arXiv subjects

Mohammad Ali Salahuddin

Publications and source records attributed to Mohammad Ali Salahuddin.

5 recordsLinked to original sources

arXiv2026

Data-Driven Online Slice Admission Control and Resource Allocation in NextG Mobile Networks

Virtualization in 5G and beyond networks enables the creation of virtual networks (i.e., network slices) tailored to the needs of different applications. To maximize revenue under limited infrastructure resources, InPs must decide in real time whether to admit incoming slice requests (SRs) based on their resource demands and offered values, while accounting for the opportunity cost of consuming scarce resources. To address this challenge, we introduce Online Pricing-based Slice Admission Control and Resource Allocation (OPA) framework. This framework dynamically assigns pseudo-prices to resources that capture long-term scarcity and anticipated inter-temporal opportunity costs. The short-term admission and resource allocation decisions for each SR are then guided by these prices. Additionally, we design an exponential pricing strategy that guarantees bounded worst-case performance. To improve practical performance, we further develop a data-driven exponential pricing approach that learns from historical data. Evaluations on a real-world network topology show that it improves mean revenue by 32.2% and 26.7% over state-of-the-art DRL and optimization-based approaches, respectively, while reducing computational cost by an order of magnitude relative to the latter.

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arXiv2025

One task to rule them all: A closer look at traffic classification generalizability

Existing website fingerprinting and traffic classification solutions do not work well when the evaluation context changes, as their performances often heavily rely on context-specific assumptions. To clarify this problem, we take three prior solutions presented for different but similar traffic classification and website fingerprinting tasks, and apply each solution's model to another solution's dataset. We pinpoint dataset-specific and model-specific properties that lead each of them to overperform in their specific evaluation context. As a realistic evaluation context that takes practical labeling constraints into account, we design an evaluation framework using two recent real-world TLS traffic datasets from large-scale networks. The framework simulates a futuristic scenario in which SNIs are hidden in some networks but not in others, and the classifier's goal is to predict destination services in one network's traffic, having been trained on a labelled dataset collected from a different network. Our framework has the distinction of including real-world distribution shift, while excluding concept drift. We show that, even when abundant labeled data is available, the best solutions' performances under distribution shift are between 30% and 40%, and a simple 1-Nearest Neighbor classifier's performance is not far behind. We depict all performances measured on different models, not just the best ones, for a fair representation of traffic models in practice.

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arXiv2025

5Guard: Isolation-aware End-to-End Slicing of 5G Networks

Network slicing logically partitions the 5G infrastructure to cater to diverse verticals with varying requirements. However, resource sharing exposes the slices to threats and performance degradation, making slice isolation essential. Fully isolating slices is resource-prohibitive, prompting the need for isolation-aware network slicing, where each slice is assigned a tailored isolation level to balance security, usability, and overhead. This paper investigates end-to-end 5G network slicing with resource isolation from the perspective of the infrastructure provider, ensuring compliance with the customers' service-level agreements. We formulate the online 5G isolation-aware network slicing (5G-INS) as a mixed-integer programming problem, modeling realistic slice isolation levels and integrating slice prioritization. To solve 5G-INS, we propose 5Guard, a novel adaptive framework that leverages an ensemble of custom optimization algorithms to achieve the best solution within resource budget and time constraints. Our results show that 5Guard increases profit by up to 10.1% in resource-constrained environments and up to 25.4% in a real-world large-scale network compared to the best-performing individual algorithm. Furthermore, we analyze the trade-offs between isolation levels, their impact on resource utilization, and the effects of slice placement, demonstrating significant advantages over baseline approaches that enforce uniform isolation policies.

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arXiv2025

Data-driven Online Slice Admission Control and Resource Allocation for 5G and Beyond Networks

Virtualization in 5G and beyond networks allows the creation of virtual networks, or network slices, tailored to meet the requirements of various applications. However, this flexibility introduces several challenges for infrastructure providers (InPs) in slice admission control (AC) and resource allocation. To maximize revenue, InPs must decide in real-time whether to admit new slice requests (SRs) given slices' revenues, limited infrastructure resources, unknown relationship between resource allocation and Quality of Service (QoS), and the unpredictability of future SRs. To address these challenges, this paper introduces a novel data-driven framework for 5G slice admission control that offers a guaranteed upper bound on the competitive ratio, i.e., the ratio between the revenue obtained by an oracle solution and that of the online solution. The proposed framework leverages a pricing function to dynamically estimate resources' pseudo-prices that reflect resource scarcity. Such prices are further coupled with a resource allocation algorithm, which leverages a machine-learned slice model and employs a primal-dual algorithm to determine the minimum-cost resource allocation. The resource cost is then compared with the offered revenue to admit or reject a SR. To demonstrate the efficacy of our framework, we train the data-driven slice model using real traces collected from our 5G testbed. Our results show that our novel approach achieves up to 42% improvement in the empirical competitive ratio, i.e., ratio between the optimal and the online solution, compared to other benchmark algorithms.

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arXiv2015

On Optimizing Replica Migration in Distributed Cloud Storage Systems

With the wide adoption of large-scale Internet services and big data, the cloud has become the ideal environment to satisfy the ever-growing storage demand, thanks to its seemingly limitless capacity, high availability and faster access time. In this context, data replication has been touted as the ultimate solution to improve data availability and reduce access time. However, replica placement systems usually need to migrate and create a large number of data replicas over time between and within data centers, incurring a large overhead in terms of network load and availability. In this paper, we propose CRANE, an effiCient Replica migrAtion scheme for distributed cloud Storage systEms. CRANE complements any replica placement algorithm by efficiently managing replica creation in geo-distributed infrastructures by (1) minimizing the time needed to copy the data to the new replica location, (2) avoiding network congestion, and (3) ensuring a minimal availability of the data. Our results show that, compared to swift (the OpenStack project for managing data storage), CRANE is able to minimize up to 30% of the replica creation time and 25% of inter-data center network traffic, while ensuring the minimum required availability of the data.

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