A memory constraint error in a distributed computing environment signifies that the collective RAM of the cluster's nodes is insufficient for the model's processing demands. The most direct solution is to scale the cluster horizontally by adding more nodes. This action increases the total aggregate memory available to the distributed system. The workload can then be partitioned and distributed across a larger number of machines, reducing the memory load on each individual node and resolving the overall memory bottleneck. This is a fundamental principle of scaling distributed data processing frameworks.

A. Converting an on-premises deployment to a containerized deployment packages the application but does not inherently increase the physical memory of the host machines.

B. Migrating to a cloud deployment only provides the potential for scaling; the direct action that resolves the error is provisioning more or larger resources (nodes), not the migration itself.

C. Moving model processing to an edge deployment would likely worsen the problem, as edge devices are typically far more constrained in memory and processing power than cluster nodes.

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Chowdhury

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Shenker

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"RDD Abstraction

" describes how datasets are partitioned across machines in a cluster. Increasing the number of machines (nodes) allows for a greater distribution of these partitions

thus increasing the total memory capacity for the dataset.

2. MIT OpenCourseWare. (2020). 6.824 Distributed Systems

Spring 2020. Lecture 3: GFS. The lecture discusses how Google File System (and by extension

other distributed systems like MapReduce) achieves scalability for large datasets by distributing data and computation across a large number of commodity machines (nodes). Adding nodes is the primary mechanism for scaling.

3. Armbrust

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et al. (2015). Spark SQL: Relational Data Processing in Spark. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15). Association for Computing Machinery

New York

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1383–1394. Section 2

"Programming Model

" explains how Spark's distributed execution allows it to scale "by adding more machines to a cluster." This directly addresses resource constraints like memory. DOI: https://doi.org/10.1145/2723372.2742797