Hiding Access-pattern is Not Enough! Veil: A Storage and Communication Efficient Volume-Hiding Algorithm

This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage, coupled with prior knowledge about data distribution and/or previously executed queries, can reveal both ciphertexts and current user queries. We develop a solution to prevent volume leakage, entitled Veil, that partitions the dataset by randomly mapping keys to a set of equi-sized buckets. Veil provides a tunable mechanism for data owners to explore a trade-off between storage and communication overheads. To make buckets indistinguishable to the adversary, Veil uses a novel padding strategy that allow buckets to overlap, reducing the need to add fake records. Both theoretical and experimental results show Veil to significantly outperform existing state-of-the-art

Database System Acceleration on FPGAs

Relational database systems provide various services and applications with an efficient means for storing, processing, and retrieving their data. The performance of these systems has a direct impact on the quality of service of the applications that rely on them. Therefore, it is crucial that database systems are able to adapt and grow in tandem with the demands of these applications, ensuring that their performance scales accordingly. In the past, Moore's law and algorithmic advancements have been sufficient to meet these demands. However, with the slowdown of Moore's law, researchers have begun exploring alternative methods, such as application-specific technologies, to satisfy the more challenging performance requirements. One such technology is field-programmable gate arrays (FPGAs), which provide ideal platforms for developing and running custom architectures for accelerating database systems. The goal of this thesis is to develop a domain-specific architecture that can enhance the performance of in-memory database systems when executing analytical queries. Our research is guided by a combination of academic and industrial requirements that seek to strike a balance between generality and performance. The former ensures that our platform can be used to process a diverse range of workloads, while the latter makes it an attractive solution for high-performance use cases. Throughout this thesis, we present the development of a system-on-chip for database system acceleration that meets our requirements. The resulting architecture, called CbMSMK, is capable of processing the projection, sort, aggregation, and equi-join database operators and can also run some complex TPC-H queries. CbMSMK employs a shared sort-merge pipeline for executing all these operators, which results in an efficient use of FPGA resources. This approach enables the instantiation of multiple acceleration cores on the FPGA, allowing it to serve multiple clients simultaneously. CbMSMK can process both arbitrarily deep and wide tables efficiently. The former is achieved through the use of the sort-merge algorithm which utilizes the FPGA RAM for buffering intermediate sort results. The latter is achieved through the use of KeRRaS, a novel variant of the forward radix sort algorithm introduced in this thesis. KeRRaS allows CbMSMK to process a table a few columns at a time, incrementally generating the final result through multiple iterations. Given that acceleration is a key objective of our work, CbMSMK benefits from many performance optimizations. For instance, multi-way merging is employed to reduce the number of merge passes required for the execution of the sort-merge algorithm, thus improving the performance of all our pipeline-breaking operators. Another example is our in-depth analysis of early aggregation, which led to the development of a novel cache-based algorithm that significantly enhances aggregation performance. Our experiments demonstrate that CbMSMK performs on average 5 times faster than the state-of-the-art CPU-based database management system MonetDB.:I Database Systems & FPGAs 1 INTRODUCTION 1.1 Databases & the Importance of Performance 1.2 Accelerators & FPGAs 1.3 Requirements 1.4 Outline & Summary of Contributions 2 BACKGROUND ON DATABASE SYSTEMS 2.1 Databases 2.1.1 Storage Model 2.1.2 Storage Medium 2.2 Database Operators 2.2.1 Projection 2.2.2 Filter 2.2.3 Sort 2.2.4 Aggregation 2.2.5 Join 2.2.6 Operator Classification 2.3 Database Queries 2.4 Impact of Acceleration 3 BACKGROUND ON FPGAS 3.1 FPGA 3.1.1 Logic Element 3.1.2 Block RAM (BRAM) 3.1.3 Digital Signal Processor (DSP) 3.1.4 IO Element 3.1.5 Programmable Interconnect 3.2 FPGADesignFlow 3.2.1 Specifications 3.2.2 RTL Description 3.2.3 Verification 3.2.4 Synthesis, Mapping, Placement, and Routing 3.2.5 TimingAnalysis 3.2.6 Bitstream Generation and FPGA Programming 3.3 Implementation Quality Metrics 3.4 FPGA Cards 3.5 Benefits of Using FPGAs 3.6 Challenges of Using FPGAs 4 RELATED WORK 4.1 Summary of Related Work 4.2 Platform Type 4.2.1 Accelerator Card 4.2.2 Coprocessor 4.2.3 Smart Storage 4.2.4 Network Processor 4.3 Implementation 4.3.1 Loop-based implementation 4.3.2 Sort-based Implementation 4.3.3 Hash-based Implementation 4.3.4 Mixed Implementation 4.4 A Note on Quantitative Performance Comparisons II Cache-Based Morphing Sort-Merge with KeRRaS (CbMSMK) 5 OBJECTIVES AND ARCHITECTURE OVERVIEW 5.1 From Requirements to Objectives 5.2 Architecture Overview 5.3 Outlineof Part II 6 COMPARATIVE ANALYSIS OF OPENCL AND RTL FOR SORT-MERGE PRIMITIVES ON FPGAS 6.1 Programming FPGAs 6.2 RelatedWork 6.3 Architecture 6.3.1 Global Architecture 6.3.2 Sorter Architecture 6.3.3 Merger Architecture 6.3.4 Scalability and Resource Adaptability 6.4 Experiments 6.4.1 OpenCL Sort-Merge Implementation 6.4.2 RTLSorters 6.4.3 RTLMergers 6.4.4 Hybrid OpenCL-RTL Sort-Merge Implementation 6.5 Summary & Discussion 7 RESOURCE-EFFICIENT ACCELERATION OF PIPELINE-BREAKING DATABASE OPERATORS ON FPGAS 7.1 The Case for Resource Efficiency 7.2 Related Work 7.3 Architecture 7.3.1 Sorters 7.3.2 Sort-Network 7.3.3 X:Y Mergers 7.3.4 Merge-Network 7.3.5 Join Materialiser (JoinMat) 7.4 Experiments 7.4.1 Experimental Setup 7.4.2 Implementation Description & Tuning 7.4.3 Sort Benchmarks 7.4.4 Aggregation Benchmarks 7.4.5 Join Benchmarks 7. Summary 8 KERRAS: COLUMN-ORIENTED WIDE TABLE PROCESSING ON FPGAS 8.1 The Scope of Database System Accelerators 8.2 Related Work 8.3 Key-Reduce Radix Sort(KeRRaS) 8.3.1 Time Complexity 8.3.2 Space Complexity (Memory Utilization) 8.3.3 Discussion and Optimizations 8.4 Architecture 8.4.1 MSM 8.4.2 MSMK: Extending MSM with KeRRaS 8.4.3 Payload, Aggregation and Join Processing 8.4.4 Limitations 8.5 Experiments 8.5.1 Experimental Setup 8.5.2 Datasets 8.5.3 MSMK vs. MSM 8.5.4 Payload-Less Benchmarks 8.5.5 Payload-Based Benchmarks 8.5.6 Flexibility 8.6 Summary 9 A STUDY OF EARLY AGGREGATION IN DATABASE QUERY PROCESSING ON FPGAS 9.1 Early Aggregation 9.2 Background & Related Work 9.2.1 Sort-Based Early Aggregation 9.2.2 Cache-Based Early Aggregation 9.3 Simulations 9.3.1 Datasets 9.3.2 Metrics 9.3.3 Sort-Based Versus Cache-Based Early Aggregation 9.3.4 Comparison of Set-Associative Caches 9.3.5 Comparison of Cache Structures 9.3.6 Comparison of Replacement Policies 9.3.7 Cache Selection Methodology 9.4 Cache System Architecture 9.4.1 Window Aggregator 9.4.2 Compressor & Hasher 9.4.3 Collision Detector 9.4.4 Collision Resolver 9.4.5 Cache 9.5 Experiments 9.5.1 Experimental Setup 9.5.2 Resource Utilization and Parameter Tuning 9.5.3 Datasets 9.5.4 Benchmarks on Synthetic Data 9.5.5 Benchmarks on Real Data 9.6 Summary 10 THE FULL PICTURE 10.1 System Architecture 10.2 Benchmarks 10.3 Meeting the Objectives III Conclusion 11 SUMMARY AND OUTLOOK ON FUTURE RESEARCH 11.1 Summary 11.2 Future Work BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLE

Toward timely, predictable and cost-effective data analytics

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to extract deep insights is now a key ingredient for success. Traditional database systems (DBMS) are, however, not the first choice for servicing these modern applications, despite 40 years of database research. This is due to the fact that modern applications exhibit different behavior from the one assumed by DBMS: a) timely data exploration as a new trend is characterized by ad-hoc queries and a short user interaction period, leaving little time for DBMS to do good performance tuning, b) accurate statistics representing relevant summary information about distributions of ever increasing data are frequently missing, resulting in suboptimal plan decisions and consequently poor and unpredictable query execution performance, and c) cloud service providers - a major winner in the data analytics game due to the low cost of (shared) storage - have shifted the control over data storage from DBMS to the cloud providers, making it harder for DBMS to optimize data access. This thesis demonstrates that database systems can still provide timely, predictable and cost-effective analytical processing, if they use an agile and adaptive approach. In particular, DBMS need to adapt at three levels (to workload, data and hardware characteristics) in order to stabilize and optimize performance and cost when faced with requirements posed by modern data analytics applications. Workload-driven data ingestion is introduced with NoDB as a means to enable efficient data exploration and reduce the data-to-insight time (i.e., the time to load the data and tune the system) by doing these steps lazily and incrementally as a side-effect of posed queries as opposed to mandatory first steps. Data-driven runtime access path decision making introduced with Smooth Scan alleviates suboptimal query execution, postponing the decision on access paths from query optimization, where statistics are heavily exploited, to query execution, where the system can obtain more details about data distributions. Smooth Scan uses access path morphing from one physical alternative to another to fit the observed data distributions, which removes the need for a priori access path decisions and substantially improves the predictability of DBMS. Hardware-driven query execution introduced with Skipper enables the usage of cold storage devices (CSD) as a cost-effective solution for storing the ever increasing customer data. Skipper uses an out-of-order CSD-driven query execution model based on multi-way joins coupled with efficient cache and I/O scheduling policies to hide the non-uniform access latencies of CSD. This thesis advocates runtime adaptivity as a key to dealing with raising uncertainty about workload characteristics that modern data analytics applications exhibit. Overall, the techniques introduced in this thesis through the three levels of adaptivity (workload, data and hardware-driven adaptivity) increase the usability of database systems and the user satisfaction in the case of big data exploration, making low-cost data analytics reality

Energy-Aware Data Management on NUMA Architectures

The ever-increasing need for more computing and data processing power demands for a continuous and rapid growth of power-hungry data center capacities all over the world. As a first study in 2008 revealed, energy consumption of such data centers is becoming a critical problem, since their power consumption is about to double every 5 years. However, a recently (2016) released follow-up study points out that this threatening trend was dramatically throttled within the past years, due to the increased energy efficiency actions taken by data center operators. Furthermore, the authors of the study emphasize that making and keeping data centers energy-efficient is a continuous task, because more and more computing power is demanded from the same or an even lower energy budget, and that this threatening energy consumption trend will resume as soon as energy efficiency research efforts and its market adoption are reduced. An important class of applications running in data centers are data management systems, which are a fundamental component of nearly every application stack. While those systems were traditionally designed as disk-based databases that are optimized for keeping disk accesses as low a possible, modern state-of-the-art database systems are main memory-centric and store the entire data pool in the main memory, which replaces the disk as main bottleneck. To scale up such in-memory database systems, non-uniform memory access (NUMA) hardware architectures are employed that face a decreased bandwidth and an increased latency when accessing remote memory compared to the local memory. In this thesis, we investigate energy awareness aspects of large scale-up NUMA systems in the context of in-memory data management systems. To do so, we pick up the idea of a fine-grained data-oriented architecture and improve the concept in a way that it keeps pace with increased absolute performance numbers of a pure in-memory DBMS and scales up on NUMA systems in the large scale. To achieve this goal, we design and build ERIS, the first scale-up in-memory data management system that is designed from scratch to implement a data-oriented architecture. With the help of the ERIS platform, we explore our novel core concept for energy awareness, which is Energy Awareness by Adaptivity. The concept describes that software and especially database systems have to quickly respond to environmental changes (i.e., workload changes) by adapting themselves to enter a state of low energy consumption. We present the hierarchically organized Energy-Control Loop (ECL), which is a reactive control loop and provides two concrete implementations of our Energy Awareness by Adaptivity concept, namely the hardware-centric Resource Adaptivity and the software-centric Storage Adaptivity. Finally, we will give an exhaustive evaluation regarding the scalability of ERIS as well as our adaptivity facilities