In recent years, autoscaling has garnered significant attention in cloud computing, emphasizing cost efficiency, performance optimization, and availability for dynamic workloads. New algorithms for horizontal, vertical, and hybrid scaling, targeting instances, VM specifications, and resources like CPU, memory, and IO, have emerged. Various approaches, including forecasting and custom autoscaling functions, are used. However, conducting comprehensive end-to-end testing remains a complex and costly endeavor due to the variety of technology constraints involved. This paper introduces VASIM, an autoscaling simulator toolkit designed for testing recommendation algorithms, with a particular focus on CPU usage in VMs and Kubernetes pods. The toolkit replicates common components found in autoscaler architectures, including the controller, metrics collector, recommender, and resource updater. It enables a comprehensive simulation of the entire autoscaling system's behavior, with the flexibility to customize various parameters. In our demonstration, we showcase VASIM's versatility across multiple use cases, highlighting its effectiveness in evaluating autoscaling strategies, fine-tuning parameters, comparing algorithm performance, and addressing autoscaling-related challenges. This underscores VASIM's critical role in expediting algorithm development and refinement by providing a controlled environment for testing and experimentation.

Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, cleaning, featurizing the datasets) and a machine learning (ML) part invoking one or more trained models to perform predictions. These parts have so far been optimized in isolation, leaving significant opportunities for optimization unexplored. We present Raven, a production-ready system for optimizing prediction queries. Raven follows the enterprise architectural trend of collocating data and ML runtimes. It relies on a unified intermediate representation that captures both data and ML operators in a single graph structure to unlock two families of optimizations. First, it employs logical optimizations that pass information between the data part (and the properties of the underlying data) and the ML part to optimize each other. Second, it introduces logical-to-physical transformations that allow operators to be executed on different run-times (relational, ML, and DNN) and hardware (CPU, GPU). Novel data-driven optimizations determine the runtime to be used for each part of the query to achieve optimal performance. Our evaluation shows that Raven is able to improve performance of prediction queries on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. Finally, for complex models where GPU acceleration is beneficial, Raven provides up to 8× speedup compared to state-of-the-art systems.

The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major cloud vendors. By hiding the low-level complexity through a tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this paper, we explore how database management systems can ride the wave of innovation happening in the AI space. We design, build, and evaluate Tensor Query Processor (TQP): TQP transforms SQL queries into tensor programs and executes them on TCRs. TQP is able to run the full TPC-H benchmark by implementing novel algorithms for relational operators on the tensor routines. At the same time, TQP can support various hardware while only requiring a fraction of the usual development effort. Experiments show that TQP can improve query execution time by up to 10X over specialized CPU- and GPU-only systems. Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9X speedup over CPU baselines.

User-defined functions (UDFs) have long been used as the de facto way to extend the capabilities of data management systems. However, they are restricted to the specificities of each DBMS, and recent demands for advanced analytics have increased the need for complex UDFs that may require execution of arbitrary computation written in any programming language, management of library dependencies, portability across environments and engines, and resource isolation. These requirements go beyond what traditional UDFs were designed for, and have given rise to containerized UDFs that enable encapsulation and portability. However, this approach is nascent and can result in significant performance penalties and usability issues. In this paper, we present the first study that spans all stages of containerized UDFs' life cycle, performance bottlenecks in their execution, and extensibility to support different engines. Our experiments show that the performance of containerized UDF execution can be greatly affected by system design choices and that there are many trade-offs to consider. For example, regarding the method of communication with the containerized UDF, we show that binary-based implementations minimize overheads and are more than 2.4x faster than widely used text-based ones. Adopting a newer general-purpose communication method such as Arrow Flight can improve performance dramatically, causing a minimal ~10% slowdown compared to non-containerized UDFs. Additionally, containerized UDF start times vary wildly due to program size and complexity, from .07s to 7s in our experiments. Our insights can help DBMS developers make appropriate choices based on individual use cases when designing their systems.

Deep Learning (DL) has created a growing demand for simpler ways to develop complex models and efficient ways to execute them. Thus, a significant effort has gone into frameworks like PyTorch or TensorFlow to support a variety of DL models and run efficiently and seamlessly over heterogeneous and distributed hardware. Since these frameworks will continue improving given the predominance of DL workloads, it is natural to ask what else can be done with them. This is not a trivial question since these frameworks are based on the efficient implementation of tensors, which are well adapted to DL but, in principle, to nothing else. In this paper we explore to what extent Tensor Computation Runtimes (TCRs) can support non-ML data processing applications, so that other use cases can take advantage of the investments made on TCRs. In particular, we are interested in graph processing and relational operators, two use cases very different from ML, in high demand, and complement quite well what TCRs can do today. Building on HUMMINGBIRD, a recent platform converting traditional machine learning algorithms to tensor computations, we explore how to map selected graph processing and relational operator algorithms into tensor computations. Our vision is supported by the results: our code often outperforms custom-built C++ and CUDA kernels, while massively reducing the development effort, taking advantage of the cross-platform compilation capabilities of TCRs.