Roberto Palmieri

OSARE: Opportunistic Speculation in Actively REplicated Transactional Systems

In this work we present OSARE, an active replication protocol for transactional systems that combines the usage of Optimistic Atomic Broadcast with a speculative concurrency control mechanism in order to overlap transaction processing and replica synchronization. OSARE biases the speculative serialization of transactions towards an order aligned with the optimistic message delivery order. However, due to the lock-free nature of its concurrency control algorithm, at high concurrency levels, namely when the probability of mismatches between optimistic and final deliveries is higher, OSARE explores additional alternative transaction serialization orders in a lightweight and opportunistic fashion. A simulation study we carried out in the context of Software Transactional Memory systems shows that OSARE achieves robust performance also in scenarios characterized by non-minimal likelihood of reorder between optimistic and final deliveries, providing remarkable speed-up with respect to state of the art speculative replication protocols.

Hyflow2: a high performance distributed transactional memory framework in scala

Distributed Transactional Memory (DTM) is a recent but promising model for programming distributed systems. It aims to present programmers with a simple to use distributed concurrency control abstraction (transactions), while maintaining performance and scalability similar to distributed fine-grained locks. Any complications usually associated with such locks (e.g., distributed deadlocks) are avoided. We propose a new DTM framework for the Java Virtual Machine named Hyflow2. We implement Hyflow2 in Scala and base it on the existing ScalaSTM API soon to be included in the Scala standard library. We thus aim to create a smooth transition from multiprocessor STM programs to DTM.

AGGRO: Boosting STM Replication via Aggressively Optimistic Transaction Processing

Software Transactional Memories (STMs) are emerging as a potentially disruptive programming model. In this paper we are address the issue of how to enhance dependability of STM systems via replication. In particular we present AGGRO, an innovative Optimistic Atomic Broadcast-based (OAB) active replication protocol that aims at maximizing the overlap between communication and processing through a novel AGGRessively Optimistic concurrency control scheme. The key idea underlying AGGRO is to propagate dependencies across uncommitted transactions in a controlled manner, namely according to a serialization order compliant with the optimistic message delivery order provided by the OAB service. Another relevant distinguishing feature of AGGRO is of not requiring a-priori knowledge about read/write sets of transactions, but rather to detect and handle conflicts dynamically, i.e. as soon (and only if) they materialize. Based on a detailed simulation study we show the striking performance gains achievable by AGGRO (up to 6x increase of the maximum sustainable throughput, and 75% response time reduction) compared to literature approaches for active replication of transactional systems.

Evaluating database-oriented replication schemes in Software Transactional Memory systems

Software Transactional Memories (STMs) are emerging as a highly attractive programming model, thanks to their ability to mask concurrency management issues to the overlying applications. In this paper we are interested in dependability of STM systems via replication. In particular we present an extensive simulation study aimed at assessing the efficiency of some recently proposed database-oriented replication schemes, when employed in the context of STM systems. Our results point out the limited efficiency and scalability of these schemes, highlighting the need for redesigning ad-hoc solutions well fitting the requirements of STM environments. Possible directions for the re-design process are also discussed and supported by some early quantitative data.

Archie: a speculative replicated transactional system

We present Archie, a high performance fault-tolerant transactional system. Archie complies with the State Machine Approach, where the transactional state is fully replicated and total ordered transactions are executed on the replicas. Archie avoids the serial execution after transactions get ordered, which is the typical bottleneck of those protocols, by anticipating the work and using speculation to process transactions in parallel, enforcing a predefined order. The key feature of Archie is to avoid any non-trivial operation to perform post total orders notification, in case the sequencer node remains stable (only a single timestamp increment is needed for committing a transaction). This approach significantly shortens the transactions critical path. The contention of speculative execution is always kept limited by activating a fixed number of transactions at a time. A comprehensive evaluation, using three competitors and three well known benchmarks, shows that Archie outperforms competitors in all medium/high contention scenarios.

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.

Optimistic transactional boosting

The last two decades witnessed the success of many efficient designs of concurrent data structures. A large set of them has a common base principle: each operation is split into a <italic>read-only traversal</italic> phase, which scans the data structure without locking or monitoring, and a <italic>read-write commit</italic> phase, which atomically validates the output of the <italic>traversal</italic> phase and applies the needed modifications to the data structure. In this paper we introduce <italic>Optimistic Transactional Boosting</italic> (OTB), an optimistic methodology for extending those designs in order to support the composition of multiple operations into one atomic execution by building a single <italic>traversal</italic> phase and a single <italic>commit</italic> phase for the whole atomic execution. As a result, OTB-based data structures are <italic>optimistic</italic> and <italic>composable </italic>. The former because they defer any locking and/or monitoring to the commit phase of the entire atomic execution; the latter because they allow the execution of multiple operations atomically. Additionally, in this paper we provide a theoretical model for analyzing OTB-based data structures and proving their correctness. In particular, we extended a recent approach that models concurrent data structures by including the two notions of <italic>optimism </italic> and <italic>composition</italic> of operations.

Adaptive Live Migration to Improve Load Balancing in Virtual Machine Environment

Load balancing is one of the main challenges in a Virtual Machine (VM) Environment in order to ensure equal utilization of all the available resources while avoiding overloading a subset of machines. In this paper, we propose an efficient load balancing strategy based on VM live migration. Unlike previous work, our strategy records the history of mappings to inform future placement decisions. We also apply a workload-adaptive live migration algorithm in order to minimize VM downtime and improve the user experience. The evaluation shows that our load balancing technique is faster than previous approaches, thus reducing the decision generating latency by as much as 79%. Furthermore, the results also show that we provide minimal downtime. Compared with competitors, our proposed migration mechanism reduces the downtime by up to 73%.

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.

Enhancing concurrency in distributed transactional memory through commutativity

Distributed software transactional memory is an emerging, alternative concurrency control model for distributed systems promising to alleviate the difficulties of lock-based distributed synchronization. We consider the multi-versioning (MV) model to avoid unnecessary aborts. MV schemes inherently guarantee commits of read-only transactions, but limit the concurrency of write transactions. In this paper we propose CRF (Commutative Requests First), a new scheduler tailored for enhancing concurrency of write transactions. CRF relies on the notion of commutative transactions, namely conflicting transactions that leave the state of the shared data-set consistent even if validated and committed concurrently. CRF is responsible to detect conflicts among commutative and non-commutative write transactions and then schedules them according to the execution state. We assess the goodness of the approach by an extensive evaluation of a fully implementation of CRF. The tests reveal that CRF improves throughput over a state-of-the-art DTM solution.