Performance models of concurrency control protocols for transaction processing systems

Transaction processing plays a key role in a lot of IT infrastructures. It is widely used in a variety of contexts, spanning from database management systems to concurrent programming tools. Transaction processing systems leverage on concurrency control protocols, which allow them to concurrently process transactions preserving essential properties, as isolation and atomicity. Performance is a critical aspect of transaction processing systems, and it is unavoidably affected by the concurrency control. For this reason, methods and techniques to assess and predict the performance of concurrency control protocols are of interest for many IT players, including application designers, developers and system administrators. The analysis and the proper understanding of the impact on the system performance of these protocols require quantitative approaches. Analytical modeling is a practical approach for building cost-effective computer system performance models, enabling us to quantitatively describe the complex dynamics characterizing these systems. In this dissertation we present analytical performance models of concurrency control protocols. We deal with both traditional transaction processing systems, such as database management systems, and emerging ones, as transactional memories. The analysis focuses on widely used protocols, providing detailed performance models and validation studies. In addition, we propose new modeling approaches, which also broaden the scope of our study towards a more realistic, application-oriented, performance analysis

Performance Optimization Strategies for Transactional Memory Applications

This thesis presents tools for Transactional Memory (TM) applications that cover multiple TM systems (Software, Hardware, and hybrid TM) and use information of all different layers of the TM software stack. Therefore, this thesis addresses a number of challenges to extract static information, information about the run time behavior, and expert-level knowledge to develop these new methods and strategies for the optimization of TM applications

ESTIMA: Extrapolating ScalabiliTy of In-Memory Applications

This paper presents ESTIMA, an easy-to-use tool for extrapolating the scalability of in-memory applications. ESTIMA is designed to perform a simple, yet important task: given the performance of an application on a small machine with a handful of cores, ESTIMA extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying ESTIMA is the use of stalled cycles (e.g. cycles that the processor spends waiting for various events, such as cache misses or waiting on a lock). ESTIMA measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. ESTIMA can be effectively used to predict the scalability of in-memory applications. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation on a large number of in-memory benchmarks shows that ESTIMA has generally low prediction errors

ADDING PERSISTENCE TO MAIN MEMORY PROGRAMMING

Unlocking the true potential of the new persistent memories (PMEMs) requires eliminating traditional persistent I/O abstractions altogether, by introducing persistent semantics directly into main memory programming. Such a programming model elevates failure atomicity to a first-class application property in addition to in-memory data layout, concurrency-control, and fault tolerance, and therefore requires redesign of programming abstractions for both program correctness and maximum performance gains. To address these challenges, this thesis proposes a set of system software designs that integrate persistence with main memory programming, and makes the following contributions. First, this thesis proposes a PMEM-aware I/O runtime, NVStream, that supports fast durable streaming I/O. NVStream uses a memory-based I/O interface that integrates with existing I/O data movement operations of an application to accelerate persistent data writes. NVStream carefully designs its persistent data storage layout and crash-consistent semantics to match both application and PMEM characteristics. Specifically, we leverage the streaming nature of I/O in HPC workflows, to benefit from using a log-structured PMEM storage engine design, that uses relaxed write orderings and append-only failure-atomic semantics to form strongly consistent application checkpoints. Furthermore, we identify that optimizing the I/O software stack exposes the PMEM bandwidth limitations as a bottleneck during parallel HPC I/O writes, and propose a novel data movement design – PHX. PHX uses alternative network data movement paths available in datacenters to ease up the bandwidth pressure on the PMEM memory interconnects, all while maintaining the correctness of the persistent data. Next, the thesis explores the challenges and opportunities of using PMEM for true main memory persistent programming – a single data domain for both runtime and persistent applicationstate. Such a programming model includes maintaining ACID properties during each and every update to applications persistent structures. ACID-qualified persistent programming for multi-threaded applications is hard, as the programmer has to reason about both crash-consistency and synchronization – crash-sync – semantics for programming correctness. The thesis contributes new understanding of the correctness requirements for mixing different crash-consistent and synchronization protocols, characterizes the performance of different crash-sync realizations for different applications and hardware architectures, and draws actionable insights for future designs of PMEM systems. Finally, the application state stored on node-local persistent memory is still vulnerable to catastrophic node failures. The thesis proposes a replicated persistent memory runtime, Blizzard, that supports truly fault tolerant, concurrent and persistent data-structure programming. Blizzard carefully integrates userspace networking with byte addressable PMEM for a fast, persistent memory replication runtime. The design also incorporates a replication-aware crash-sync protocol that supports consistent and concurrent updates on persistent data-structures. Blizzard offers applications the flexibility to use the data structures that best match their functional requirements, while offering better performance, and providing crucial reliability guarantees lacking from existing persistent memory runtimes.Ph.D

Transitions in the Mental Health Field\u27s System of Professions from WWII until the Present: The Case of Dubville

A case study of one mental health field in a medium-sized, steel-belt city over a 50 year period is presented. Through interviews and archive data, the narrative elucidates the transitions that occurred in regards to which professions are dominant, and which services count as mental health services, in different eras and under different science and governmental regimes. The dissertation focuses on four professional categories in particular: psychoanalytic psychiatrists, neurological psychiatrists, research psychologists, and clinical psychologists. It also focuses, primarily, on three institutions: a large psychiatric research hospital, a university psychology department, and a psychoanalytic institute. Through tracing the dynamics of the professional transitions undergone by these professions and institutions, four major conclusions are reached: (1) The transitions in mental health were not just scientific advancements, but changes in the object over which mental health professionals are considered to have expertise. (2) Sidestepping the total person has resulted in a radical transition in the very hierarchy of the mental health system of professions. (3) The problem of the total person continues to complicate efforts in mental health, despite having ostensibly been sidestepped. (4) Totality has become an area of technocratic expertise for those working in private practice psychotherapy. However, totality is no longer exclusively conceptualized through a psychoanalytic lens, while the ground to make a claim on totality is still grounded in clinical experience

Data trust framework using blockchain and smart contracts

Lack of trust is the main barrier preventing more widespread data sharing. The lack of transparent and reliable infrastructure for data sharing prevents many data owners from sharing their data. Data trust is a paradigm that facilitates data sharing by forcing data controllers to be transparent about the process of sharing and reusing data. Blockchain technology has the potential to present the essential properties for creating a practical and secure data trust framework by transforming current auditing practices and automatic enforcement of smart contracts logic without relying on intermediaries to establish trust. Blockchain holds an enormous potential to remove the barriers of traditional centralized applications and propose a distributed and transparent administration by employing the involved parties to maintain consensus on the ledger. Furthermore, smart contracts are a programmable component that provides blockchain with more flexible and powerful capabilities. Recent advances in blockchain platforms toward smart contracts' development have revealed the possibility of implementing blockchain-based applications in various domains, such as health care, supply chain and digital identity. This dissertation investigates the blockchain's potential to present a framework for data trust. It starts with a comprehensive study of smart contracts as the main component of blockchain for developing decentralized data trust. Interrelated, three decentralized applications that address data sharing and access control problems in various fields, including healthcare data sharing, business process, and physical access control system, have been developed and examined. In addition, a general-purpose application based on an attribute-based access control model is proposed that can provide trusted auditability required for data sharing and access control systems and, ultimately, a data trust framework. Besides auditing, the system presents a transparency level that both access requesters (data users) and resource owners (data controllers) can benefit from. The proposed solutions have been validated through a use case of independent digital libraries. It also provides a detailed performance analysis of the system implementation. The performance results have been compared based on different consensus mechanisms and databases, indicating the system's high throughput and low latency. Finally, this dissertation presents an end-to-end data trust framework based on blockchain technology. The proposed framework promotes data trustworthiness by assessing input datasets, effectively managing access control, and presenting data provenance and activity monitoring. A trust assessment model that examines the trustworthiness of input data sets and calculates the trust value is presented. The number of transaction validators is defined adaptively with the trust value. This research provides solutions for both data owners and data users’ by ensuring the trustworthiness and quality of the data at origin and transparent and secure usage of the data at the end. A comprehensive experimental study indicates the presented system effectively handles a large number of transactions with low latency

Techniques for Transparent Parallelization of Discrete Event Simulation Models

Simulation is a powerful technique to represent the evolution of real-world phenomena or systems over time. It has been extensively used in different research fields (from medicine to biology, to economy, and to disaster rescue) to study the behaviour of complex systems during their evolution (symbiotic simulation) or before their actual realization (what-if analysis). A traditional way to achieve high performance simulations is the employment of Parallel Discrete Event Simulation (PDES) techniques, which are based on the partitioning of the simulation model into Logical Processes (LPs) that can execute events in parallel on different CPUs and/or different CPU cores, and rely on synchronization mechanisms to achieve causally consistent execution of simulation events. As it is well recognized, the optimistic synchronization approach, namely the Time Warp protocol, which is based on rollback for recovering possible timestamp-order violations due to the absence of block-until-safe policies for event processing, is likely to favour speedup in general application/ architectural contexts. However, the optimistic PDES paradigm implicitly relies on a programming model that shifts from traditional sequential-style programming, given that there is no notion of global address space (fully accessible while processing events at any LP). Furthermore, there is the underlying assumption that the code associated with event handlers cannot execute unrecoverable operations given their speculative processing nature. Nevertheless, even though no unrecoverable action is ever executed by event handlers, a means to actually undo the action if requested needs to be devised and implemented within the software stack. On the other hand, sequential-style programming is an easy paradigm for the development of simulation code, given that it does not require the programmer to reason about memory partitioning (and therefore message passing) and speculative (concurrent) processing of the application. In this thesis, we present methodological and technical innovations which will show how it is possible, by developing innovative runtime mechanisms, to allow a programmer to implement its simulation model in a fully sequential way, and have the underlying simulation framework to execute it in parallel according to speculative processing techniques. Some of the approaches we provide show applicability in either shared- or distributed-memory systems, while others will be specifically tailored to multi/many-core architectures. We will clearly show, during the development of these supports, what is the effect on performance of these solutions, which will nevertheless be negligible, allowing a fruitful exploitation of the available computing power. In the end, we will highlight which are the clear benefits on the programming model tha