Constant-time sliding window framework with reduced memory footprint and efficient bulk evictions

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 data centers, 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. 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. In this paper we present the 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.This project is partially supported by the European Research Council (ERC), Spain under the European Unions Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015- 65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).Peer ReviewedPostprint (published version

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

Out-of-Order Sliding-Window Aggregation with Efficient Bulk Evictions and Insertions (Extended Version)

Sliding-window aggregation is a foundational stream processing primitive that efficiently summarizes recent data. The state-of-the-art algorithms for sliding-window aggregation are highly efficient when stream data items are evicted or inserted one at a time, even when some of the insertions occur out-of-order. However, real-world streams are often not only out-of-order but also burtsy, causing data items to be evicted or inserted in larger bulks. This paper introduces a new algorithm for sliding-window aggregation with bulk eviction and bulk insertion. For the special case of single insert and evict, our algorithm matches the theoretical complexity of the best previous out-of-order algorithms. For the case of bulk evict, our algorithm improves upon the theoretical complexity of the best previous algorithm for that case and also outperforms it in practice. For the case of bulk insert, there are no prior algorithms, and our algorithm improves upon the naive approach of emulating bulk insert with a loop over single inserts, both in theory and in practice. Overall, this paper makes high-performance algorithms for sliding window aggregation more broadly applicable by efficiently handling the ubiquitous cases of out-of-order data and bursts.Comment: Extended version for VLDB 2023 pape

Reconfigurable-Hardware Accelerated Stream Aggregation

High throughput and low latency stream aggregation is essential for many applications that analyze massive volumes of data in real-time. Incoming data need to be stored in a single sliding-window before processing, in cases where incremental aggregations are wasteful or not possible at all. However, storing all incoming values in a single-window puts tremendous pressure on the memory bandwidth and capacity. GPU and CPU memory management is inefficient for this task as it introduces unnecessary data movement that wastes bandwidth. FPGAs can make more efficient use of their memory but existing approaches employ only on-chip memory and therefore, can only support small problem sizes (i.e. small sliding windows and number of keys) due to the limited capacity. This thesis addresses the above limitations of stream processing systems by proposing techniques for accelerating single sliding-window stream aggregation using FPGAs to achieve line-rate processing throughput and ultra low latency. It does so first by building accelerators using FPGAs and second, by alleviating the memory pressure posed by single-window stream aggregation. The initial part of this thesis presents the accelerators for both windowing policies, namely, tuple- and time-\ua0based, using Maxeler\u27s DataFlow Engines\ua0(DFEs) which have a direct feed of incoming data from the network as well as direct access to off-chip DRAM. Compared to state-of-the-art stream processing software system, the DFEs offer 1-2 orders of magnitude higher processing throughput and 4 orders of magnitude lower latency. The later part of this thesis focuses on alleviating the memory pressure due to the various steps in single-window stream aggregation. Updating the window with new incoming values and reading it to feed the aggregation functions are the two primary steps in stream aggregation. The high on-chip SRAM bandwidth enables line-rate processing, but only for small problem sizes due to the limited capacity. The larger off-chip DRAM size supports larger problems, but falls short on performance due to lower bandwidth. In order to bridge this gap, this thesis introduces a specialized memory hierarchy for stream aggregation. It employs Multi-Level Queues (MLQs) spanning across multiple memory levels with different characteristics to offer both high bandwidth and capacity. In doing so, larger stream aggregation problems can be supported at line-rate performance, outperforming existing competing solutions. Compared to designs with only on-chip memory, our approach supports 4 orders of magnitude larger problems. Compared to designs that use only DRAM, our design achieves up to 8x higher throughput. Finally, this thesis aims to alleviate the memory pressure due to the window-aggregation step. Although window-updates can be supported efficiently using MLQs, frequent window-aggregations remain a performance bottleneck. This thesis addresses this problem by introducing StreamZip, a dataflow stream aggregation engine that is able to compress the sliding-windows. StreamZip deals with a number of data and control dependency challenges to integrate a compressor in the stream aggregation pipeline and alleviate the memory pressure posed by frequent aggregations. In doing so, StreamZip offers higher throughput as well as larger effective window capacity to support larger problems. StreamZip supports diverse compression algorithms offering both lossless and lossy compression to fixed- as well as floating- point numbers. Compared to designs using MLQs, StreamZip lossless and lossy designs achieve up to 7.5x and 22x higher throughput, while improving the effective memory capacity by up to 5x and 23x, respectively

Constant-time approximate sliding window framework with error control

Stream Processing is a crucial element for the Edge Computing paradigm, in which large amount of devices generate data at the edge of the network. This data needs to be aggregated and processed on-the-move across different layers before reaching the Cloud. Therefore, defining Stream Processing services that adapt to different levels of resource availability is of paramount importance. In this context, Stream Processing frameworks need to combine efficient algorithms with low computational complexity to manage sliding windows, with the ability to adjust resource demands for different deployment scenarios, from very low capacity edge devices to virtually unlimited Cloud platforms. The Approximate Computing paradigm provides improved performance and adaptive resource demands in data analytics, at the price of introducing some level of inaccuracy that can be calculated. In this paper we present the Approximate and Amortized Monoid Tree Aggregator (A 2 MTA). It is, to our knowledge, the first general purpose sliding window programable framework that combines constant-time aggregations with error bounded approximate computing techniques. It is very suitable for adverse stream processing environments, such as resource scarce multi-tenant edge computing. The framework can compute aggregations over multiple data dimensions, setting error bounds on any of them, 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.This project is partially supported by the European Research Council (ERC), Spain under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493).Peer ReviewedPostprint (author's final draft