Abdelhakim Hafid

Yaser Baseri, Abdelhakim Hafid, Arash Habibi Lashkari

Quantum Computing (QC) threatens the cryptographic foundations of Cloud Computing (CC), exposing distributed infrastructures to novel attack vectors. This survey provides comprehensive analysis of quantum-safe cloud security, examining vulnerabilities, transition strategies, and layer-specific countermeasures across nine architectural layers (application, data, runtime, middleware, OS, virtualization, server, storage, networking). We employ STRIDE-based risk assessment aligned with NIST SP 800-30 to evaluate quantum threats through three transition phases: pre-transition (classical cryptography vulnerabilities), hybrid (migration risks), and post-transition (PQC implementation weaknesses including side-channel attacks). Our security framework integrates hybrid cryptographic strategies (algorithmic combiners, dual/composite certificates, protocol-level migration), cryptographic agility, and risk-prioritized mitigation tailored to cloud environments. We benchmark NIST-standardized PQC algorithms for performance and deployment suitability, assess side-channel and implementation vulnerabilities, and analyze quantum-safe strategies from leading CSPs (AWS, Azure, GCP). The survey delivers layer-specific threat taxonomies, likelihood-impact risk matrices, and CSP-informed deployment roadmaps for cloud architects, policymakers, and researchers. We identify six critical research directions: standardization and interoperability, hardware acceleration and performance optimization, AI-enhanced security and threat mitigation, integration with emerging cloud technologies, systemic preparedness and workforce development, and migration frameworks with crypto-agility.

Maad Ebrahim, Abdelhakim Hafid

Real-time Internet of Things (IoT) applications require real-time support to handle the ever-growing demand for computing resources to process IoT workloads. Fog Computing provides high availability of such resources in a distributed manner. However, these resources must be efficiently managed to distribute unpredictable traffic demands among heterogeneous Fog resources. This paper proposes a fully distributed load-balancing solution with Multi-Agent Reinforcement Learning (MARL) that intelligently distributes IoT workloads to optimize the waiting time while providing fair resource utilization in the Fog network. These agents use transfer learning for life-long self-adaptation to dynamic changes in the environment. By leveraging distributed decision-making, MARL agents effectively minimize the waiting time compared to a single centralized agent solution and other baselines, enhancing end-to-end execution delay. Besides performance gain, a fully distributed solution allows for a global-scale implementation where agents can work independently in small collaboration regions, leveraging nearby local resources. Furthermore, we analyze the impact of a realistic frequency to observe the state of the environment, unlike the unrealistic common assumption in the literature of having observations readily available in real-time for every required action. The findings highlight the trade-off between realism and performance using an interval-based Gossip-based multi-casting protocol against assuming real-time observation availability for every generated workload.

Reyhane Askari Hemmat, Abdelhakim Hafid

Service level agreement (SLA) is an essential part of cloud systems to ensure maximum availability of services for customers. With a violation of SLA, the provider has to pay penalties. In this paper, we explore two machine learning models: Naive Bayes and Random Forest Classifiers to predict SLA violations. Since SLA violations are a rare event in the real world (~0.2 %), the classification task becomes more challenging. In order to overcome these challenges, we use several re-sampling methods. We find that random forests with SMOTE-ENN re-sampling have the best performance among other methods with the accuracy of 99.88 % and F_1 score of 0.9980.