7,073 research outputs found
Improving the Performance and Endurance of Persistent Memory with Loose-Ordering Consistency
Persistent memory provides high-performance data persistence at main memory.
Memory writes need to be performed in strict order to satisfy storage
consistency requirements and enable correct recovery from system crashes.
Unfortunately, adhering to such a strict order significantly degrades system
performance and persistent memory endurance. This paper introduces a new
mechanism, Loose-Ordering Consistency (LOC), that satisfies the ordering
requirements at significantly lower performance and endurance loss. LOC
consists of two key techniques. First, Eager Commit eliminates the need to
perform a persistent commit record write within a transaction. We do so by
ensuring that we can determine the status of all committed transactions during
recovery by storing necessary metadata information statically with blocks of
data written to memory. Second, Speculative Persistence relaxes the write
ordering between transactions by allowing writes to be speculatively written to
persistent memory. A speculative write is made visible to software only after
its associated transaction commits. To enable this, our mechanism supports the
tracking of committed transaction ID and multi-versioning in the CPU cache. Our
evaluations show that LOC reduces the average performance overhead of memory
persistence from 66.9% to 34.9% and the memory write traffic overhead from
17.1% to 3.4% on a variety of workloads.Comment: This paper has been accepted by IEEE Transactions on Parallel and
Distributed System
Blazes: Coordination Analysis for Distributed Programs
Distributed consistency is perhaps the most discussed topic in distributed
systems today. Coordination protocols can ensure consistency, but in practice
they cause undesirable performance unless used judiciously. Scalable
distributed architectures avoid coordination whenever possible, but
under-coordinated systems can exhibit behavioral anomalies under fault, which
are often extremely difficult to debug. This raises significant challenges for
distributed system architects and developers. In this paper we present Blazes,
a cross-platform program analysis framework that (a) identifies program
locations that require coordination to ensure consistent executions, and (b)
automatically synthesizes application-specific coordination code that can
significantly outperform general-purpose techniques. We present two case
studies, one using annotated programs in the Twitter Storm system, and another
using the Bloom declarative language.Comment: Updated to include additional materials from the original technical
report: derivation rules, output stream label
Theory and Practice of Transactional Method Caching
Nowadays, tiered architectures are widely accepted for constructing large
scale information systems. In this context application servers often form the
bottleneck for a system's efficiency. An application server exposes an object
oriented interface consisting of set of methods which are accessed by
potentially remote clients. The idea of method caching is to store results of
read-only method invocations with respect to the application server's interface
on the client side. If the client invokes the same method with the same
arguments again, the corresponding result can be taken from the cache without
contacting the server. It has been shown that this approach can considerably
improve a real world system's efficiency.
This paper extends the concept of method caching by addressing the case where
clients wrap related method invocations in ACID transactions. Demarcating
sequences of method calls in this way is supported by many important
application server standards. In this context the paper presents an
architecture, a theory and an efficient protocol for maintaining full
transactional consistency and in particular serializability when using a method
cache on the client side. In order to create a protocol for scheduling cached
method results, the paper extends a classical transaction formalism. Based on
this extension, a recovery protocol and an optimistic serializability protocol
are derived. The latter one differs from traditional transactional cache
protocols in many essential ways. An efficiency experiment validates the
approach: Using the cache a system's performance and scalability are
considerably improved
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