Visible to the public Analysis of Java Lock Performance Metrics Classification

TitleAnalysis of Java Lock Performance Metrics Classification
Publication TypeConference Paper
Year of Publication2022
AuthorsHuang, Pinguo, Fu, Min
Conference Name2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)
Date Publisheddec
Keywordsclassification, composability, Concurrency, Data security, Instruction sets, Java, Java locks, Measurement, Memory management, Metrics, Performance analysis, pubcrawl, resilience, Resiliency, security, synchronization mechanism, Systematics, Taxonomy
Abstract

Java locking is an essential functionality and tool in the development of applications and systems, and this is mainly because several modules may run in a synchronized way inside an application and these modules need a good coordination manner in order for them to run properly and in order to make the whole application or system stable and normal. As such, this paper focuses on comparing various Java locking mechanisms in order to achieve a better understanding of how these locks work and how to conduct a proper locking mechanism. The comparison of locks is made according to CPU usage, memory consumption, and ease of implementation indicators, with the aim of providing guidance to developers in choosing locks for different scenarios. For example, if the Pessimistic Locks are used in any program execution environment, i.e., whenever a thread obtains resources, it needs to obtain the lock first, which can ensure a certain level of data security. However, it will bring great CPU overhead and reduce efficiency. Also, different locks have different memory consumption, and developers are sometimes faced with the need to choose locks rationally with limited memory, or they will cause a series of memory problems. In particular, the comparison of Java locks is able to lead to a systematic classification of these locks and can help improve the understanding of the taxonomy logic of the Java locks.

DOI10.1109/ISAIEE57420.2022.00090
Citation Keyhuang_analysis_2022