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In this paper we present two analytical frameworks for calculating theperformance of lock-free data structures. Lock-free data structures are basedon retry loops and are called by application-specific routines. In contrast toprevious work, we consider in this paper lock-free data structures in dynamicenvironments. The size of each of the retry loops, and the size of theapplication routines invoked in between, are not constant but may changedynamically. The new frameworks follow two different approaches. The firstframework, the simplest one, is based on queuing theory. It introduces anaverage-based approach that facilitates a more coarse-grained analysis, withthe benefit of being ignorant of size distributions. Because of thisindependence from the distribution nature it covers a set of complicateddesigns. The second approach, instantiated with an exponential distribution forthe size of the application routines, uses Markov chains, and is tighterbecause it constructs stochastically the execution, step by step.  Both frameworks provide a performance estimate which is close to what weobserve in practice. We have validated our analysis on (i) several fundamentallock-free data structures such as stacks, queues, deques and counters, some ofthem employing helping mechanisms, and (ii) synthetic tests covering a widerange of possible lock-free designs. We show the applicability of our resultsby introducing new back-off mechanisms, tested in application contexts, and bydesigning an efficient memory management scheme that typical lock-freealgorithms can utilize.

How Lock-free Data Structures Perform in Dynamic Environments: Models and Analyses