Verbatim feedback constitutes a valuable repository of user experiences, opinions, and requirements, crucial for data engineering and software development. Extracting meaningful insights from large-scale feedback data presents a significant challenge. This paper introduces Allhands, an innovative ana-lytic framework that transforms traditional large-scale feedback analysis tasks through a natural language interface, leveraging large language models (LLMs). Allhands performs initial classification and topic modeling on feedback to convert it into a structurally augmented format, enhancing accuracy, robustness and generalization with the aid of LLMs. Subsequently, an LLM-based code-first agent interprets users' diverse natural language questions about the feedback, automatically translates them into executable call of analytic tools or code, and delivers comprehensive multi-modal responses, including text, code, tables, and images. This eliminates the need for developing individual feedback analytic tools for each request, reducing human effort and making the system more accessible and flexible to users. We evaluate Allhands across three diverse feedback datasets, demonstrating its superior efficacy in all stages of analysis, from classification and topic modeling to providing an “ask me anything” experience with comprehensive, accurate, and human-readable responses. To the best of our knowl-edge, Allhands is the first comprehensive feedback analysis framework supporting diverse and customized insight extraction requirements through a natural language interface.

Time series anomaly detection (TSAD) plays a crucial role in various industrial applications. Traditional deep learning TSAD models require extensive training data and operate as black boxes, lacking interpretability for detected anomalies. To address these challenges, we propose LLMAD, a novel TSAD method that employs Large Language Models (LLMs) to deliver accurate and interpretable TSAD results. LLMAD applies in-context anomaly detection by retrieving both positive and negative similar time series segments, significantly enhancing LLMs' effectiveness. Furthermore, LLMAD employs the Anomaly Detection Chain-of-Thought approach to mimic expert logic for its decision-making process. This further enhances its performance and enables LLMAD to provide explanations for their detections through versatile perspectives.

We address the real problem of safe, robust, adaptive resource oversubscription in uncertain environments with our proposed novel technique of chance-constrained imitation learning. Our objective is to enhance resource efficiency while ensuring safety against congestion risk. Traditional supervised or forecasting models are ineffective in learning adaptive oversubscription policies, and conventional online optimization or reinforcement learning is difficult to deploy on real systems. Offline policy learning methods, such as Imitation Learning (IL) can leverage historical resource utilization telemetry data to learn effective policies if we can ensure robustness and safety from the underlying uncertainty in the domain, and thus the data. Our work investigates the nature of this uncertainty, how it can be quantified and proposes a novel chance-constrained IL that implicitly models such uncertainty in a principled manner via additional knowledge in the form of stochastic constraints on the associated risk, to learn provably safe and robust policies. We show empirically a substantial improvement (~ 3-4×) in capacity efficiency and congestion safety in test as well as real deployments.

Scoring systems are commonly seen for platforms in the era of Big Data. From credit scoring systems in financial services to membership scores in E-commerce shopping platforms, platform managers use such systems to guide users towards the encouraged activity pattern, and manage resources more effectively and efficiently. To establish such scoring systems, several “empirical criteria” are first determined, followed by a dedicated top-down design for each score factor, which usually requires enormous effort to adjust and tune the scoring function in the new application scenario. What's worse, many fresh projects usually have no ground truth or any experience to evaluate a reasonable scoring system, making the designing even harder. To reduce the effort of manual adjustment of the scoring function in every new scoring system, we innovatively study the scoring system from the preset empirical criteria without any ground truth and propose a novel framework to improve the system from scratch. In this paper, we propose a “counter-empirical attacking” mechanism that can generate “attacking” behavior traces and try to break the empirical rules of the scoring system. Then an adversarial “enhancer” is applied to evaluate the scoring system and find the improvement strategy. By training the adversarial learning problem, a proper scoring function can be learned to be robust to the attacking activity traces that are trying to violate the empirical criteria. Extensive experiments have been conducted on two scoring systems, including a shared computing resource platform and a financial credit system. The experimental results have validated the effectiveness of our proposed framework.

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and proposes an effective Chance-Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization () under different levels of safety constraints.

This paper investigates a critical resource allocation problem in the first party cloud: scheduling containers to machines. There are tens of services, and each service runs a set of homogeneous containers with dynamic resource usage; containers of a service are scheduled daily in a batch fashion. This problem can be naturally formulated as Stochastic Bin Packing Problem (SBPP). However, traditional SBPP research often focuses on cases of empty machines, whose objective, i.e., to minimize the number of used machines, is not well-defined for the more common reality with nonempty machines. This paper aims to close this gap. First, we define a new objective metric, Used Capacity at Confidence (UCaC), which measures the maximum used resources at a probability and is proved to be consistent for both empty and nonempty machines and reformulate the SBPP under chance constraints. Second, by modeling the container resource usage distribution in a generative approach, we reveal that UCaC can be approximated with Gaussian, which is verified by trace data of real-world applications. Third, we propose an exact solver by solving the equivalent cutting stock variant as well as two heuristics-based solvers -- UCaC best fit, bi-level heuristics. We experimentally evaluate these solvers on both synthetic datasets and real application traces, demonstrating our methodology's advantage over traditional SBPP optimal solver minimizing the number of used machines, with a low rate of resource violations.

A typical cloud system has a large amount of telemetry data collected by pervasive software monitors that keep tracking the health status of the system. The telemetry data is essentially multi-dimensional data, which contains attributes and failure/success status of the system being monitored. By identifying the attribute value combinations where the failures are mostly concentrated (which we call fault-indicating combination), we can localize the cause of system failures into a smaller scope, thus facilitating fault diagnosis. However, due to the combinatorial explosion problem and the latent hierarchical structure in cloud telemetry data, it is still intractable to localize the fault to a proper granularity in an efficient way. In this paper, we propose HALO, a hierarchy-aware fault localization approach for locating the fault-indicating combinations from telemetry data. Our approach automatically learns the hierarchical relationship among attributes and leverages the hierarchy structure for precise and efficient fault localization. We have evaluated HALO on both industrial and synthetic datasets and the results confirm that HALO outperforms the existing methods. Furthermore, we have successfully deployed HALO to different services in Microsoft Azure and Microsoft 365, witnessed its impact in real-world practice.