The dynamic nature of cloud spending and pricing structures pose challenges for practitioners in IT Financial Operations (FinOps). Recent advances in agentic systems enables them to instead rely on agents for complex FinOps tasks such as drawing insights from their data through natural language queries. In this work, we present an IT FinOps Data Insights Agent, that implements “chat with your data” approach to support practitioners in their daily tasks. Our agent achieves up to 90% accuracy across ITBench FinOps scenarios.

Coordination services and protocols are critical components of distributed systems and are essential for providing consistency, fault tolerance, and scalability. However, due to the lack of standard benchmarking and evaluation tools for distributed coordination services, coordination service developers/researchers either use a NoSQL standard benchmark and omit evaluating consistency, distribution, and fault tolerance; or create their own ad-hoc microbenchmarks and skip comparability with other services. In this study, we analyze and compare the evaluation mechanisms for known and widely used consensus algorithms, distributed coordination services, and distributed applications built on top of these services. We identify the most important requirements of distributed coordination service benchmarking, such as the metrics and parameters for the evaluation of the performance, scalability, availability, and consistency of these systems. Finally, we discuss why the existing benchmarks fail to address the complex requirements of distributed coordination system evaluation.

Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release targets three key areas: Site Reliability Engineering (SRE), Compliance and Security Operations (CISO), and Financial Operations (FinOps). The design enables AI researchers to understand the challenges and opportunities of AI agents for IT automation with push-button workflows and interpretable metrics. IT-Bench includes an initial set of 102 real-world scenarios, which can be easily extended by community contributions. Our results show that agents powered by state-of-the-art models resolve only 11.4% of SRE scenarios, 25.2% of CISO scenarios, and 25.8% of FinOps scenarios (excluding anomaly detection). For FinOps-specific anomaly detection (AD) scenarios, AI agents achieve an F1 score of 0.35. We expect ITBench to be a key enabler of AI-driven IT automation that is correct, safe, and fast. IT-Bench, along with a leaderboard and sample agent implementations, is available at https://github.com/ibm/itbench.

In the context of cloud computing, enterprises are increasingly adopting multi-cloud strategies to enhance performance, ensure cost efficiency, and avoid vendor lock-in. This trend presents a significant challenge for the migration of AI for IT operations (AIOps) models across different cloud providers due to variations in architecture, performance, and data distribution. Traditional methods of re-training AIOps models for new cloud environments are labor-intensive and delay deployment. To address this issue, we introduce a novel framework called SAM (Subseries Augmentation-based Meta-learning), which facilitates seamless model migration between clouds without the need for re-training from scratch. SAM leverages data augmentation and meta-learning to efficiently adapt AIOps models to new cloud environments. It has proven effective in adapting anomaly detectors across various config-urations over both public and simulated datasets. We believe that SAM can also be adapted to other AI models used for automating IT tasks such as alerting and resource scaling.

Adaptive bitrate (ABR) algorithms play a critical role in video streaming by making optimal bitrate decisions in dynamically changing network conditions to provide a high quality of experience (QoE) for users. However, most existing ABRs suffer from limitations such as predefined rules and incorrect assumptions about streaming parameters. They often prioritize higher bitrates and ignore the corresponding energy footprint, resulting in increased energy consumption, especially for mobile device users. Additionally, most ABR algorithms do not consider perceived quality, leading to suboptimal user experience. This article proposes a novel ABR scheme called GreenABR+, which utilizes deep reinforcement learning to optimize energy consumption during video streaming while maintaining high user QoE. Unlike existing rule-based ABR algorithms, GreenABR+ makes no assumptions about video settings or the streaming environment. GreenABR+ model works on different video representation sets and can adapt to dynamically changing conditions in a wide range of network scenarios. Our experiments demonstrate that GreenABR+ outperforms state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 57% in data consumption while providing up to 25% more perceptual QoE due to up to 87% less rebuffering time and near-zero capacity violations. The generalization and dynamic adaptability make GreenABR+ a flexible solution for energy-efficient ABR optimization.

Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video streaming. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming parameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR's deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations.

Traditional coordination services for distributed applications do not scale well over wide-area networks (WAN): centralized coordination fails to scale with respect to the increasing distances in the WAN, and distributed coordination fails to scale with respect to the number of nodes involved. We argue that it is possible to achieve scalability over WAN using a hierarchical coordination architecture and a smart token migration mechanism, and lay down the foundation of a novel design for a flexible-consistent coordination framework, called WanKeeper. We implemented WanKeeper based on the ZooKeeper API and deployed it over WAN as a proof of concept. Our experimental results based on the Yahoo! Cloud Serving Benchmark (YCSB), Apache BookKeeper replicated log service, and the Shared Cloud-backed File System (SCFS) show that WanKeeper provides multiple folds improvement in write/update performance in WAN compared to ZooKeeper, while keeping the same read performance.