Def-DReL: Towards a sustainable serverless functions deployment strategy for fog-cloud environments using deep reinforcement learning

Dehury, Chinmaya Kumar; Poojara, Shivananda; Srirama, Satish Narayana

doi:10.1016/j.asoc.2023.111179

Cited by 6 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RL algorithms can optimize network performance by dynamically adjusting caching strategies based on continuous learning from environmental feedback. This signifies a notable deviation from the unchanging nature of conventional approaches, demonstrating the capacity of RL to greatly improve the efficiency and dependability of VoD services [6], [17].…”

Section: Related Workmentioning

confidence: 99%

“…The combination of federated learning, edge computing, and Multi-Agent RL (MARL) systems has the potential to significantly transform network optimization. These developments indicate a future in which the delivery of video material is not only more efficient, but also takes privacy into consideration and can adapt to different network conditions [3], [6], [7], [24]. This research review emphasizes the significant impact of machine learning, specifically reinforcement learning, in surpassing conventional video caching techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Conventional caching algorithms, like Least Recently Used (LRU) and Least Frequently Used (LFU), have a drawback in that they are fixed and cannot anticipate or adjust to the changing patterns of content popularity. On the other hand, a reinforcement learning (RL)-based system has the ability to continuously improve caching decisions in real-time by analyzing user interactions, content access patterns, and network circumstances [4], [5], [6], [7], [8]. This proactive caching strategy not only reduces the delay and amount of data transferred but also enhances the overall effectiveness of content delivery networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Video Caching at The Edge of Network by Using Machine Learning

Ullah,

Xiaopeng,

Khan

2024

Preprint

View full text Add to dashboard Cite

Efficiently managing network resources in the dynamic field of video-on-demand (VoD) services is a significant challenge. This requires creative methods to optimize video caching strategies. This study examines the use of machine learning (ML), particularly reinforcement learning (RL), into edge network designs to improve video caching procedures. The decentralized, adaptable, and learning characteristics of machine learning offer a powerful method for effectively managing caching in real-time. This technique leads to reduced latency, optimized bandwidth utilization, and improved cache hit rates. Conventional caching algorithms frequently fail to adjust to the changing needs and popularity of material that is inherent in Video on Demand (VoD) services. Our study presents an innovative reinforcement learning (RL) caching method that is specifically developed to automatically adapt caching choices using real-time data analysis, thus guaranteeing an effective content delivery network. We created and evaluated three separate machine learning models: Convolutional Neural Networks (CNN) for extracting features, Deep Q-Networks (DQN) for generating decisions, and a hybrid model that combines the advantages of both CNN and DQN to achieve improved performance. By conducting thorough simulations and implementing our models in real-world deployment scenarios, we have shown substantial improvements compared to traditional caching methods in terms of scalability, adaptability to evolving user patterns, and overall network efficiency. This innovative method not only simplifies the transfer of content in Video on Demand (VoD) systems, but also establishes a new standard for integrating Artificial Intelligence (AI) technologies in network optimization. It effectively tackles present and future obstacles in the digital streaming industry.

show abstract

Section: Related Workmentioning

confidence: 99%