arXiv subjects

Hadi Tabatabaee Malazi

Publications and source records attributed to Hadi Tabatabaee Malazi.

3 recordsLinked to original sources

arXiv2026

Orchestrating Serverless Applications in the Edge Cloud Space Continuum: What Breaks and What is Next?

Serverless computing has matured into an effective execution model for edge cloud environments, enabling function level decomposition, demand driven scaling, and workflow execution across stable, well provisioned infrastructure. This success motivates extending it to the edge cloud space continuum, where Low Earth Orbit (LEO) constellations are increasingly explored as distributed compute substrates. However, existing serverless orchestration is not directly applicable in this setting, where LEO systems impose time varying contact graphs, intermittent link availability, and strict feasibility constraints on energy, memory, communication, and operational cost. This article identifies ten broken assumptions in existing serverless orchestration and organizes them into three core challenges: spatiotemporal execution over dynamic graphs, constraint aware function placement and scaling, and correctness and progress under decentralized and delayed state. It then proposes an architecture that enables robust and efficient serverless execution across the continuum, grounded in these challenges and demonstrated through a representative flood response use case.

cs.DC↗

arXiv2026

ASA: Adaptive Smart Agent Federated Learning via Device-Aware Clustering for Heterogeneous IoT

Federated learning (FL) has become a promising answer to facilitating privacy-preserving collaborative learning in distributed IoT devices. However, device heterogeneity is a key challenge because IoT networks include devices with very different computational powers, memory availability, and network environments. To this end, we introduce ASA (Adaptive Smart Agent). This new framework clusters devices adaptively based on real-time resource profiles and adapts customized models suited to every cluster's capability. ASA capitalizes on an intelligent agent layer that examines CPU power, available memory, and network environment to categorize devices into three levels: high-performance, mid-tier, and low-capability. Each level is provided with a model tuned to its computational power to ensure inclusive engagement across the network. Experimental evaluation on two benchmark datasets, MNIST and CIFAR-10, proves that ASA decreases communication burden by 43% to 50%, improves resource utilization by 43%, and achieves final model accuracies of 98.89% on MNIST and 85.30% on CIFAR-10. These results highlight ASA's efficacy in enhancing efficiency, scalability, and fairness in heterogeneous FL environments, rendering it a suitable answer for real-world IoT apps.

cs.NI↗

arXiv2019

Raft Consensus Algorithm: an Effective Substitute for Paxos in High Throughput P2P-based Systems

One of the significant problem in peer-to-peer databases is collision problem. These databases do not rely on a central leader that is a reason to increase scalability and fault tolerance. Utilizing these systems in high throughput computing cause more flexibility in computing system and meanwhile solve the problems in most of the computing systems which are depend on a central nodes. There are limited researches in this scope and they seem are not suitable for using in a large scale. In this paper, we used Cassandra which is a distributed database based on peer-to-peer network as a high throughput computing system. Cassandra uses Paxos to elect central leader by default that causes collision problem. Among existent consensus algorithms Raft separates the key elements of consensus, such as leader election, so enforces a stronger degree of coherency to reduce the number of states that must be considered, such as collision.

cs.DC↗