Sebastiano Peluso

When Scalability Meets Consistency: Genuine Multiversion Update-Serializable Partial Data Replication

In this article we introduce GMU, a genuine partial replication protocol for transactional systems, which exploits an innovative, highly scalable, distributed multiversioning scheme. Unlike existing multiversion-based solutions, GMU does not rely on a global logical clock, which represents a contention point and can limit system scalability. Also, GMU never aborts read-only transactions and spares them from distributed validation schemes. This makes GMU particularly efficient in presence of read-intensive workloads, as typical of a wide range of real-world applications. GMU guarantees the Extended Update Serializability (EUS) isolation level. This consistency criterion is particularly attractive as it is sufficiently strong to ensure correctness even for very demanding applications (such as TPC-C), but is also weak enough to allow efficient and scalable implementations, such as GMU. Further, unlike several relaxed consistency models proposed in literature, EUS has simple and intuitive semantics, thus being an attractive, scalable consistency model for ordinary programmers. We integrated the GMU protocol in a popular open source in-memory transactional data grid, namely Infinispan. On the basis of a large scale experimental study performed on heterogeneous experimental platforms and using industry standard benchmarks (namely TPC-C and YCSB), we show that GMU achieves linear scalability and that it introduces negligible overheads (less than 10%), with respect to solutions ensuring non-serializable semantics, in a wide range of workloads.

SCORe: a scalable one-copy serializable partial replication protocol

In this article we present SCORe, a scalable one-copy serializable partial replication protocol. Differently from any other literature proposal, SCORe jointly guarantees the following properties: (i) it is genuine, thus ensuring that only the replicas that maintain data accessed by a transaction are involved in its processing, and (ii) it guarantees that read operations always access consistent snapshots, thanks to a one-copy serializable multiversion scheme, which never aborts read-only transactions and spares them from any (distributed) validation phase. This makes SCORe particularly efficient in presence of read-intensive workloads, as typical of a wide range of real-world applications. We have integrated SCORe into a popular open source distributed data grid and performed a large scale experimental study with well-known benchmarks using both private and public cloud infrastructures. The experimental results demonstrate that SCORe provides stronger consistency guarantees (namely One-Copy Serializability) than existing multiversion partial replication protocols at no additional overhead.

Transactional auto scaler: elastic scaling of in-memory transactional data grids

In this paper we introduce TAS (Transactional Auto Scaler), a system for automating elastic-scaling of in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from on-line self-optimization of in-production applications to automatic generation of QoS/cost driven elastic scaling policies, and support for what-if analysis on the scalability of transactional applications. The key innovation at the core of TAS is a novel performance forecasting methodology that relies on the joint usage of analytical modeling and machine-learning. By exploiting these two, classically competing, methodologies in a synergic fashion, TAS achieves the best of the two worlds, namely high extrapolation power and good accuracy even when faced with complex workloads deployed over public cloud infrastructures. We demonstrate the accuracy and feasibility of TAS via an extensive experimental study based on a fully fledged prototype implementation, integrated with a popular open-source transactional in-memory data store (Red Hats Infinispan), and industry-standard benchmarks generating a breadth of heterogeneous workloads.

Exploiting Locality in Lease-Based Replicated Transactional Memory via Task Migration

We present Lilac-TM, the first locality-aware Distributed Software Transactional Memory (DSTM) implementation. Lilac-TM is a fully decentralized lease-based replicated DSTM. It employs a novel self-optimizing lease circulation scheme based on the idea of dynamically determining whether to migrate transactions to the nodes that own the leases required for their validation, or to demand the acquisition of these leases by the node that originated the transaction. Our experimental evaluation establishes that Lilac-TM provides significant performance gains for distributed workloads exhibiting data locality, while typically incurring little or no overhead for non-data local workloads.

Automated Workload Characterization in Cloud-based Transactional Data Grids

Cloud computing represents a cost-effective paradigm to deploy a wide class of large-scale distributed applications, for which the pay-per-use model combined with automatic resource provisioning promise to reduce the cost of dependability and scalability. However, a key challenge to be addressed to materialize the advantages promised by Cloud computing is the design of effective auto-scaling and self-tuning mechanisms capable of ensuring pre-determined QoS levels at minimum cost in face of changing workload conditions. This is one of the keys goals that are being pursued by the Cloud-TM project, a recent EU project that is developing a novel, self-optimizing transactional data platform for the cloud. In this paper we present the key design choices underlying the development of Cloud-TMs Workload Analyzer (WA), a crucial component of the Cloud-TM platform that is change of three key functionalities: aggregating, filtering and correlating the streams of statistical data gathered from the various nodes of the Cloud-TM platform, building detailed workload profiles of applications deployed on the Cloud-TM platform, characterizing their present and future demands in terms of both logical (i.e. data) and physical (e.g. hardware-related) resources, triggering alerts in presence of violations (or risks of future violations) of pre-determined SLAs.

Transactional Auto Scaler: Elastic Scaling of Replicated In-Memory Transactional Data Grids

In this article, we introduce TAS (Transactional Auto Scaler), a system for automating the elastic scaling of replicated in-memory transactional data grids, such as NoSQL data stores or Distributed Transactional Memories. Applications of TAS range from online self-optimization of in-production applications to the automatic generation of QoS/cost-driven elastic scaling policies, as well as to support for what-if analysis on the scalability of transactional applications. In this article, we present the key innovation at the core of TAS, namely, a novel performance forecasting methodology that relies on the joint usage of analytical modeling and machine learning. By exploiting these two classically competing approaches in a synergic fashion, TAS achieves the best of the two worlds, namely, high extrapolation power and good accuracy, even when faced with complex workloads deployed over public cloud infrastructures. We demonstrate the accuracy and feasibility of TAS’s performance forecasting methodology via an extensive experimental study based on a fully fledged prototype implementation integrated with a popular open-source in-memory transactional data grid (Red Hat’s Infinispan) and industry-standard benchmarks generating a breadth of heterogeneous workloads.

Be General and Don’t Give Up Consistency in Geo-Replicated Transactional Systems

We present Alvin, a system for managing concurrent transactions running on a set of geographically distributed sites. Alvin supports general-purpose transactions, and guarantees strong consistency criteria. Through a novel partial order broadcast protocol, Alvin maximizes the parallelism of ordering and local transaction processing. Alvin processes read-only transactions either locally or globally, according to the selected consistency criterion, and orders only conflicting transactions across all sites. We built Alvin in the Go language and conducted an evaluation study relying on the Amazon EC2 infrastructure and Paxos- and EPaxos-based state machine replication protocols as competitors. Our experimental results reveal that Alvin provides significant speed up for read-dominated TPC-C workloads and on 7 datacenters by as much as 4.8x when compared to EPaxos, and up to 26% in write-intensive workloads.

SPECULA: Speculative Replication of Software Transactional Memory

This paper introduces SPECULA, a novel replication protocol for Software Transactional Memory (STM) systems that seeks maximum overlap between transaction execution and replica synchronization phases via speculative processing techniques. By removing the replica synchronization phase from the critical path of execution of transactions, SPECULA allows threads to speculatively pipeline the execution of both transactional and/or non-transactional code. The core of SPECULA is a multi-version concurrency control algorithm that supports speculative transaction processing while ensuring the strong consistency criteria that are desirable in non-sand-boxed environments like STMs. Via an experimental study, based on a fully-fledged prototype and on both synthetic and standard STM benchmarks, we demonstrate that SPECULA can achieve speedups of up to one order of magnitude with respect to state-of-the-art non-speculative replication techniques.

Making Fast Consensus Generally Faster

New multi-leader consensus protocols leverage the Generalized Consensus specification to enable low latency, even load balancing, and high parallelism. However, these protocols introduce inherent costs with significant performance impact: they need quorums bigger than the minimum required to solve consensus and need to track dependency relations among proposals. In this paper we present M2PAXOS, an implementation of Generalized Consensus that provides fast decisions (i.e., delivery of a command in two communication delays) by leveraging quorums composed of a majority of nodes and by exploiting workload locality. M2PAXOS does not establish command dependencies based on conflicts, instead mapping nodes to accessed objects and enforcing that commands accessing the same objects be ordered by the same node. Our experimental evaluation confirms the effectiveness of M2PAXOS, gaining up to 7X over state-of-the-art Consensus and Generalized Consensus algorithms under partitioned data accesses and up to 5.5× using the TPC-C workload.

Supports for transparent object-migration in PDES systems

It is well known that Parallel Discrete Event Simulation systems may suffer, in terms of delivered performance, from imbalance of the computational load. In case of conservative synchronization we may experience CPU under-utilization and/or excessive communication overhead. On the other hand, for the optimistic paradigm we may even have rollback thrashing effects, with a consequent reduction of the percentage of productive (ie not rolled back) work carried out. This paper presents the design of a global memory management architecture supporting application-transparent migration of simulation objects whose state is scattered across dynamically allocated memory chunks. Our approach is based on a non-intrusive background protocol that provides each instance of the simulation kernel with information on the current mapping of the virtual address space of all the other instances. Dynamic memory requests by the application layer are then locally mapped onto virtual-address ranges that maximize the likelihood of being portable onto the address space of a remote kernel instance. In this way, independently of the load-balancing trigger (or policy), we maximize the likelihood that a desirable migration across a specific couple of kernels can actually take place due to compliance of the corresponding source/destination address spaces. We have integrated the global memory manager within the ROme OpTimistic Simulator (ROOT-Sim), namely a run-time environment based on the optimistic synchronization paradigm which automatically and transparently parallelizes the execution of event-handler-based simulation programs conforming to ANSI-C. Further, we provide a contribution in the direction of widening load-balancing schemes for optimistic simulation systems by defining migration triggers and selection policies for the objects to be migrated on the basis of memory usage patterns. An experimental assessment of the architecture and of memory-oriented load balancing is also provided.