Computer Science > Distributed, Parallel, and Cluster Computing
[Submitted on 4 Apr 2022]
Title:Parameterized algorithm for replicated objects with local reads
View PDFAbstract:We consider the problem of implementing linearizable objects that support both read and read-modify-write (RMW) operations in message-passing systems with process crashes. Since in many systems read operations vastly outnumber RMW operations, we are interested in implementations that emphasize the efficiency of read operations.
We present a parametrized algorithm for partially synchronous systems where processes have access to external clocks that are synchronized within $\epsilon$. With this algorithm, every read operation is local (intuitively, it does not trigger messages). If a read is not concurrent with a conflicting RMW, it is performed immediately with no waiting; furthermore, even with a concurrent conflicting RMW, a read experiences very little delay in the worst-case. For example, the algorithm's parameters can be set to ensure that every read takes $\epsilon$ time in the worst-case. To the best of our knowledge this is the first algorithm to achieve this bound in the partially synchronous systems that we assume here. Our parametrized algorithm generalizes the (non-parameterized) lease-based algorithm of Chandra et al. [6] where the worst-case time for reads is $3\delta$, where $\delta$ is the maximum message delay.
The algorithm's parameters can be used to trade-off the worst-case times for read and RMW operations. They can also be used to take advantage of the fact that in many message-passing systems the delay of most messages is order of magnitudes smaller than the maximum message delay~$\delta$: for example, the parameters can be set so that, in "nice" periods where message delays are $\delta^* \ll \delta$, reads take at most $\epsilon$ time while RMWs take at most $3 \delta^*$ time.
References & Citations
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.