Advanced Techniques for Storage Optimization in Resource-Constrained Systems Using AI and Machine Learning
Keywords:
Resource-constrained systems, storage optimizationAbstract
Data volume and complexity are rising in resource-constrained systems, making storage management difficult. IoT, edge, and mobile devices use these systems. Their memory, cognition, and energy are limited. Traditional storage management systems waste resources, delay performance, and limit data access. This paper explores advanced AI and ML techniques for resource-constrained storage optimization. We boost storage efficiency and system performance in resource-constrained contexts. Resource-constrained systems struggle with storage. Storage, processing, and energy limits are investigated. We analyze how these limits affect data access latency, retrieval throughput, and system responsiveness. These systems' storage management is next. Caching, prefetching, and compression are examples. Traditional methods are successful, but they lack the flexibility and dynamic decision-making needed to maximize storage under changing data access patterns and system resource variations.
Studying AI and ML integration into resource-constrained storage management systems addresses these restrictions. Learning and adapting AI may improve storage and system efficiency. AI/ML storage optimization is in the study. Data compression via machine learning is prevalent. ML systems can dynamically pick compression algorithms by training on data kinds and access patterns. Resource-constrained scenarios need optimum compression and little computational cost. Machine learning is crucial for clever caching. Traditional caching uses static rules or heuristics. Certain solutions may not support dynamic access patterns. Data requests may be predicted by machine learning from prior access patterns. Caching frequently used data using ML reduces wait time and boosts system performance.
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