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Distributed cloud environments running data-intensive applications often slow down because of network congestion, uneven bandwidth, and data shuffling between nodes. Traditional host metrics such as CPU or memory do not capture these factors. Scheduling without considering network conditions causes poor placement, longer data transfers, and weaker job performance. This work presents a network-aware job scheduler that uses supervised learning to predict job completion time. The system collects real-time telemetry from all nodes, uses a trained model to estimate how long a job would take on each node, and ranks nodes to choose the best placement. The scheduler is evaluated on a geo-distributed Kubernetes cluster on the FABRIC testbed using network-intensive Spark workloads. Compared to the default Kubernetes scheduler, which uses only current resource availability, the supervised scheduler shows 34–54% higher accuracy in selecting the optimal node. The contribution is the demonstration of supervised learning for real-time, network-aware job scheduling on a multi-site cluster.
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Optimizing Performance and Computing Resource Management of In-memory Big Data Analytics with Disaggregated Persistent Memory
The performance of modern Big Data frameworks, e.g. Spark, depends greatly on high-speed storage and shuffling, which impose a significant memory burden on production data centers. In many production situations, the persistence and shuffling intensive applications can suffer a major performance loss due to lack of memory. Thus, the common practice is usually to over-allocate the memory assigned to the data workers for production applications, which in turn reduces overall resource utilization. One efficient way to address the dilemma between the performance and cost efficiency of Big Data applications is through data center computing resource disaggregation. This paper proposes and implements a system that incorporates the Spark Big Data framework with a novel in-memory distributed file system to achieve memory disaggregation for data persistence and shuffling. We address the challenge of optimizing performance at affordable cost by co-designing the proposed in-memory distributed file system with large-volume DIMM-based persistent memory (PMEM) and RDMA technology. The disaggregation design allows each part of the system to be scaled independently, which is particularly suitable for cloud deployments. The proposed system is evaluated in a production-level cluster using real enterprise-level Spark production applications. The results of an empirical evaluation show that the system can achieve up to a 3.5- fold performance improvement for shuffle-intensive applications with the same amount of memory, compared to the default Spark setup. Moreover, by leveraging PMEM, we demonstrate that our system can effectively increase the memory capacity of the computing cluster with affordable cost, with a reasonable execution time overhead with respect to using local DRAM only.
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- PAR ID:
- 10158247
- Date Published:
- Journal Name:
- 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
- Page Range / eLocation ID:
- 21 to 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/CCGRID.2019.00012
