Garbage collection: algorithms for automatic dynamic memory management
Garbage collection: algorithms for automatic dynamic memory management
On the Optimum Checkpoint Interval
Journal of the ACM (JACM)
A Comprehensive Model for Software Rejuvenation
IEEE Transactions on Dependable and Secure Computing
Quantifying the performance of garbage collection vs. explicit memory management
OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Structured Markov chains solver: software tools
SMCtools '06 Proceeding from the 2006 workshop on Tools for solving structured Markov chains
Nonblocking real-time garbage collection
ACM Transactions on Embedded Computing Systems (TECS)
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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Garbage collection represents an important feature of many modern systems that allows programmers to avoid explicit memory management and related common errors. However, its usage introduces problems in software performance engineering, because on the one hand the system exhibits better performances when there is low memory occupancy and on the other hand the garbage collection activity blocks other processes, so it may introduce a performance drawback. We propose a queueing model to analyse a system with a garbage collector, where customers arrive according to a Poisson process and the service time distribution depends on the amount of free memory. Customer arrivals correspond to process activations in the system. The model parameters allow one to specify the garbage collector service rate, and the distribution of the delays between successive activations. We show that the process underlying such a queueing model is a Quasi-Birth-Death stochastic process and we derive the steady-state analysis via Matrix Geometric Methods. Finally, we propose a heuristic based on this model to derive an appropriate and effective garbage collector activation rate in order to minimise the average system response time. The parametrisation is done using system statistics.