Tag: Clustering

SUSCOM’18 Paper: Program Power Profiling Based on Phase Behaviors

Program Power Profiling Based on Phase Behaviors, Xiaobin Ma, Zhihui Du, and Jason Liu. Sustainable Computing, Informatics and Systems, doi:10.1016/j.suscom.2018.05.001 – 17 May 2018. To appear. [preprint]

Abstract

Power profiling tools based on fast and accurate workload analysis can be useful for job scheduling and resource allocation aiming to optimize the power consumption of large-scale, high-performance computer systems. In this article, we propose a novel method for predicting the power consumption of a complete workload or application by extrapolating the power consumption of only a few code segments of the same application obtained from measurements. As such, it provides a fast and yet effective way for predicting the power consumption of the execution of both single and multi-threaded programs on arbitrary architectures without having to profile the entire program’s execution. The latter would be costly to obtain, especially if it is a long-running program. Our method employs a set of code analysis tools to capture the program’s phase behavior and then uses a multi-variable linear regression method to estimate the power consumption of the entire program. For validation, we select the SPEC 2006 benchmark suite and the NAS parallel benchmarks to evaluate the accuracy and effectiveness of our method. Experimental results on three generations of multicore processors show that our power profiling method achieves good accuracy in predicting program’s energy use with relatively small errors.

Bibtex

@Article{suscom18,
AUTHOR = {Ma, Xiaobin and Du, Zhihui and Liu, Jason},
TITLE = {Program Power Profiling Based on Phase Behaviors},
JOURNAL = {Sustainable Computing, Informatics and Systems},
URL = {https://doi.org/10.1016/j.suscom.2018.05.001},
DOI = {10.1016/j.suscom.2018.05.001}
}

IEEECloud’18 Paper: Detecting Containerized Application Dependencies

A Toolset for Detecting Containerized Application’s Dependencies in CaaS Clouds, Pinchao Liu, Liting Hu, Hailu Xu, Zhiyuan Shi, Jason Liu, Qingyang Wang, Jai Dayal, and Yuzhe Tang. In Proceedings of the 2018 IEEE International Conference on Cloud Computing (IEEE CLOUD 2018), July 2018. [to appear]

Abstract

There has been a dramatic increase in the popularity of Container as a Service (CaaS) clouds. The CaaS multi-tier applications could be optimized by using network topology, link or server load knowledge to choose the best endpoints to run in CaaS cloud. However, it is difficult to apply those optimizations to the public datacenter shared by multi-tenants. This is because of the opacity between the tenants and the datacenter providers: Providers have no insight into tenant’s container workloads and dependencies, while tenants have no clue about the underlying network topology, link, and load. As a result, containers might be booted at wrong physical nodes that lead to performance degradation due to bi-section bandwidth bottleneck or co-located container interference. We propose `DocMan’, a toolset that adopts a black-box approach to discover container ensembles and collect information about intra-ensemble container interactions. It uses a combination of techniques such as distance identification and hierarchical clustering. The experimental results demonstrate that DocMan enables optimized containers placement to reduce the stress on bi-section bandwidth of the datacenter’s network. The method can detect container ensembles at low cost and with 92% accuracy and significantly improve performance for multi-tier applications under the best of circumstances.

Bibtex

@inproceedings{cloud18-docman,
title = {A Toolset for Detecting Containerized Application's Dependencies in CaaS Clouds},
author = {Pinchao Liu and Liting Hu and Hailu Xu and Zhiyuan Shi and Jason Liu and Qingyang Wang and Jai Dayal and Yuzhe Tang},
booktitle = {Proceedings of the 2018 IEEE International Conference on Cloud Computing (IEEE CLOUD 2018)},
month = {July},
year = {2018}
}