A Review of Data Placement and Replication Strategies Based on Machine Learning

The global increase in data volumes has brought forth the need for scalable distributed systems that can provide satisfactory quality of service. Data placement and replication are well known techniques that provide increased performance, improved fault tolerance and higher availability. These techn...

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Published in:Proceedings - International Conference on Parallel and Distributed Systems pp. 278 - 285
Main Authors: Najjar, Amir, Mokadem, Riad, Pierson, Jean-Marc
Format: Conference Proceeding
Language:English
Published: IEEE 10.10.2024
Subjects:
Costs
Data Placement
Data Replication
Distributed databases
Distributed Systems
Fault tolerance
Fault tolerant systems
Machine Learning
Quality of service
Reinforcement learning
Taxonomy
Time factors
Tuning
Unsupervised learning
ISSN:2690-5965
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Abstract The global increase in data volumes has brought forth the need for scalable distributed systems that can provide satisfactory quality of service. Data placement and replication are well known techniques that provide increased performance, improved fault tolerance and higher availability. These techniques often require threshold-based activation mechanisms that can vary due to the nature of the workload and the underlying system architecture. Hence, setting and adjusting those thresholds usually require human intervention. In this context, machine learning presents a promising facet to automatically define such thresholds to adapt to different workloads and architectures. In this paper, we study the data placement and replication strategies proposed in the literature that employ machine learning. We classify such strategies based on the machine learning method, the platform on which they are deployed, the dynamicity and the achieved objectives. We describe the approach applied by each strategy as well as possible limitations. In addition, we provide insights into metrics used to evaluate the strategies. We highlight the need to design data placement and replication strategies that respond better to modern needs for distributed systems. We also motivate the use of machine learning to achieve autonomy in distributed systems.
AbstractList The global increase in data volumes has brought forth the need for scalable distributed systems that can provide satisfactory quality of service. Data placement and replication are well known techniques that provide increased performance, improved fault tolerance and higher availability. These techniques often require threshold-based activation mechanisms that can vary due to the nature of the workload and the underlying system architecture. Hence, setting and adjusting those thresholds usually require human intervention. In this context, machine learning presents a promising facet to automatically define such thresholds to adapt to different workloads and architectures. In this paper, we study the data placement and replication strategies proposed in the literature that employ machine learning. We classify such strategies based on the machine learning method, the platform on which they are deployed, the dynamicity and the achieved objectives. We describe the approach applied by each strategy as well as possible limitations. In addition, we provide insights into metrics used to evaluate the strategies. We highlight the need to design data placement and replication strategies that respond better to modern needs for distributed systems. We also motivate the use of machine learning to achieve autonomy in distributed systems.
Author Mokadem, Riad
Pierson, Jean-Marc
Najjar, Amir
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Snippet The global increase in data volumes has brought forth the need for scalable distributed systems that can provide satisfactory quality of service. Data...
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StartPage 278
SubjectTerms Costs
Data Placement
Data Replication
Distributed databases
Distributed Systems
Fault tolerance
Fault tolerant systems
Machine Learning
Quality of service
Reinforcement learning
Taxonomy
Time factors
Tuning
Unsupervised learning
Title A Review of Data Placement and Replication Strategies Based on Machine Learning
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