18 research outputs found
Event Stream Processing with Multiple Threads
Current runtime verification tools seldom make use of multi-threading to
speed up the evaluation of a property on a large event trace. In this paper, we
present an extension to the BeepBeep 3 event stream engine that allows the use
of multiple threads during the evaluation of a query. Various parallelization
strategies are presented and described on simple examples. The implementation
of these strategies is then evaluated empirically on a sample of problems.
Compared to the previous, single-threaded version of the BeepBeep engine, the
allocation of just a few threads to specific portions of a query provides
dramatic improvement in terms of running time
Hardware-conscious query processing for the many-core era
Die optimale Nutzung von moderner Hardware zur Beschleunigung von Datenbank-Anfragen ist keine triviale Aufgabe. Viele DBMS als auch DSMS der letzten Jahrzehnte basieren auf Sachverhalten, die heute kaum noch GĂŒltigkeit besitzen. Ein Beispiel hierfĂŒr sind heutige Server-Systeme, deren HauptspeichergröĂe im Bereich mehrerer Terabytes liegen kann und somit den Weg fĂŒr Hauptspeicherdatenbanken geebnet haben. Einer der gröĂeren letzten Hardware Trends geht hin zu Prozessoren mit einer hohen Anzahl von Kernen, den sogenannten Manycore CPUs. Diese erlauben hohe ParallelitĂ€tsgrade fĂŒr Programme durch Multithreading sowie Vektorisierung (SIMD), was die Anforderungen an die Speicher-Bandbreite allerdings deutlich erhöht. Der sogenannte High-Bandwidth Memory (HBM) versucht diese LĂŒcke zu schlieĂen, kann aber ebenso wie Many-core CPUs jeglichen Performance-Vorteil negieren, wenn dieser leichtfertig eingesetzt wird. Diese Arbeit stellt die Many-core CPU-Architektur zusammen mit HBM vor, um Datenbank sowie Datenstrom-Anfragen zu beschleunigen. Es wird gezeigt, dass ein hardwarenahes Kostenmodell zusammen mit einem Kalibrierungsansatz die Performance verschiedener Anfrageoperatoren verlĂ€sslich vorhersagen kann. Dies ermöglicht sowohl eine adaptive Partitionierungs und Merge-Strategie fĂŒr die Parallelisierung von Datenstrom-Anfragen als auch eine ideale Konfiguration von Join-Operationen auf einem DBMS. Nichtsdestotrotz ist nicht jede Operation und Anwendung fĂŒr die Nutzung einer Many-core CPU und HBM geeignet. Datenstrom-Anfragen sind oft auch an niedrige Latenz und schnelle Antwortzeiten gebunden, welche von höherer Speicher-Bandbreite kaum profitieren können. Hinzu kommen ĂŒblicherweise niedrigere Taktraten durch die hohe Kernzahl der CPUs, sowie Nachteile fĂŒr geteilte Datenstrukturen, wie das Herstellen von Cache-KohĂ€renz und das Synchronisieren von parallelen Thread-Zugriffen. Basierend auf den Ergebnissen dieser Arbeit lĂ€sst sich ableiten, welche parallelen Datenstrukturen sich fĂŒr die Verwendung von HBM besonders eignen. Des Weiteren werden verschiedene Techniken zur Parallelisierung und Synchronisierung von Datenstrukturen vorgestellt, deren Effizienz anhand eines Mehrwege-Datenstrom-Joins demonstriert wird.Exploiting the opportunities given by modern hardware for accelerating query processing speed is no trivial task. Many DBMS and also DSMS from past decades are based on fundamentals that have changed over time, e.g., servers of today with terabytes of main memory capacity allow complete avoidance of spilling data to disk, which has prepared the ground some time ago for main memory databases. One of the recent trends in hardware are many-core processors with hundreds of logical cores on a single CPU, providing an intense degree of parallelism through multithreading as well as vectorized instructions (SIMD). Their demand for memory bandwidth has led to the further development of high-bandwidth memory (HBM) to overcome the memory wall. However, many-core CPUs as well as HBM have many pitfalls that can nullify any performance gain with ease. In this work, we explore the many-core architecture along with HBM for database and data stream query processing. We demonstrate that a hardware-conscious cost model with a calibration approach allows reliable performance prediction of various query operations. Based on that information, we can, therefore, come to an adaptive partitioning and merging strategy for stream query parallelization as well as finding an ideal configuration of parameters for one of the most common tasks in the history of DBMS, join processing. However, not all operations and applications can exploit a many-core processor or HBM, though. Stream queries optimized for low latency and quick individual responses usually do not benefit well from more bandwidth and suffer from penalties like low clock frequencies of many-core CPUs as well. Shared data structures between cores also lead to problems with cache coherence as well as high contention. Based on our insights, we give a rule of thumb which data structures are suitable to parallelize with focus on HBM usage. In addition, different parallelization schemas and synchronization techniques are evaluated, based on the example of a multiway stream join operation
Transitioning power distribution grid into nanostructured ecosystem : prosumer-centric sovereignty
PhD ThesisGrowing acceptance for in-house Distributed Energy Resource (DER) installations at lowvoltage
level have gained much significance in recent years due to electricity market liberalisations
and opportunities in reduced energy billings through personalised utilisation
management for targeted business model. In consequence, modelling of passive customersâ
electric power system are progressively transitioned into Prosumer-based settings where presidency
for Transactive Energy (TE) system framework is favoured. It amplifies Prosumersâ
commitments into annexing TE values during market participations and optimised energy
management to earn larger rebates and incentives from TE programs. However, when dealing
with mass Behind-The-Meter DER administrations, Utility foresee managerial challenges
when dealing with distribution network analysis, planning, protection, and power quality
security based on Prosumersâ flexibility in optimising their energy needs.
This dissertation contributes prepositions into modelling Distributed Energy Resources
Management System (DERMS) as an aggregator designed for Prosumer-centered cooperation,
interoperating TE control and coordination as key parameters to market for both
optimised energy trading and ancillary services in a Community setting. However, Prosumers
are primarily driven to create a profitable business model when modelling their
DERMS aggregator. Greedy-optimisation exploitations are negative concerns when decisions
made resulted in detrimental-uncoordinated outcomes on Demand-Side Response (DSR)
and capacity market engagements. This calls for policy decision makers to contract safe (i.e.
cooperative yet competitive tendency) business models for Prosumers to maximise TE values
while enhancing networkâs power quality metrics and reliability performances.
Firstly, digitalisation and nanostructuring of distribution network is suggested to identify
Prosumer as a sole energy citizen while extending bilateral trading between Prosumer-to-
Prosumer (PtP) with the involvements of other grid operatorsâTE system. Modelling of
Nanogrid environment for DER integrations and establishment of local area network infrastructure
for IoT security (i.e. personal computing solutions and data protection) are committed
for communal engagements in a decentralise setting. Secondly, a multi-layered Distributed
Control Framework (DCF) is proposed using Microsoft Azure cloud-edge platform that cascades energy actors into respective layers of TE control and coordination. Furthermore,
modelling of flexi-edge computing architecture is proposed, comprising of Contract-Oriented
Sensor-based Application Platform (COSAP) employing Multi-Agent System (MAS) to
enhance data-sharing privacy and contract coalition agreements during PtP engagements.
Lastly, the Agents of MAS are programmed with cooperative yet competitive intelligences
attributed to Reinforcement Learning (RL) and Neural Networks (NN) algorithms to solve
multimodal socio-economical and uncertainty problems that corresponded to Prosumersâ
dynamic energy priorities within the TE framework. To verify the DERMS aggregator
operations, three business models were proposed (i.e. greedy-profit margin, collegial-peak
demand, reserved-standalone) to analyse comparative technical/physical and economic/social
dimensions. Results showed that the proposed TE-valued DERMS aggregator provides
participation versatility in the electricity market that enables competitive edginess when utilising
Behind-The-Meter DERs in view of Prosumerâs asset scheduling, bidding strategy, and
corroborative ancillary services. Performance metrics were evaluated on both domestic and
industrial NG environments against IEEE Standard 2030.7-2017 & 2030.8-2018 compliances
to ensure deployment practicability.
Subsequently, proposed in-house protection system for DER installation serves as an
add-on monitoring service which can be incorporated into existing Advance Distribution
Management System (ADMS) for Distribution Service Operator (DSO) and field engineers
use, ADMS aggregator. It provides early fault detections and isolation processes from allowing
fault current to propagate upstream causing cascading power quality issues across
the feeder line. In addition, ADMS aggregator also serves as islanding indicator that distinguishes
Nanogridâs islanding state from unintentional or intentional operations. Therefore, a
Overcurrent Current Relay (OCR) is proposed using Fuzzy Logic (FL) algorithm to detect,
profile, and provide decisional isolation processes using specified OCRs. Moreover, the
proposed expert knowledge in FL is programmed to detect fault crises despite insufficient
fault current level contributed by DER (i.e. solar PV system) which conventional OCR fails
to trigger
Tietuekimppujen indeksointi flash-muistilla
In database applications, bulk operations which affect multiple records at once are common. They are performed when operations on single records at a time are not efficient enough. They can occur in several ways, both by applications naturally having bulk operations (such as a sales database which updates daily) and by applications performing them routinely as part of some other operation.
While bulk operations have been studied for decades, their use with flash memory has been studied less. Flash memory, an increasingly popular alternative/complement to magnetic hard disks, has far better seek times, low power consumption and other desirable characteristics for database applications. However, erasing data is a costly operation, which means that designing index structures specifically for flash disks is useful. This thesis will investigate flash memory on data structures in general, identifying some common design traits, and incorporate those traits into a novel index structure, the bulk index.
The bulk index is an index structure for bulk operations on flash memory, and was experimentally compared to a flash-based index
structure that has shown impressive results, the Lazy Adaptive Tree (LA-tree for short).
The bulk insertion experiments were made with varying-sized elementary bulks, i.e. maximal sets of inserted keys that fall between two consecutive keys in the existing data.
The bulk index consistently performed better than the LA-tree, and especially well on bulk insertion experiments with many very small or a few very large elementary bulks, or with large inserted bulks.
It was more than 4 times as fast at best. On range searches, it performed up to 50 % faster than the LA-tree, performing better on large ranges. Range deletions were also shown to be constant-time on the bulk index.Tietokantasovelluksissa kimppuoperaatiot jotka vaikuttavat useampaan alkioon kerralla ovat yleisiÀ, ja niitÀ kÀytetÀÀn tehostamaan tietokannan toimintaa. NiitÀ voi kÀyttÀÀ kun data lisÀtÀÀn tietokantaan suuressa erÀssÀ (esimerkiksi myyntidata jota pÀivitetÀÀn kerran pÀivÀssÀ)tai osana muita tietokantaoperaatioita.
Kimppuoperaatioita on tutkittu jo vuosikymmeniÀ, mutta niiden kÀyttöÀ flash-muistilla on tutkittu vÀhemmÀn. Flash-muisti on yleistyvÀ muistiteknologiajota kÀytetÀÀn magneettisten kiintolevyjen sijaan tai niiden rinnalla. Sen tietokannoille hyödyllisiin ominaisuuksiin kuuluvat mm. nopeat hakuajat ja alhainen sÀhkönkulutus. Kuitenkin datan poisto levyltÀ on työlÀs operaatio flash-levyillÀ, mistÀ johtuen tietorakenteet kannattaa suunnitella erikseen flash-levyille. TÀmÀ työ tutkii flashin kÀyttöÀ tietorakenteissa ja koostaa niistÀ flashille soveltuvia suunnitteluperiaatteita.
NÀitÀ periaatteita edustaa myös työssÀ esitetty uusi rakenne, kimppuhakemisto (bulk index). Kimppuhakemisto on tietorakenne kimppuoperaatioille flash-muistilla, ja sitÀ verrataan kokeellisesti LA-puuhun (Lazy Adaptive Tree, suom. laiska adaptiivinen puu), joka on suoriutunut hyvin kokeissa flash-muistilla.
Kokeissa kÀytettiin vaihtelevan kokoisia alkeiskimppuja, eli maksimaalisia joukkoja lisÀtyssÀ datassa jotka sijoittuvat kahden olemassaolevan avaimen vÀliin.
Kimppuhakemisto oli nopeampi kuin LA-puu, ja erityisen paljon nopeampi kimppulisÀyksissÀ pienellÀ mÀÀrÀllÀ hyvin suuria tai suurella mÀÀrÀllÀ hyvin pieniÀ alkeiskimppuja, tai suurilla kimppulisÀyksillÀ.
Parhaimmillaan se oli yli neljÀ kertaa nopeampi. VÀlihauissa se oli jopa 50 % nopeampi kuin LA-puu, ja parempi suurten vÀlien kanssa. VÀlipoistot nÀytettiin vakioaikaisiksi kimppuhakemistossa
Allocation Strategies for Data-Oriented Architectures
Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. The tight coupling of data and processing thereon is implemented in different systems in a variety of application scenarios such as data analysis, database-as-a-service, and data management on multiprocessor systems. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies, i.e., methods that decide the mapping from tasks to nodes, are core components in data-oriented systems. Good allocation strategies can lead to balanced systems while bad allocation strategies cause skew in the load and therefore suboptimal application performance and infrastructure utilization. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. To ensure the scalability of data-oriented systems and to keep them manageable with hundreds of thousands of tasks, thousands of nodes, and dynamic workloads, fast and reliable allocation strategies are mandatory. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms.
Therefore, we show that systems from different application scenarios with different abstraction levels can be generalized to generic infrastructure and workload descriptions. We use weighted graph representations to model infrastructures with bounded and unbounded, i.e., overcommited, resources and possibly non-linear performance characteristics. Based on our generalized infrastructure and workload model, we formalize the allocation problem, which seeks valid and balanced allocations that minimize communication. Our allocation strategies partition the workload graph using solution heuristics that work with single and multiple vertex weights. Novel extensions to these solution heuristics can be used to balance penalized and secondary graph partition weights. These extensions enable the allocation strategies to handle infrastructures with non-linear performance behavior. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end--performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods
Advancements in Real-Time Simulation of Power and Energy Systems
Modern power and energy systems are characterized by the wide integration of distributed generation, storage and electric vehicles, adoption of ICT solutions, and interconnection of different energy carriers and consumer engagement, posing new challenges and creating new opportunities. Advanced testing and validation methods are needed to efficiently validate power equipment and controls in the contemporary complex environment and support the transition to a cleaner and sustainable energy system. Real-time hardware-in-the-loop (HIL) simulation has proven to be an effective method for validating and de-risking power system equipment in highly realistic, flexible, and repeatable conditions. Controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) are the two main HIL simulation methods used in industry and academia that contribute to system-level testing enhancement by exploiting the flexibility of digital simulations in testing actual controllers and power equipment. This book addresses recent advances in real-time HIL simulation in several domains (also in new and promising areas), including technique improvements to promote its wider use. It is composed of 14 papers dealing with advances in HIL testing of power electronic converters, power system protection, modeling for real-time digital simulation, co-simulation, geographically distributed HIL, and multiphysics HIL, among other topics
Smart Energy Management for Smart Grids
This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book
The Democratization of Artificial Intelligence
After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms