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Application Performance Management Using Pull-Based Methodology

D. Salangai Nayagi, M. Chandana, Nivedita Adiveppa Kagi, KS. Shri Gowri


In this digital world, all the developing countries' growth has improved elastically with the impact of information technology and their innovative development processes. In the fields of information technology and systems management, an apm system can be developed with ancient traditional methodologies for maintaining the quality of the application and their performance. Their system was developed and has given more profit only with the quality of the tracking of metrics and the tools used for system result. But the drawback is they were spending much time to ensure monitoring of system level resources, unable to give any intuitions on how micro-service is performing, also the user needs more knowledge to operate it and becomes difficult to understand bottlenecks in micro-service which if not handled at initial stages, can cause irreversible damage at scale. Moreover, more space was used for making an application check with huge manpower is required for maintaining the entire system, which is synchronous. Most of the countries are moved to pull-based methodology which is an asynchronous concepts with Web platforms for optimizing the time and techniques. In that Application performance management is the best innovative idea to allow users to consistently track performance metrics with minimal knowledge. Time-series database which is a InfluxDB  used for storing the data in the form of data points with user interface, so that the end-user outlook the performance at regular intervals will provide better results as well as no need to wait for a long time for tracking of metrics. This method was implemented in most of the countries that were developing apm with pull-based technology with less manpower and low cost. The application performance management methodology is implemented by an IT team, for to optimize the performance of their system as well as to sense and fix any issues in it quickly. This technology is used to increase the productivity of the system with a small space of apm and less manpower. Perhaps the cost of the entire system has been taken into the consideration by using apm which provides better results when compared with previous classical methods. This research paper has given the design and implementation of application performance managements with pull-based methodology and their analytics will be done using user interface visualization.


Application Performance Monitoring, Performance, Optimize, Metrics, Micro-service, Bottlenecks, End User Experience.

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Mandar Sahasrabudhe, Meenakshi Panvar, Sagar Chaudari (2013, September). Application Performance monitoring and prediction. In International Conference on Signal Processing, Computing and Control.

Vidroha Debroy, Alireza Mansoori, James Haleblian, Mark Wilkens (2020, October). Challenges Faced with Application Performance Monitoring when migrating to the cloud. In IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).

Meenakshi Panvar (2013, November).Application Performance Management Emerging Trends. In International Conference on Cloud and Ubiquitous Computing and Emerging Technologies.

Subramanyeswara Rao Dasari, Sasirekha G.V.K (2017, March). Application Performance Monitoring in Software Defined Networks. In 26th International Telecommunication Networks and Applications Conference (ITNAC).

C Steigner, J Wilke, I Wulff (2000, October). Integrated performance monitoring of client/server software. In 1st European Conference on Universal Multiservice Networks ECUMN’2000(Cat. No.00EX423).

Chao Wang, Lili Su, Xue Zhao, Ying Zhang (2015, March). Application Performance Monitoring and Analyzing Based on Bayesian Network. In 11th Web Information System and Application Conference.

Miroslay Zivkovic, Charles Loomis, Yuri Demchenko (2017, January). Runtime Application Performance Management for Multi-Cloud Cyclone Environment. In international conference on cloud computing technology and science (cloudcom).

Lingyun Yang, J.M.Schoof, C.L.Dumitrescu, I. Foster (2006, May). Statistical data reduction for efficient application performance monitoring. In Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

Yujun Shi, Kehua Miao (2020, May). Detecting Anomalies in Application Performance Management System with Machine Learning Algorihms. In 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE).

Wooram Ann, Prabhat Awasthi, Jihun Chae, Jaegil Lee, Taeyoung Lee, Jinyong Ahn (2020, December). Performance Analysis and Management System Architectural Design. In IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia).

Yutian Tang, Haoyu Wang, Xian Zhan, Xiapu Luo, Yajin Zhou, Hao Zhou, Qiben Yan, Yulei Sui, Jacky Wai Keung (2021, May). A Systematical Study on Application Performance Management Libraries for Apps. In IEEE Transactions on Software Engineering (Early Access).

Yutian Tang, Xian Zhan, Hao Zhou , Xiapu Luo, Zhou Xu,Yajin Zhou, Qiben Yan(2019, November). Demystifying Application Performance Management Libraries for Android. In 34th IEEE/ACM International Conference on Automated Software Engineering (ASE).

G. Khanna, K. Beaty, G. Kar, A. Kochut (2006, October). Application Performance Management in Virtualized Server Environments. In IEEE/IFIP Network Operations and Management Symposium NOMS 2006.

Pongsakorn Yoosook, Paskorn Apirukvorapinit(2018, June). Performance monitoring tool for mobile application. In 5th International Conference on Business and Industrial Research (ICBIR).

Tarek M. Ahmed, Cor-Paul Bezemer, Tse-Hsun Chen, Ahmed E. Hassan, Weiyi Shang (2017, January). Studying the Effectiveness of Application Performance Management (APM) Tools for Detecting Performance Regressions for Web Applications-An Experience Report. In IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR).

Tom Shott, Chris Imondi, Ryan Fedie(2012, September). Evaluation of performance management systems for knowledge workers. In Proceedings of PICMET '12: Technology Management for Emerging Technologies.

Saeed Zareian,Rodrigo Veleda, Marin Litoiu, Mark Shtern, Hamoun Ghanbari, Manish Garg.(2015, August). K-Feed - A Data-Oriented Approach to Application Performance Management in Cloud. In IEEE 8th International Conference on Cloud Computing.

Deji Zhao, Bo Ning, Chao Yang (2021, February). Application research on application performance management system in big data of power grid. In International Conference on Distributes system.

Yuan Chen, Daniel Gmach, Chris Hyser, Zhikui Wang, Cullen Bash, Christopher Hoover, Sharad Singhal (2010, June). Integrated management of application performance, power and cooling in data centers. In IEEE Network Operations and Management Symposium - NOMS 2010.

Hemanta Kumar Kalita, Manoj K. Nambiar, Benny Mathew (2012, January). Network Emulation and Simultaneous Monitoring of Web Based Applications Performance Using ScrutiNem. In First International Conference on Informatics and Computational Intelligence.

R. Steinert, D. Gillblad (2012, December). Performance evaluation of a distributed and probabilistic network monitoring approach. In 8th international conference on network and service management (cnsm) and 2012 workshop on systems virtualiztion management (svm).

Pu Huang, Hui Lei, Lipyeow Lim(2006, December). Real Time Business Performance Monitoring and Analysis Using Metric Network. In IEEE International Conference on e-Business Engineering (ICEBE'06).

Pedro Heleno Isolani, Juliano Araujo Wickboldt, Cristiano Bonato Both, Juergen Rochol (2015, July). Interactive monitoring, visualization, and configuration of Open Flow-based SDN. In IFIP/IEEE International Symposium on Integrated Network Management (IM).

Alexander Horch (2007, November) A Maintenance View on Control Performance Monitoring. In IEEE International Conference on Control Applications.

Julie Wenbin Zhu, Patrick G. Bridges, Arthur B. Maccabe (2008, July). Lightweight Online Performance Monitoring and Tuning with Embedded Gossip. In IEEE Transactions on Parallel and Distributed Systems.

K. Kaarela, R. Korhonen, H. Huotari, K. Autio (2002, August). An embedded expert system for performance monitoring of process stations in a distributed process automation system. In Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.

P. Vorreau, J. Leuthold (2005, December). Optical performance monitoring applications in transparent networks. In 14th Annual International Conference on Wireless and Optical Communications, 2005. WOCC 2005.


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