In this paper, we propose efficient scheduling algorithms that reduce the cost of resource usage in a cloud-deployed Apache Spark cluster.
In this paper, we propose efficient scheduling algorithms that reduce the cost of resource usage in a cloud-deployed Apache Spark cluster. In addition, the ...
In this paper, we propose efficient scheduling algorithms that reduce the cost of resource usage in a cloud-deployed Apache Spark cluster. In addition, the ...
The default big data processing framework schedulers fail to reduce the cost of VM usages in the cloud environment while satisfying the performance constraints ...
Oct 22, 2024 · In this paper, we formulate the job scheduling problem of a cloud-deployed Spark cluster and propose a novel Reinforcement Learning (RL) model ...
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An SLA-based Scheduler for Apache Spark jobs implemented on top of Apache Mesos APIs ... Cost-efficient Dynamic Scheduling of Big Data Applications in Apache ...
In particular, we propose efficient resource allocation and scheduling mecha- nisms for Cloud-deployed Apache Spark clusters. This thesis advances the state-of- ...
MapReduce framework has been one of the most prominent ways for efficient processing large amount of data requiring huge computational capacity.
Jun 22, 2023 · In this paper, a low-cost task scheduling algorithm for Spark based on heterogeneous cloud environment is proposed to minimize cost while improving resource ...
Hence, we introduce our cloud-agnostic system PIVOT with the novel cost-aware scheduling algorithm, which enables data- intensive applications to run and scale ...