14,287 research outputs found
Okapi: Causally Consistent Geo-Replication Made Faster, Cheaper and More Available
Okapi is a new causally consistent geo-replicated key- value store. Okapi
leverages two key design choices to achieve high performance. First, it relies
on hybrid logical/physical clocks to achieve low latency even in the presence
of clock skew. Second, Okapi achieves higher resource efficiency and better
availability, at the expense of a slight increase in update visibility latency.
To this end, Okapi implements a new stabilization protocol that uses a
combination of vector and scalar clocks and makes a remote update visible when
its delivery has been acknowledged by every data center. We evaluate Okapi with
different workloads on Amazon AWS, using three geographically distributed
regions and 96 nodes. We compare Okapi with two recent approaches to causal
consistency, Cure and GentleRain. We show that Okapi delivers up to two orders
of magnitude better performance than GentleRain and that Okapi achieves up to
3.5x lower latency and a 60% reduction of the meta-data overhead with respect
to Cure
PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication
Geo-replicated data platforms are at the backbone of several large-scale
online services. Transactional Causal Consistency (TCC) is an attractive
consistency level for building such platforms. TCC avoids many anomalies of
eventual consistency, eschews the synchronization costs of strong consistency,
and supports interactive read-write transactions. Partial replication is
another attractive design choice for building geo-replicated platforms, as it
increases the storage capacity and reduces update propagation costs. This paper
presents PaRiS, the first TCC system that supports partial replication and
implements non-blocking parallel read operations, whose latency is paramount
for the performance of read-intensive applications. PaRiS relies on a novel
protocol to track dependencies, called Universal Stable Time (UST). By means of
a lightweight background gossip process, UST identifies a snapshot of the data
that has been installed by every DC in the system. Hence, transactions can
consistently read from such a snapshot on any server in any replication site
without having to block. Moreover, PaRiS requires only one timestamp to track
dependencies and define transactional snapshots, thereby achieving resource
efficiency and scalability. We evaluate PaRiS on a large-scale AWS deployment
composed of up to 10 replication sites. We show that PaRiS scales well with the
number of DCs and partitions, while being able to handle larger data-sets than
existing solutions that assume full replication. We also demonstrate a
performance gain of non-blocking reads vs. a blocking alternative (up to 1.47x
higher throughput with 5.91x lower latency for read-dominated workloads and up
to 1.46x higher throughput with 20.56x lower latency for write-heavy
workloads)
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