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.

Table Question Answering (TQA) presents a substantial challenge at the intersection of natural language processing and data analytics. This task involves answering natural language (NL) questions on top of tabular data, demanding proficiency in logical reasoning, understanding of data semantics, and fundamental analytical capabilities. Due to its significance, a substantial volume of research has been dedicated to exploring a wide range of strategies aimed at tackling this challenge including approaches that leverage Large Language Models (LLMs) through in-context learning or Chain-of-Thought (CoT) prompting as well as approaches that train and fine-tune custom models. Nonetheless, a conspicuous gap exists in the research landscape, where there is limited exploration of how innovative foundational research, which integrates incremental reasoning with external tools in the context of LLMs, as exemplified by the ReAct paradigm, could potentially bring advantages to the TQA task. In this paper, we aim to fill this gap, by introducing ReAcTable ( ReAct for Table Question Answering tasks), a framework inspired by the ReAct paradigm that is carefully enhanced to address the challenges uniquely appearing in TQA tasks such as interpreting complex data semantics, dealing with errors generated by inconsistent data and generating intricate data transformations. ReAcTable relies on external tools such as SQL and Python code executors, to progressively enhance the data by generating intermediate data representations, ultimately transforming it into a more accessible format for answering the user's questions with greater ease. Through extensive empirical evaluations using three popular TQA benchmarks, we demonstrate that ReAcTable achieves remarkable performance even when compared to fine-tuned approaches. In particular, it outperforms the best prior result on the WikiTQ benchmark by 2.1%, achieving an accuracy of 68.0% without requiring training a new model or fine-tuning.

Schema matching over relational data has been studied for more than two decades. However, the state-of-the-art methods do not address key modern-day challenges encountered in real customer scenarios, namely: 1) no access to the source (customer) data due to privacy constraints, 2) target schema with a much larger number of entities and attributes compared to the source schema, and 3) different but semantically equivalent entity and attribute names in the source and target schemata. In this paper, we address these shortcomings. Using real-world customer schemata, we demonstrate that existing linguistic matching approaches have low accuracy. Next, we propose the Learned Schema Mapper (LSM), a novel linguistic schema matching system that leverages the natural language understanding capabilities of pre-trained language models to improve the overall accuracy. Combining this with active learning and a smart attribute selection strategy that selects the most informative attributes for users to label, LSM can significantly reduce the overall human labeling cost. Experimental results demonstrate that users can correctly match their full schema while saving as much as 81% of the labeling cost compared to manual labeling.

Selecting the optimal cloud target to migrate SQL estates from on-premises to the cloud remains a challenge. Current solutions are not only time-consuming and error-prone, requiring significant user input, but also fail to provide appropriate recommendations. We present Doppler, a scalable recommendation engine that provides right-sized Azure SQL Platform-as-a-Service (PaaS) recommendations without requiring access to sensitive customer data and queries. Doppler introduces a novel price-performance methodology that allows customers to get a personalized rank of relevant cloud targets solely based on low-level resource statistics, such as latency and memory usage. Doppler supplements this rank with internal knowledge of Azure customer behavior to help guide new migration customers towards one optimal target. Experimental results over a 9-month period from prospective and existing customers indicate that Doppler can identify optimal targets and adapt to changes in customer workloads. It has also found cost-saving opportunities among over-provisioned cloud customers, without compromising on capacity or other requirements. Doppler has been integrated and released in the Azure Data Migration Assistant v5.5, which receives hundreds of assessment requests daily.

We demonstrate Pipemizer , an optimizer and recommender aimed at improving the performance of queries or jobs in pipelines. These job pipelines are ubiquitous in modern data analytics due to jobs reading output files written by other jobs. Given that more than 650k jobs run on Microsoft's SCOPE job service per day and about 70% have inter-job dependencies, identifying optimization opportunities across query jobs is of considerable interest to both cluster operators and users. Pipemizer addresses this need by providing recommendations to users, allowing users to understand their system, and facilitating automated application of recommendations. Pipemizer introduces novel optimizations that include holistic pipeline-aware statistics generation, inter-job operator push-up, and job split & merge. This demonstration showcases optimizations and recommendations generated by Pipemizer , enabling users to understand and optimize job pipelines.