1,608 research outputs found
Robustness Against Read Committed for Transaction Templates with Functional Constraints
The popular isolation level Multiversion Read Committed (RC) trades some of the strong guarantees of serializability for increased transaction throughput. Sometimes, transaction workloads can be safely executed under RC obtaining serializability at the lower cost of RC. Such workloads are said to be robust against RC. Previous work has yielded a tractable procedure for deciding robustness against RC for workloads generated by transaction programs modeled as transaction templates. An important insight of that work is that, by more accurately modeling transaction programs, we are able to recognize larger sets of workloads as robust. In this work, we increase the modeling power of transaction templates by extending them with functional constraints, which are useful for capturing data dependencies like foreign keys. We show that the incorporation of functional constraints can identify more workloads as robust that otherwise would not be. Even though we establish that the robustness problem becomes undecidable in its most general form, we show that various restrictions on functional constraints lead to decidable and even tractable fragments that can be used to model and test for robustness against RC for realistic scenarios
GeoGauss: Strongly Consistent and Light-Coordinated OLTP for Geo-Replicated SQL Database
Multinational enterprises conduct global business that has a demand for
geo-distributed transactional databases. Existing state-of-the-art databases
adopt a sharded master-follower replication architecture. However, the
single-master serving mode incurs massive cross-region writes from clients, and
the sharded architecture requires multiple round-trip acknowledgments (e.g.,
2PC) to ensure atomicity for cross-shard transactions. These limitations drive
us to seek yet another design choice. In this paper, we propose a strongly
consistent OLTP database GeoGauss with full replica multi-master architecture.
To efficiently merge the updates from different master nodes, we propose a
multi-master OCC that unifies data replication and concurrent transaction
processing. By leveraging an epoch-based delta state merge rule and the
optimistic asynchronous execution, GeoGauss ensures strong consistency with
light-coordinated protocol and allows more concurrency with weak isolation,
which are sufficient to meet our needs. Our geo-distributed experimental
results show that GeoGauss achieves 7.06X higher throughput and 17.41X lower
latency than the state-of-the-art geo-distributed database CockroachDB on the
TPC-C benchmark
Ensuring Serializable Executions with Snapshot Isolation DBMS
Snapshot Isolation (SI) is a multiversion concurrency control that has been implemented by open source and commercial database systems such as PostgreSQL and Oracle. The main feature of SI is that a read operation does not block a write operation and vice versa, which allows higher degree of concurrency than traditional two-phase locking. SI prevents many anomalies that appear in other isolation levels, but it still can result in non-serializable execution, in which database integrity constraints can be violated. Several techniques have been proposed to ensure serializable execution with engines running SI; these techniques are based on modifying the applications by introducing conflicting SQL statements. However, with each of these techniques the DBA has to make a difficult choice among possible transactions to modify. This thesis helps the DBA’s to choose between these different techniques and choices by understanding how the choices affect system performance. It also proposes a novel technique called ’External Lock Manager’ (ELM) which introduces conflicts in a separate lock-manager object so that every execution will be serializable. We build a prototype system for ELM and we run experiments to demonstrate the robustness of the new technique compare to the previous techniques. Experiments show that modifying the application code for some transactions has a high impact on performance for some choices, which makes it very hard for DBA’s to choose wisely. However, ELM has peak performance which is similar to SI, no matter which transactions are chosen for modification. Thus we say that ELM is a robust technique for ensure serializable execution
Ensuring Serializable Executions with Snapshot Isolation DBMS
Snapshot Isolation (SI) is a multiversion concurrency control that has been implemented by open source and commercial database systems such as PostgreSQL and Oracle. The main feature of SI is that a read operation does not block a write operation and vice versa, which allows higher degree of concurrency than traditional two-phase locking. SI prevents many anomalies that appear in other isolation levels, but it still can result in non-serializable execution, in which database integrity constraints can be violated. Several techniques have been proposed to ensure serializable execution with engines running SI; these techniques are based on modifying the applications by introducing conflicting SQL statements. However, with each of these techniques the DBA has to make a difficult choice among possible transactions to modify. This thesis helps the DBA’s to choose between these different techniques and choices by understanding how the choices affect system performance. It also proposes a novel technique called ’External Lock Manager’ (ELM) which introduces conflicts in a separate lock-manager object so that every execution will be serializable. We build a prototype system for ELM and we run experiments to demonstrate the robustness of the new technique compare to the previous techniques. Experiments show that modifying the application code for some transactions has a high impact on performance for some choices, which makes it very hard for DBA’s to choose wisely. However, ELM has peak performance which is similar to SI, no matter which transactions are chosen for modification. Thus we say that ELM is a robust technique for ensure serializable execution
Dataplane Specialization for High-performance OpenFlow Software Switching
OpenFlow is an amazingly expressive dataplane program-
ming language, but this expressiveness comes at a severe
performance price as switches must do excessive packet clas-
sification in the fast path. The prevalent OpenFlow software
switch architecture is therefore built on flow caching, but
this imposes intricate limitations on the workloads that can
be supported efficiently and may even open the door to mali-
cious cache overflow attacks. In this paper we argue that in-
stead of enforcing the same universal flow cache semantics
to all OpenFlow applications and optimize for the common
case, a switch should rather automatically specialize its dat-
aplane piecemeal with respect to the configured workload.
We introduce ES WITCH , a novel switch architecture that
uses on-the-fly template-based code generation to compile
any OpenFlow pipeline into efficient machine code, which
can then be readily used as fast path. We present a proof-
of-concept prototype and we demonstrate on illustrative use
cases that ES WITCH yields a simpler architecture, superior
packet processing speed, improved latency and CPU scala-
bility, and predictable performance. Our prototype can eas-
ily scale beyond 100 Gbps on a single Intel blade even with
complex OpenFlow pipelines
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters
requires complex algorithms. Current systems, however, use simple generalized
heuristics and ignore workload characteristics, since developing and tuning a
scheduling policy for each workload is infeasible. In this paper, we show that
modern machine learning techniques can generate highly-efficient policies
automatically. Decima uses reinforcement learning (RL) and neural networks to
learn workload-specific scheduling algorithms without any human instruction
beyond a high-level objective such as minimizing average job completion time.
Off-the-shelf RL techniques, however, cannot handle the complexity and scale of
the scheduling problem. To build Decima, we had to develop new representations
for jobs' dependency graphs, design scalable RL models, and invent RL training
methods for dealing with continuous stochastic job arrivals. Our prototype
integration with Spark on a 25-node cluster shows that Decima improves the
average job completion time over hand-tuned scheduling heuristics by at least
21%, achieving up to 2x improvement during periods of high cluster load
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