Learning Driven Parallelization for Large-Scale Video Workload in Hybrid CPU-GPU Cluster

ABSTRACT

Hybrid CPU-GPU cluster has become a promising computing paradigm for large-scale video analytics. However, the uncertainty and variability of workloads and heterogeneous resources in the cluster can lead to the unbalanced use of the hybrid computing resources and further cause the performance degradation of the computing platform. This problem becomes more challenging with the computation complexity and dependencies of video tasks in the hybrid cluster. In this paper, we focus on the video workload parallelization problem with fine-grained task division and feature description in the hybrid CPU-GPU cluster. Firstly, for achieving high resource utilization and task throughput, we propose a two-stage video task scheduling approach based on deep reinforcement learning. In our approach, a task execution node is selected by the cluster-level scheduler for the mutually independent video tasks, and then the node-level scheduler assigns the interrelated video subtasks to the appropriate computing units. By using the deep Q-network, the two-stage scheduling model is online learned to perform the current optimal scheduling actions according to the runtime status of cluster environments, the characteristics of video tasks, and the dependencies between video tasks. Secondly, based on the transfer learning technology, a scheduling strategy generalization method is proposed to efficiently rebuild the task scheduling model referring to the existing model. Finally, we conduct the extensive experiments to analyze the impact of the model parameters on the scheduling actions, and then the experimental results also validate that our learning based task scheduling approach outperforms the other widely used methods.

References

1. R. Bleuse, S. Hunold, S. Kedad-Sidhoum, F. Monna, G. Mounié, and D. Trystram. 2017. Scheduling Independent Moldable Tasks on Multi-Cores with GPUs. IEEE Transactions on Parallel and Distributed Systems 28, 9 (2017), 2689--2702.
2. L. Bottou. 2012. Stochastic Gradient Descent Tricks. In Neural Networks: Tricks of the Trade. 421--436.
3. M. X. Cheng, J. Li, and S. H. Nazarian. 2018. DRL-Cloud: Deep Reinforcement Learning-based Resource Provisioning and Task Scheduling for Cloud Service Providers. In ASP-DAC. 129--134.
4. M. Ciznicki, K. Kurowski, and J. Weglarz. 2017. Energy Aware Scheduling Model and Online Heuristics for Stencil Codes on Heterogeneous Computing Architectures. Cluster Computing 20, 3 (2017), 2535--2549.
5. R. Salakhutdinov E. Parisotto, J. L. Ba. 2016. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning. In arXiv preprint arXiv:1511.06342.
6. D. P. Fu, Y. H. Xiong, C. D. Lu, M. Wu, and K. Y. Jiang. 2016. A Task Scheduling Method for Energy-Efficient Cloud Video Surveillance System Using A Time-Clustering-Based Genetic Algorithm. In IEEE ICPADS. 661--668.
7. J. Fung and S. Mann. 2008. Using Graphics Devices in Reverse: GPU-based Image Processing and Computer Vision. In IEEE ICME. 9--12.
8. Y. Y. Gao, H. T. Zhang, Y. P. Zhu, B. C. Tang, and H. D. Ma. 2017. A Load-Aware Data Migration Scheme for Distributed Surveillance Video Processing with Hybrid Storage Architecture. In IEEE HPCC. 563--570.
9. M. Hussin, Y. C. Lee, and A. Y. Zomaya. 2011. Efficient Energy Management using Adaptive Reinforcement Learning-based Scheduling in Large-Scale Distributed Systems. In IEEE ICPP. 385--393.
10. A. Ilic, S. Momcilovic, N. Roma, and L. Sousa. 2014. FEVES: Framework for Efficient Parallel Video Encoding on Heterogeneous Systems. In IEEE ICPP. 20--29.
11. J. W. Liu and H. Y. Shen. 2016. Dependency-Aware and Resource-Efficient Scheduling for Heterogeneous Jobs in Clouds. In IEEE Cloudcom. 110--117.
12. Y. Ma, L. Z. Wang, A. Y. Zomaya, D. Chen, and R. Ranjan. 2014. Task-Tree based Large-Scale Mosaicking for Massive Remote Sensed Imageries with Dynamic DAG Scheduling. IEEE Transactions on Parallel and Distributed Systems 25, 8 (2014), 2126--2137.
13. H. Z. Mao, M. Alizadeh, I. Menache, and S. Kandula. 2016. Resource Management with Deep Reinforcement Learning. In ACM HotNets'16. 50--56.
14. V. Mnih, K. Kavukcuoglu, and D. Silver et al. 2013. Playing Atari with Deep Reinforcement Learning. arXiv preprint arXiv:1312.5602 (2013).
15. A. Naithani, S. Eyerman, and L. Eeckhout. 2017. Reliability-Aware Scheduling on Heterogeneous Multicore Processors. In IEEE HPCA. 397--408.
16. S. J. Pan and Q. Yang. 2010. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (2010), 1345--1359.
17. R. Pereira, M. Azambuja, K. Breitman, and M. Endler. 2010. An Architecture for Distributed High Performance Video Processing in the Cloud. In IEEE CLOUD. 482--489.
18. D. Silver, A. Huang, and C. J. Maddison et al. 2016. Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature 529, 7587 (2016), 484--489.
19. X. D. Song, X. L. Peng, J. Z. Xu, G. G. Shi, and F. Wu. 2015. Cloud-based Distributed Image Coding. IEEE Transactions on Circuits and Systems for Video Technology 25, 12 (2015), 1926--1940.
20. G. Teodoro, T. M. Kurc, T. Pan, L. A. Cooper, J. Kong, P. Widener, and J. H. Saltz. 2012. Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems. In IEEE IPDPS. 1093--1104.
21. G. Teodoro, T. Pan, T. M. Kurc, J. Kong, and L. A. Cooper. 2013. High-Throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms. In IEEE IPDPS. 103--114.
22. Z. Tong, Z. Xiao, K. Li, and K. Li. 2014. Proactive Scheduling in Distributed Computing---A Reinforcement Learning Approach. Elsevier Journal of Parallel and Distributed Computing 74, 7 (2014), 2662--2672.
23. Y. H. Xiong, S. Y. Wan, J. H. She, M. Wu, and Y. He. 2016. An Energy-Optimization-based Method of Task Scheduling for a Cloud Video Surveillance Center. Journal of Network and Computer Applications 59 (2016), 63--73.
24. H. B. Yang, J. H. Guo, C. Liang, Z. Lei, and C. S. Wang. 2016. An Optimization of the Delay Scheduling Algorithm for Real-Time Video Stream Processing. In Frontier Computing. 173--183.
25. H. T. Zhang, B. Xu, J. Yan, L. J. Liu, and H. D. Ma. 2016. Proactive Data Placement for Surveillance Video Processing in Heterogeneous Cluster. In IEEE Cloudcom. 206--213.
26. H. T. Zhang, J. Yan, and Y. Kou. 2016. Efficient Online Surveillance Video Processing Based on Spark Framework. In Bigcom. 309--318.
27. L. X. Zhang, K. L. Li, Y. M. Xu, J. Mei, and F. Zhang. 2015. Maximizing Reliability with Energy Conservation for Parallel Task Scheduling in a Heterogeneous Cluster. Information Sciences 319 (2015), 113--131.
28. T. Zhang and J. Li. 2015. Online Task Scheduling for LiDAR Data Preprocessing on Hybrid GPU/CPU Devices: A Reinforcement Learning Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, 1 (2015), 386--397.