Energy-Efficient and Security-Aware Task Offloading for Multi-Tier Edge-Cloud Computing Systems

Waleed, AL-Museelem

doi:10.1109/access.2023.3290139

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…IDS models detect malicious activities by monitoring network traffic and system behaviours. While IDS models are effective in identifying known attacks, they may not perform well against new or sophisticated attacks, as they rely on predefined rules and signatures [27,28,29].…”

Section: Literature Surveymentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

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As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the Load Balancing and Energy-Efficient Migration Model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic Algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a Deep Reinforcement Learning-based Iterative-learning Contextual Side chaining Model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means Clustering and Fuzzy Logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ Bidirectional Long Short-Term Memory networks with Recurrent Graph Neural Network, for accurate workload prediction and informed migration decision Making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved by 2.5%, and the delay of computation was significantly reduced by 9.5%. Importantly, the integration of the Iterative-learning Contextual Side chaining Model significantly enhanced the security and quality of service (QoS) under attack scenarios, resulting in a 10.4% improvement in response speed, a 2.5% reduction in energy consumption during block mining, a 3.9% improvement in throughput, and an 8.5% reduction in storage costs. This Load Balancing and Energy-Efficient Migration Model represents a significant advancement in addressing the challenges of load balancing, energy efficiency, and security in VM migrations. Through the meticulous integration of bioinspired algorithms, advanced security measures, and machine learning techniques, our model provides a comprehensive and innovative solution that markedly improves system performance, reduces energy consumption, and fortifies security, thereby paving the way for a more efficient and secure cloud computing ecosystem.

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Section: Literature Surveymentioning

confidence: 99%

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

Brahmam,

2024

IEEE Access

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“…However, this scheme is vulnerable to both active and passive attacks and lacks support for SO. Almuseelem proposed an energy-efficient and security-conscious computing system in 2023 28 . This approach utilizes cloud and fog computing for offloading but does not address security concerns.…”

Section: Related Workmentioning

confidence: 99%

A new secure offloading approach for internet of vehicles in fog-cloud federation

Salami,

Khajehvand,

Zeinali

2024

Sci Rep

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The Internet of Vehicles (IoV) plays a crucial role in advancing intelligent transportation systems. However, due to limited processing power, IoV faces challenges in independently handling large volumes of data, necessitating the use of offloading as a solution. Offloading data in wireless environments raises security concerns, highlighting the need for robust data protection mechanisms. This study introduces a secure offloading (SO) scheme within the Fog-Cloud Federation for IoV. The proposed NSO-VFC scheme undergoes both informal and formal analysis using the Avispa tool, demonstrating resilience against active and passive attacks. Performance evaluations indicate that the security measures of NSO-VFC meet acceptable standards compared to similar approaches. Nonetheless, the heightened focus on security incurs higher computational and communication costs than alternative strategies. Simulation experiments using the NS3 tool involve varying numbers of IoVs (50, 70, and 100), revealing that increased IoV density correlates with enhanced packet delivery rates and throughput within the NSO-VFC scheme.

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Healthbot- An AI based chatbot using Webspider and Mediglobe

Reddy,

Singh,

Muralidharan

2024

2024 10th International Conference on Communication and Signal Processing (ICCSP)

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Energy-Efficient and Security-Aware Task Offloading for Multi-Tier Edge-Cloud Computing Systems

Cited by 4 publications

References 42 publications

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

A new secure offloading approach for internet of vehicles in fog-cloud federation

Healthbot- An AI based chatbot using Webspider and Mediglobe

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