ABSTRACT
The emergence of large-scale software deployments in the cloud has led to several challenges: (1) measuring software performance in the data center, and (2) optimizing software for resource management. This tutorial addresses the two challenges by bringing the knowledge of software performance monitoring in the data center to the world of applying performance analytics. It introduces data transformations for software performance metrics. The transformations enable effective applications of analytics. This tutorial starts with software performance in the small and ends with applying analytics to software performance in the large. In software performance in the small, it summarizes performance tools, data collection and manual analysis. Then it describes monitoring tools that are helpful in performance analysis in the large. The tutorial will guide the audience in applying analytics to performance data obtained by common tools. This tutorial describes how to select analytical methods and what precautions should be taken to get effective results.
- J.P. Buzen and A.W. Shum, "MASF: multivariate adaptive statistical filtering," in Int. Computer Measurement Group (CMG) Conf., Nashville, TN, USA, Dec. 4--8, pp. 1--10, 1995.Google Scholar
- Owen Vallis, Jordan Hochenbaum, Arun Kejariwal, A novel technique for long-term anomaly detection in the cloud, Proceedings of the 6th USENIX conference on Hot Topics in Cloud Computing, p.15--15, June 17--18, 2014, Philadelphia, PA Google ScholarDigital Library
- Chengwei Wang, Krishnamurthy Viswanathan, Lakshminarayan Choudur, Vanish Talwar, Wade Satterfield, and Karsten Schwan. 2011. Statistical techniques for online anomaly detection in data centers. In Proceedings of the IFIP/IEEE International Symposium on Integrated Network Management (IM'11). IEEE, 385--392.Google ScholarCross Ref
- Kingsum Chow, Pooja Jain and Khun Ban, "Tutorial: Java Application Performance in the Data Center, Data Collection and Analysis" CMG imPACt 2016 Conference. November 7--10, 2016 in La Jolla, California.Google Scholar
- Kingsum Chow, Li Chen and Colin Cunningham, "How We Coach Performance Engineers to Adopt Data Science in Reproducible Analytics", Intel Analytics Summit, March 22--24, 2016, Santa Clara, California.Google Scholar
- Kingsum Chow and Pranita Maldikar "Applying Analytics to Workload Optimized Systems" {best paper award} presented at the FastPath workshop, held in conjunction with the IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), March 23-25, 2014.Google Scholar
- Keerthi Palanivel, Kingsum Chow, Khun Ban and David Lilja, "A Stepwise Approach to Software-Hardware Performance Co-Optimization Using Design of Experiments", Computer Measurement Group Performance and Capacity 2013, Nov 5th to 7th, La Jolla, California, USA.Google Scholar
- Shruthi Deshpande, Kingsum Chow, Peng-fei Chuang and Latifur Khan, "Big Data Analysis to Characterize Workload using Machine Learning Algorithms for High Dimensional Performance Data", Computer Measurement Group Performance and Capacity 2013, Nov 5th to 7th, La Jolla, California, USA.Google Scholar
- Cormen, Thomas H., Charles Eric. Leiserson, and Ronald L. Rivest. Introduction to Algorithms DC. Cambridge, MA: MIT, 1989. Print. Google ScholarDigital Library
- Wickham, Hadley. Advanced R. Boca Raton, Fla.: Chapman & Hall, 2015. Print.Google Scholar
- Hennessy, John L., David A. Patterson, and Krste Asanović. Computer Architecture: A Quantitative Approach. Amsterdam: Elsevier, 2012. Print. Google ScholarDigital Library
- Gregg, Brendan. Systems Performance: Enterprise and the Cloud. Upper Saddle River, NJ: Prentice Hall, 2014. Print Google ScholarDigital Library
Index Terms
- Software Performance Analytics in the Cloud
Recommendations
Developing Software Performance Training at Alibaba
ICPE '17 Companion: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering CompanionEffective software performance analysis needs to be conducted by crossing multiple disciplines such as algorithms, data structures, effective coding, performance data collection and its associated overheads, computer architecture, operating systems, ...
Big data analytics in Cloud computing: an overview
AbstractBig Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable ...
Towards Cloud-Based Analytics-as-a-Service (CLAaaS) for Big Data Analytics in the Cloud
BIGDATACONGRESS '13: Proceedings of the 2013 IEEE International Congress on Big DataData Analytics has proven its importance in knowledge discovery and decision support in different data and application domains. Big data analytics poses a serious challenge in terms of the necessary hardware and software resources. The cloud technology ...
Comments