11,995 research outputs found
Lifeguard: Local Health Awareness for More Accurate Failure Detection
SWIM is a peer-to-peer group membership protocol with attractive scaling and
robustness properties. However, slow message processing can cause SWIM to mark
healthy members as failed (so called false positive failure detection), despite
inclusion of a mechanism to avoid this.
We identify the properties of SWIM that lead to the problem, and propose
Lifeguard, a set of extensions to SWIM which consider that the local failure
detector module may be at fault, via the concept of local health. We evaluate
this approach in a precisely controlled environment and validate it in a
real-world scenario, showing that it drastically reduces the rate of false
positives. The false positive rate and detection time for true failures can be
reduced simultaneously, compared to the baseline levels of SWIM
Packet Transactions: High-level Programming for Line-Rate Switches
Many algorithms for congestion control, scheduling, network measurement,
active queue management, security, and load balancing require custom processing
of packets as they traverse the data plane of a network switch. To run at line
rate, these data-plane algorithms must be in hardware. With today's switch
hardware, algorithms cannot be changed, nor new algorithms installed, after a
switch has been built.
This paper shows how to program data-plane algorithms in a high-level
language and compile those programs into low-level microcode that can run on
emerging programmable line-rate switching chipsets. The key challenge is that
these algorithms create and modify algorithmic state. The key idea to achieve
line-rate programmability for stateful algorithms is the notion of a packet
transaction : a sequential code block that is atomic and isolated from other
such code blocks. We have developed this idea in Domino, a C-like imperative
language to express data-plane algorithms. We show with many examples that
Domino provides a convenient and natural way to express sophisticated
data-plane algorithms, and show that these algorithms can be run at line rate
with modest estimated die-area overhead.Comment: 16 page
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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