaching evaluations that the simulator is limited in time to have them. The results show the importance of having consensus executions on parallel clusters to distribute the loads between nodes and clients. With cluster-based parallel PBFT, we can reach even twenty thousand transactions per second close to the validation values of Visa transactions. This opens the horizons towards the effective use of blockchains for new types of fast applications.
Improving network performance and user experience by tuning network configurations is crucial to cellular service providers (CSPs). However, predicting the performance impacts of configuration changes is non-trivial. The large scale, diversity and complexity of configuration parameters and base station deployments, and more importantly, the uncontrollable external factors ( e.g. , weather and called latents ), lead to confounding effects between configurations and performance metrics. In this article, we show that the effects of latents can be properly mitigated by considering intermediates, called Mobility, Access, and Traffic (MAT) metrics , which separate the configurations and latents from performance metrics. Then, we propose the C onfiguration I mpact P rediction A nalysis T oolkit (CIPAT), a novel two-stage toolkit, driven by a large real-world dataset from live LTE and 5G networks. Our extensive evaluation shows that CIPAT enables network operators to confidently predict the performance impact of candidate configuration settings with an accuracy of up to 86% and an efficacy of up to 85%.
We present a methodology for automated performance regression testing at the unit level, designed to integrate seamlessly into the development workflow of high-performance computing (HPC) applications. The proposed framework emphasizes the integration of performance tests with existing functional unit tests by leveraging their shared structure, thereby enabling the development of performance test suites with minimal developer effort and supporting the early detection of performance degradations. As a proof of concept, we implement and release the framework as a publicly available Julia package. It generates architecture-agnostic test suites that remain valid across heterogeneous hardware environments. To demonstrate its effectiveness, we apply it to representative HPC use cases, including memory throughput analysis in stencil-based computations and roofline modeling of a sparse linear system solver. Experiments conducted on a diverse set of CPU and GPU architectures confirm the framework’s ability to detect performance regressions consistently, validating its potential for use in continuous performance testing pipelines.
This paper examines how environmental regulation shapes pricing, capacity allocation, and infrastructure investment in the competitive Content Delivery Network (CDN) market. We develop a hierarchical game-theoretic framework modeling strategic interactions among multiple CDNs, a service provider, and end-users, capturing both economic and environmental objectives. Using backward induction, we analyze equilibrium behavior under two regulatory regimes–a baseline scenario without intervention and a regulated scenario, where CDNs incur taxes proportional to dirty capacity usage. Our analysis yields four principal findings. First, a structural tax threshold τ * , governed by the gap between renewable and fossil-fuel energy costs, determines the transition from carbon-intensive to clean infrastructure. Second, a moderate tax slightly above τ * achieves near-complete clean adoption at a total welfare loss of only 6.5 %. Third, government revenue is non-monotonic in the tax rate, peaking at low levels and collapsing as CDNs shift to untaxed clean capacity. Fourth, since the infrastructure-transition threshold is governed by the energy-cost gap of each provider, a uniform carbon tax imposes asymmetric burdens once these costs are heterogeneous across CDNs, motivating tiered tax designs. These results provide insights for policymakers seeking to align sustainability objectives with competitive dynamics in the content delivery market.
