Power Efficient Data-Aware SRAM Cell for SRAM-Based FPGA Architecture

The design of low-power SRAM cell becomes a necessity in today\u27s FPGAs, because SRAM is a critical component in FPGA design and consumes a large fraction of the total power. The present chapter provides an overview of various factors responsible for power consumption in FPGA and discusses the design techniques of low-power SRAM-based FPGA at system level, device level, and architecture levels. Finally, the chapter proposes a data-aware dynamic SRAM cell to control the power consumption in the cell. Stack effect has been adopted in the design to reduce the leakage current. The various peripheral circuits like address decoder circuit, write/read enable circuits, and sense amplifier have been modified to implement a power-efficient SRAM-based FPGA

Storage Format Selection and Optimization for Materialized Intermediate Results in Data-Intensive Flows

Modern organizations produce and collect large volumes of data, that need to be processed repeatedly and quickly for gaining business insights. For such processing, typically, Data-intensive Flows (DIFs) are deployed on distributed processing frameworks. The DIFs of different users have many computation overlaps (i.e., parts of the processing are duplicated), thus wasting computational resources and increasing the overall cost. The output of these computation overlaps (known as intermediate results) can be materialized for reuse, which helps in reducing the cost and saves computational resources if properly done. Furthermore, the way such outputs are materialized must be considered, as different storage layouts (i.e., horizontal, vertical, and hybrid) can be used to reduce the I/O cost. In this PhD work, we first propose a novel approach for automatically materializing the intermediate results of DIFs through a multi-objective optimization method, which can tackle multiple and conflicting quality metrics. Next, we study the behavior of different operators of DIFs that are the first to process the loaded materialized results. Based on this study, we devise a rule-based approach, that decides the storage layout for materialized results based on the subsequent operation types. Despite improving the cost in general, the heuristic rules do not consider the amount of data read while making the choice, which could lead to a wrong decision. Thus, we design a cost model that is capable of finding the right storage layout for every scenario. The cost model uses data and workload characteristics to estimate the I/O cost of a materialized intermediate results with different storage layouts and chooses the one which has minimum cost. The results show that storage layouts help to reduce the loading time of materialized results and overall, they improve the performance of DIFs. The thesis also focuses on the optimization of the configurable parameters of hybrid layouts. We propose ATUN-HL (Auto TUNing Hybrid Layouts), which based on the same cost model and given the workload and characteristics of data, finds the optimal values for configurable parameters in hybrid layouts (i.e., Parquet). Finally, the thesis also studies the impact of parallelism in DIFs and hybrid layouts. Our proposed cost model helps to devise an approach for fine-tuning the parallelism by deciding the number of tasks and machines to process the data. Thus, the cost model proposed in this thesis, enables in choosing the best possible storage layout for materialized intermediate results, tuning the configurable parameters of hybrid layouts, and estimating the number of tasks and machines for the execution of DIFs.Moderne Unternehmen produzieren und sammeln große Datenmengen, die wiederholt und schnell verarbeitet werden müssen, um geschäftliche Erkenntnisse zu gewinnen. Für die Verarbeitung dieser Daten werden typischerweise Datenintensive Prozesse (DIFs) auf verteilten Systemen wie z.B. MapReduce bereitgestellt. Dabei ist festzustellen, dass die DIFs verschiedener Nutzer sich in großen Teilen überschneiden, wodurch viel Arbeit mehrfach geleistet, Ressourcen verschwendet und damit die Gesamtkosten erhöht werden. Um diesen Effekt entgegenzuwirken, können die Zwischenergebnisse der DIFs für spätere Wiederverwendungen materialisiert werden. Hierbei müssen vor allem die unterschiedlichen Speicherlayouts (horizontal, vertikal und hybrid) berücksichtigt werden. In dieser Doktorarbeit wird ein neuartiger Ansatz zur automatischen Materialisierung der Zwischenergebnisse von DIFs durch eine mehrkriterielle Optimierungsmethode vorgeschlagen, der in der Lage ist widersprüchliche Qualitätsmetriken zu behandeln. Des Weiteren wird untersucht die Wechselwirkung zwischen verschiedenen peratortypen und unterschiedlichen Speicherlayouts untersucht. Basierend auf dieser Untersuchung wird ein regelbasierter Ansatz vorgeschlagen, der das Speicherlayout für materialisierte Ergebnisse, basierend auf den nachfolgenden Operationstypen, festlegt. Obwohl sich die Gesamtkosten für die Ausführung der DIFs im Allgemeinen verbessern, ist der heuristische Ansatz nicht in der Lage die gelesene Datenmenge bei der Auswahl des Speicherlayouts zu berücksichtigen. Dies kann in einigen Fällen zu falschen Entscheidung führen. Aus diesem Grund wird ein Kostenmodell entwickelt, mit dem für jedes Szenario das richtige Speicherlayout gefunden werden kann. Das Kostenmodell schätzt anhand von Daten und Auslastungsmerkmalen die E/A-Kosten eines materialisierten Zwischenergebnisses mit unterschiedlichen Speicherlayouts und wählt das kostenminimale aus. Die Ergebnisse zeigen, dass Speicherlayouts die Ladezeit materialisierter Ergebnisse verkürzen und insgesamt die Leistung von DIFs verbessern. Die Arbeit befasst sich auch mit der Optimierung der konfigurierbaren Parameter von hybriden Layouts. Konkret wird der sogenannte ATUN-HL Ansatz (Auto TUNing Hybrid Layouts) entwickelt, der auf der Grundlage des gleichen Kostenmodells und unter Berücksichtigung der Auslastung und der Merkmale der Daten die optimalen Werte für konfigurierbare Parameter in Parquet, d.h. eine Implementierung von hybrider Layouts. Schließlich werden in dieser Arbeit auch die Auswirkungen von Parallelität in DIFs und hybriden Layouts untersucht. Dazu wird ein Ansatz entwickelt, der in der Lage ist die Anzahl der Aufgaben und dafür notwendigen Maschinen automatisch zu bestimmen. Zusammengefasst lässt sich festhalten, dass das in dieser Arbeit vorgeschlagene Kostenmodell es ermöglicht, das bestmögliche Speicherlayout für materialisierte Zwischenergebnisse zu ermitteln, die konfigurierbaren Parameter hybrider Layouts festzulegen und die Anzahl der Aufgaben und Maschinen für die Ausführung von DIFs zu schätzen

Storage format selection and optimization for materialized intermediate results in data-intensive flows

Tesi en modalitat de cotuela: Universitat Politècnica de Catalunya i Technische Universität DresdenModern organizations produce and collect large volumes of data, that need to be processed repeatedly and quickly for gaining business insights. For such processing, typically, Data-intensive Flows (DIFs) are deployed on distributed processing frameworks. The DIFs of different users have many computation overlaps (i.e., parts of the processing are duplicated), thus wasting computational resources and increasing the overall cost. The output of these computation overlaps (known as intermediate results) can be materialized for reuse, which helps in reducing the cost and saves computational resources if properly done. Furthermore, the way such outputs are materialized must be considered, as different storage layouts (i.e., horizontal, vertical, and hybrid) can be used to reduce the I/O cost. In this PhD work, we first propose a novel approach for automatically materializing the intermediate results of DIFs through a multi-objective optimization method, which can tackle multiple and conflicting quality metrics. Next, we study the behavior of different operators of DIFs that are the first to process the loaded materialized results. Based on this study, we devise a rule-based approach, that decides the storage layout for materialized results based on the subsequent operation types. Despite improving the cost in general, the heuristic rules do not consider the amount of data read while making the choice, which could lead to a wrong decision. Thus, we design a cost model that is capable of finding the right storage layout for every scenario. The cost model uses data and workload characteristics to estimate the I/O cost of a materialized intermediate results with different storage layouts and chooses the one which has minimum cost. The results show that storage layouts help to reduce the loading time of materialized results and overall, they improve the performance of DIFs. The thesis also focuses on the optimization of the configurable parameters of hybrid layouts. We propose ATUN-HL (Auto TUNing Hybrid Layouts), which based on the same cost model and given the workload and characteristics of data, finds the optimal values for configurable parameters in hybrid layouts (i.e., Parquet). Finally, the thesis also studies the impact of parallelism in DIFs and hybrid layouts. Our proposed cost model helps to devise an approach for fine-tuning the parallelism by deciding the number of tasks and machines to process the data. Thus, the cost model proposed in this thesis, enables in choosing the best possible storage layout for materialized intermediate results, tuning the configurable parameters of hybrid layouts, and estimating the number of tasks and machines for the execution of DIFs.Las organizaciones producen y recopilan grandes volúmenes de datos, que deben procesarse de forma repetitiva y rápida para obtener información relevante para la empresa. Para tal procesamiento, por lo general, se emplean flujos intensivos de datos (DIFs por sussiglas en inglés) en entornos de procesamiento distribuido. Los DIFs de diferentes usuarios tienen elementos comunes (es decir, se duplican partes del procesamiento, lo que desperdicia recursos computacionales y aumenta el coste en general). Los resultados intermedios de varios DIFs pueden pues coincidir y se pueden por tanto materializar para facilitar su reutilización, lo que ayuda a reducir el coste y ahorrar recursos si se realiza correctamente. Además, la forma en qué se materializan dichos resultados debe ser considerada. Por ejemplo, diferentes tipos de diseño lógico de los datos (es decir, horizontal, vertical o híbrido) se pueden utilizar para reducir el coste de E/S. En esta tesis doctoral, primero proponemos un enfoque novedoso para materializar automáticamente los resultados intermedios de los DIFs a través de un método de optimización multi-objetivo, que puede considerar múltiples y contradictorias métricas de calidad. A continuación, estudiamos el comportamiento de diferentes operadores de DIF que acceden directamente a los resultados materializados. Sobre la base de este estudio, ideamos un enfoque basado en reglas, que decide el diseño del almacenamiento para los resultados materializados en función de los tipos de operaciones que los utilizan directamente. A pesar de mejorar el coste en general, las reglas heurísticas no consideran estadísticas sobre la cantidad de datos leídos al hacer la elección, lo que podría llevar a una decisión errónea. Consecuentemente, diseñamos un modelo de costos que es capaz de encontrar el diseño de almacenamiento adecuado para cada escenario dependiendo de las características de los datos almacenados. El modelo de costes usa estadísticas y características de acceso para estimar el coste de E/S de un resultado intervii medio materializado con diferentes diseños de almacenamiento y elige el de menor coste. Los resultados muestran que los diseños de almacenamiento ayudan a reducir el tiempo de carga de los resultados materializados y, en general, mejoran el rendimiento de los DIF. La tesis también presta atención a la optimización de los parámetros configurables de diseños híbridos. Proponemos así ATUN-HL (Auto TUNing Hybrid Layouts), que, basado en el mismo modelo de costes, las características de los datos y el tipo de acceso que se está haciendo, encuentra los valores óptimos para los parámetros de configuración en disponibles Parquet (una implementación de diseños híbridos para Hadoop Distributed File System). Finalmente, esta tesis estudia el impacto del paralelismo en DIF y diseños híbridos. El modelo de coste propuesto ayuda a idear un enfoque para ajustar el paralelismo al decidir la cantidad de tareas y máquinas para procesar los datos. En resumen, el modelo de costes propuesto permite elegir el mejor diseño de almacenamiento posible para los resultados intermedios materializados, ajustar los parámetros configurables de diseños híbridos y estimar el número de tareas y máquinas para la ejecución de DIF.Moderne Unternehmen produzieren und sammeln große Datenmengen, die wiederholt und schnell verarbeitet werden müssen, um geschäftliche Erkenntnisse zu gewinnen. Für die Verarbeitung dieser Daten werden typischerweise Datenintensive Prozesse (DIFs) auf verteilten Systemen wie z.B. MapReduce bereitgestellt. Dabei ist festzustellen, dass die DIFs verschiedener Nutzer sich in großen Teilen überschneiden, wodurch viel Arbeit mehrfach geleistet, Ressourcen verschwendet und damit die Gesamtkosten erhöht werden. Um diesen Effekt entgegenzuwirken, können die Zwischenergebnisse der DIFs für spätere Wiederverwendungen materialisiert werden. Hierbei müssen vor allem die unterschiedlichen Speicherlayouts (horizontal, vertikal und hybrid) berücksichtigt werden. In dieser Doktorarbeit wird ein neuartiger Ansatz zur automatischen Materialisierung der Zwischenergebnisse von DIFs durch eine mehrkriterielle Optimierungsmethode vorgeschlagen, der in der Lage ist widersprüchliche Qualitätsmetriken zu behandeln. Des Weiteren wird untersucht die Wechselwirkung zwischen verschiedenen Operatortypen und unterschiedlichen Speicherlayouts untersucht. Basierend auf dieser Untersuchung wird ein regelbasierter Ansatz vorgeschlagen, der das Speicherlayout für materialisierte Ergebnisse, basierend auf den nachfolgenden Operationstypen, festlegt. Obwohl sich die Gesamtkosten für die Ausführung der DIFs im Allgemeinen verbessern, ist der heuristische Ansatz nicht in der Lage die gelesene Datenmenge bei der Auswahl des Speicherlayouts zu berücksichtigen. Dies kann in einigen Fällen zu falschen Entscheidung führen. Aus diesem Grund wird ein Kostenmodell entwickelt, mit dem für jedes Szenario das richtige Speicherlayout gefunden werden kann. Das Kostenmodell schätzt anhand von Daten und Auslastungsmerkmalen die E/A-Kosten eines materialisierten Zwischenergebnisses mit unterschiedlichen Speicherlayouts und wählt das kostenminimale aus. Die Ergebnisse zeigen, dass Speicherlayouts die Ladezeit materialisierter Ergebnisse verkürzen und insgesamt die Leistung von DIFs verbessern. Die Arbeit befasst sich auch mit der Optimierung der konfigurierbaren Parameter von hybriden Layouts. Konkret wird der sogenannte ATUN-HLAnsatz (Auto TUNing Hybrid Layouts) entwickelt, der auf der Grundlage des gleichen Kostenmodells und unter Berücksichtigung der Auslastung und der Merkmale der Daten die optimalen Werte für konfigurierbare Parameter in Parquet, d.h. eine Implementierung von hybrider Layouts. Schließlich werden in dieser Arbeit auch die Auswirkungen von Parallelität in DIFs und hybriden Layouts untersucht. Dazu wird ein Ansatz entwickelt, der in der Lage ist die Anzahl der Aufgaben und dafür notwendigen Maschinen automatisch zu bestimmen. Zusammengefasst lässt sich festhalten, dass das in dieser Arbeit vorgeschlagene Kostenmodell es ermöglicht, das bestmögliche Speicherlayout für materialisierte Zwischenergebnisse zu ermitteln, die konfigurierbaren Parameter hybrider Layouts festzulegen und die Anzahl der Aufgaben und Maschinen für die Ausführung von DIFs zu schätzenPostprint (published version

KASLR-MT: kernel address space layout randomization for multi-tenant cloud systems

[EN] Cloud computing has completely changed our lives. This technology dramatically impacted on how we play, work and live. It has been widely adopted in many sectors mainly because it reduces the cost of performing tasks in a flexible, scalable and reliable way. To provide a secure cloud computing architecture, the highest possible level of protection must be applied. Unfortunately, the cloud computing paradigm introduces new scenarios where security protection techniques are weakened or disabled to obtain a better performance and resources exploitation. Kernel ASLR (KASLR) is a widely adopted protection technique present in all modern operating systems. KASLR is a very effective technique that thwarts unknown attacks but unfortunately its randomness have a significant impact on memory deduplication savings. Both techniques are very desired by the industry, the first one because of the high level of security that it provides and the latter to obtain better performance and resources exploitation. In this paper, we propose KASLR-MT, a new Linux kernel randomization approach compatible with memory deduplication. We identify why the most widely and effective technique used to mitigate attacks at kernel level, KASLR, fails to provide protection and shareability at the same time. We analyze the current Linux kernel randomization and how it affects to the shared memory of each kernel region. Then, based on the analysis, we propose KASLR-MT, the first effective and practical Kernel ASLR memory protection that maximizes the memory deduplication savings rate while providing a strong security. Our tests reveal that KASLR-MT is not intrusive, very scalable and provides strong protection without sacrificing the shareability. (C) 2019 Elsevier Inc. All rights reserved.Vañó-García, F.; Marco-Gisbert, H. (2020). KASLR-MT: kernel address space layout randomization for multi-tenant cloud systems. Journal of Parallel and Distributed Computing. 137:77-90. https://doi.org/10.1016/j.jpdc.2019.11.008S779013

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe

Automatic software for controlling cryogenic systems

A technical discussion of the lessons learned during the seven years of software development/testing which occurred on the Liquid Oxygen System for the Space Shuttle at the Kennedy Space Center is given. Problems which were solved during these years came into four distinct phases: design/debug before simulation runs, verification using simulation with models up through Space Transportation System-1 launch, hardware usage from first launch to Space Transportation System-5 launch, and future use. Each problem/solution describes the apparent problem requirements/constraints, usable alternatives, selected action, and results

CaSE: Cache-Assisted Secure Execution on ARM Processors

Recognizing the pressing demands to secure embedded applications, ARM TrustZone has been adopted in both academic research and commercial products to protect sensitive code and data in a privileged, isolated execution environment. However, the design of TrustZone cannot prevent physical memory disclosure attacks such as cold boot attack from gaining unrestricted read access to the sensitive contents in the dynamic random access memory (DRAM). A number of system-on-chip (SoC) bound execution solutions have been proposed to thaw the cold boot attack by storing sensitive data only in CPU registers, CPU cache or internal RAM. However, when the operating system, which is responsible for creating and maintaining the SoC-bound execution environment, is compromised, all the sensitive data is leaked. In this paper, we present the design and development of a cache-assisted secure execution framework, called CaSE, on ARM processors to defend against sophisticated attackers who can launch multi-vector attacks including software attacks and hardware memory disclosure attacks. CaSE utilizes TrustZone and Cache-as-RAM technique to create a cache-based isolated execution environment, which can protect both code and data of security-sensitive applications against the compromised OS and the cold boot attack. To protect the sensitive code and data against cold boot attack, applications are encrypted in memory and decrypted only within the processor for execution. The memory separation and the cache separation provided by TrustZone are used to protect the cached applications against compromised OS. We implement a prototype of CaSE on the i.MX53 running ARM Cortex-A8 processor. The experimental results show that CaSE incurs small impacts on system performance when executing cryptographic algorithms including AES, RSA, and SHA1

Development of the framework for a lean, energy efficient, and environmentally friendly port: umm qasr port as a Case Study

The research focus is to examine rigorously how the implementation of Lean within the Umm Qasr Port improves the operation processes and to explore the Lean impact on environment improvement and energy efficiency management. In this research, the ROPMEE model has been developed by the researcher to evaluate the service quality in the cargo delivery process in the Port of Umm Qasr as it covers all the functional and non-functional areas in the cargo delivery process compared to other quality dimensions. The findings confirm that the process quality dimension is the most influential factor in service quality in the Port of Umm Qasr. The reasons for the poor performance of current practices adopted by the port are the use of traditional ways of information flow and a decision-making process that requires more time and steps within the whole process. The lack of smooth process flow is a potential cause of bottlenecks within port operation that create serious problems not only for the customer but also for the port itself. In this research, a visual representation is created of how the current value stream map for different port processes has been established on the identification and elimination of non- value-added activity or “waste” involved in delivering services in Umm Qasr port for customers. A VSM tool was applied to visually map the cargo handling flow, ship entrance, ship maneuvering and cargo clearance to display the current and future states of processes in a way that highlights opportunities for improvement. Based on the defined and classified waste according to the seven deadly wastes of Lean, this research suggests a future value stream map for port processes. The impact of the identified wastes has been quantified in terms of cost, carbon dioxide emissions working time efficiency, and energy consumption cost. This research is the first attempt to develop a Lean port model for improving port processes, as there have been no previous studies aimed at providing a holistic framework for improving port performance, which can be used by other ports. Implementing the Lean approach requires a gradual shift in work culture by involving all port employees and customers in the continuous improvement process and changing the service delivery from a push to pull system