arXiv subjects

Amir Khorsandi

Publications and source records attributed to Amir Khorsandi.

2 recordsLinked to original sources

arXiv2025

Beyond Dedicated-Active: A General Reliability Provisioning Framework for SFC Placement in Fog Computing

The explosive growth of Internet of Things (IoT) devices has strained traditional cloud infrastructures, highlighting the need for low-latency and energy-efficient alternatives. Fog computing addresses this by placing computation near the network edge. However, limited and heterogeneous fog resources pose reliability challenges, especially for mission-critical applications. On the other hand, to improve flexibility, applications are deployed as Service Function Chains (SFCs), where each function runs as a Virtual Network Function (VNF). While scalable, this approach is more failure-prone than monolithic deployments, necessitating intelligent redundancy and placement strategies. This paper addresses the reliability-aware SFC placement problem over heterogeneous fog servers through the lens of reliability theory. We explore four redundancy strategies, combining shared vs. dedicated and active vs. standby modes, and propose a general framework to minimize latency and cost while meeting reliability and deadline constraints. The problem is formulated as an Integer Non-Linear Program (INLP), and two genetic algorithm (GA)-based solutions are developed. Simulation results show that shared-standby redundancy outperforms the conventional dedicated-active approach by up to 84%.

cs.NI↗

arXiv2023

Optimization of Residential Demand Response Program Cost with Consideration for Occupants Thermal Comfort and Privacy

Residential consumers can use the demand response program (DRP) if they can utilize the home energy management system (HEMS), which reduces consumer costs by automatically adjusting air conditioning (AC) setpoints and shifting some appliances to off-peak hours. If HEMS knows occupancy status, consumers can gain more economic benefits and thermal comfort. However, for the building occupancy status, direct sensing is costly, inaccurate, and intrusive for residents. So, forecasting algorithms could serve as an effective alternative. The goal of this study is to present a non-intrusive, accurate, and cost-effective approach, to develop a multi-objective simulation model for the application of DRPs in a smart residential house, where (a) electrical load demand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints, and (c) , worst cases scenario approach is very conservative. Because that is unlikely all uncertain parameters take their worst values at all times. So, the flexible robust counterpart optimization along with uncertainty budgets is developed to consider uncertainty realistically. Simulated results indicate that considering uncertainty increases the costs by 36 percent and decreases the AC temperature setpoints. Besides, using DRPs reduces demand by shifting some appliance operations to off-peak hours and lowers costs by 13.2 percent.

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