2,508 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
Dynamic Physiological Partitioning on a Shared-nothing Database Cluster
Traditional DBMS servers are usually over-provisioned for most of their daily
workloads and, because they do not show good-enough energy proportionality,
waste a lot of energy while underutilized. A cluster of small (wimpy) servers,
where its size can be dynamically adjusted to the current workload, offers
better energy characteristics for these workloads. Yet, data migration,
necessary to balance utilization among the nodes, is a non-trivial and
time-consuming task that may consume the energy saved. For this reason, a
sophisticated and easy to adjust partitioning scheme fostering dynamic
reorganization is needed. In this paper, we adapt a technique originally
created for SMP systems, called physiological partitioning, to distribute data
among nodes, that allows to easily repartition data without interrupting
transactions. We dynamically partition DB tables based on the nodes'
utilization and given energy constraints and compare our approach with physical
partitioning and logical partitioning methods. To quantify possible energy
saving and its conceivable drawback on query runtimes, we evaluate our
implementation on an experimental cluster and compare the results w.r.t.
performance and energy consumption. Depending on the workload, we can
substantially save energy without sacrificing too much performance
Letter from the Special Issue Editor
Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies
Speedy Transactions in Multicore In-Memory Databases
Silo is a new in-memory database that achieves excellent performance and scalability on modern multicore machines. Silo was designed from the ground up to use system memory and caches efficiently. For instance, it avoids all centralized contention points, including that of centralized transaction ID assignment. Silo's key contribution is a commit protocol based on optimistic concurrency control that provides serializability while avoiding all shared-memory writes for records that were only read. Though this might seem to complicate the enforcement of a serial order, correct logging and recovery is provided by linking periodically-updated epochs with the commit protocol. Silo provides the same guarantees as any serializable database without unnecessary scalability bottlenecks or much additional latency. Silo achieves almost 700,000 transactions per second on a standard TPC-C workload mix on a 32-core machine, as well as near-linear scalability. Considered per core, this is several times higher than previously reported results.Engineering and Applied Science
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