Models and metrics for software management and engineering

This paper attempts to characterize and present a state of the art view of several quantitative models and metrics of the software life cycle. These models and metrics can be used to aid in managing and engineering software projects. They deal with various aspects of the software process and product, including resources allocation and estimation, changes and errors, size, complexity and reliability. Some indication is given of the extent to which the various models have been used and the success they have achieved

Performance Analysis of Live-Virtual-Constructive and Distributed Virtual Simulations: Defining Requirements in Terms of Temporal Consistency

This research extends the knowledge of live-virtual-constructive (LVC) and distributed virtual simulations (DVS) through a detailed analysis and characterization of their underlying computing architecture. LVCs are characterized as a set of asynchronous simulation applications each serving as both producers and consumers of shared state data. In terms of data aging characteristics, LVCs are found to be first-order linear systems. System performance is quantified via two opposing factors; the consistency of the distributed state space, and the response time or interaction quality of the autonomous simulation applications. A framework is developed that defines temporal data consistency requirements such that the objectives of the simulation are satisfied. Additionally, to develop simulations that reliably execute in real-time and accurately model hierarchical systems, two real-time design patterns are developed: a tailored version of the model-view-controller architecture pattern along with a companion Component pattern. Together they provide a basis for hierarchical simulation models, graphical displays, and network I/O in a real-time environment. For both LVCs and DVSs the relationship between consistency and interactivity is established by mapping threads created by a simulation application to factors that control both interactivity and shared state consistency throughout a distributed environment

Curracurrong: a stream processing system for distributed environments

Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay

Curracurrong: a stream processing system for distributed environments

Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process them to generate a new sequences of events. Such applications need an adequate programming model that can process large amount of data with minimal latency; for this purpose, stream programming, among other paradigms, is ideal. However, stream programming needs to be adapted to meet the challenges inherent in running it in distributed environments. These challenges include the need for modern domain specific language (DSL), the placement of computations in the network to minimise energy costs, and timeliness in real-time applications. To overcome these challenges we developed a stream programming model that achieves easy-to-use programming interface, energy-efficient actor placement, and timeliness. This thesis presents Curracurrong, a stream data processing system for distributed environments. In Curracurrong, a query is represented as a stream graph of stream operators and communication channels. Curracurrong provides an extensible stream operator library and adapts to a wide range of applications. It uses an energy-efficient placement algorithm that optimises communication and computation. We extend the placement problem to support dynamically changing networks, and develop a dynamic program with polynomially bounded runtime to solve the placement problem. In many stream-based applications, real-time data processing is essential. We propose an approach that measures time delays in stream query processing; this model measures the total computational time from input to output of a query, i.e., end-to-end delay

Analysing temporal performance profiles of UAV operators using time series clustering

The continuing growth in the use of Unmanned Aerial Vehicles (UAVs) is causing an important social step forward in the performance of many sensitive tasks, reducing both human and economical risks. The work of UAV operators is a key aspect to guarantee the success of this kind of tasks, and thus UAV operations are studied in many research fields, ranging from human factors to data analysis and machine learning. The present work aims to describe the behaviour of operators over time using a profile-based model where the evolution of the operator performance during a mission is the main unit of measure. In order to compare how different operators act throughout a mission, we describe a methodology based of multivariate-time series clustering to define and analyse a set of representative temporal performance profiles. The proposed methodology is applied in a multi-UAV simulation environment with inexperienced operators, obtaining a fair description of the temporal behavioural patterns followed during the course of the simulation

Scalable processing of aggregate functions for data streams in resource-constrained environments

The fast evolution of data analytics platforms has resulted in an increasing demand for real-time data stream processing. From Internet of Things applications to the monitoring of telemetry generated in large datacenters, a common demand for currently emerging scenarios is the need to process vast amounts of data with low latencies, generally performing the analysis process as close to the data source as possible. Devices and sensors generate streams of data across a diversity of locations and protocols. That data usually reaches a central platform that is used to store and process the streams. Processing can be done in real time, with transformations and enrichment happening on-the-fly, but it can also happen after data is stored and organized in repositories. In the former case, stream processing technologies are required to operate on the data; in the latter batch analytics and queries are of common use. Stream processing platforms are required to be malleable and absorb spikes generated by fluctuations of data generation rates. Data is usually produced as time series that have to be aggregated using multiple operators, being sliding windows one of the most common abstractions used to process data in real-time. To satisfy the above-mentioned demands, efficient stream processing techniques that aggregate data with minimal computational cost need to be developed. However, data analytics might require to aggregate extensive windows of data. Approximate computing has been a central paradigm for decades in data analytics in order to improve the performance and reduce the needed resources, such as memory, computation time, bandwidth or energy. In exchange for these improvements, the aggregated results suffer from a level of inaccuracy that in some cases can be predicted and constrained. This doctoral thesis aims to demonstrate that it is possible to have constant-time and memory efficient aggregation functions with approximate computing mechanisms for constrained environments. In order to achieve this goal, the work has been structured in three research challenges. First we introduce a runtime to dynamically construct data stream processing topologies based on user-supplied code. These dynamic topologies are built on-the-fly using a data subscription model de¿ned by the applications that consume data. The subscription-based programing model enables multiple users to deploy their own data-processing services. On top of this runtime, we present the Amortized Monoid Tree Aggregator general sliding window aggregation framework, which seamlessly combines the following features: amortized O(1) time complexity and a worst-case of O(log n) between insertions; it provides both a window aggregation mechanism and a window slide policy that are user programmable; the enforcement of the window sliding policy exhibits amortized O(1) computational cost for single evictions and supports bulk evictions with cost O(log n); and it requires a local memory space of O(log n). The framework can compute aggregations over multiple data dimensions, and has been designed to support decoupling computation and data storage through the use of distributed Key-Value Stores to keep window elements and partial aggregations. Specially motivated by edge computing scenarios, we contribute Approximate and Amortized Monoid Tree Aggregator (A2MTA). It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. A2MTA uses statistical analysis of the stream data in order to perform inaccurate aggregations, providing a critical reduction of needed resources for massive stream data aggregation, and an improvement of performance.La ràpida evolució de les plataformes d'anàlisi de dades ha resultat en un increment de la demanda de processament de fluxos continus de dades en temps real. Des de la internet de les coses fins al monitoratge de telemetria generada en grans servidors, una demanda recurrent per escenaris emergents es la necessitat de processar grans quantitats de dades amb latències molt baixes, generalment fent el processat de les dades tant a prop dels origines com sigui possible. Les dades son generades com a fluxos continus per dispositius que utilitzen una varietat de localitzacions i protocols. Aquests processat de les dades s pot fer en temps real amb les transformacions efectuant-se al vol, i en aquest cas la utilització de plataformes de processat d'streams és necessària. Les plataformes de processat d'streams cal que absorbeixin pics de freqüència de dades. Les dades es generen com a series temporals que s'agreguen fent servir multiples operadors, on les finestres són l'abstracció més habitual. Per a satisfer les baixes latències i maleabilitat requerides, els operadors necesiten tenir un cost computacional mínim, inclús amb extenses finestres de dades per a agregar. La computació aproximada ha sigut durant decades un paradigma rellevant per l'anàlisi de dades on cal millorar el rendiment de diferents algorismes i reduir-ne el temps de computació, la memòria requerida, l'ample de banda o el consum energètic. A canvi d'aquestes millores, els resultats poden patir d'una falta d'exactitud que pot ser estimada i controlada. Aquesta tesi doctoral vol demostrar que es posible tenir funcions d'agregació pel processat d'streams que tinc un cost de temps constant, sigui eficient en termes de memoria i faci ús de computació aproximada. Per aconseguir aquests objectius, aquesta tesi està dividida en tres reptes. Primer presentem un entorn per a la construcció dinàmica de topologies de computació d'streams de dades utilitzant codi d'usuari. Aquestes topologies es construeixen fent servir un model de subscripció a streams, en el que les aplicación consumidores de dades amplien les topologies mentre s'estan executant. Aquest entorn permet multiples entitats ampliant una mateixa topologia. A sobre d'aquest entorn, presentem un framework de propòsit general per a l'agregació de finestres de dades anomenat AMTA (Amortized Monoid Tree Aggregator). Aquest framework combina: temps amortitzat constant per a totes les operacions, amb un cas pitjor logarítmic; programable tant en termes d'agregació com en termes d'expulsió d'elements de la finestra. L'expulsió massiva d'elements de la finestra es considera una operació atòmica, amb un cost amortitzat constant; i requereix espai en memoria local per a O(log n) elements de la finestra. Aquest framework pot computar agregacions sobre multiples dimensions de dades, i ha estat dissenyat per desacoplar la computació de les dades del seu desat, podent tenir els continguts de la finestra distribuits en diferents màquines. Motivats per la computació en l'edge (edge computing), hem contribuit A2MTA (Approximate and Amortized Monoid Tree Aggregator). Des de el nostre coneixement, es el primer framework de propòsit general per a la computació de finestres que combina un cost constant per a totes les seves operacions amb tècniques de computació aproximada amb control de l'error. A2MTA fa us d'anàlisis estadístics per a poder fer agregacions amb error limitat, reduint críticament els recursos necessaris per a la computació de grans quantitats de dades