Manual creation of IT monitoring dashboard widgets is slow, error-prone, and a barrier for both novice and expert users. We present NOVAID, an interactive chatbot that leverages Large Language Models (LLMs) to generate IT monitoring widgets directly from natural language queries. Unlike general natural language–to-visualization tools, NOVAID addresses IT operations–specific challenges: specialized widget types like SLO charts, dynamic API-driven data retrieval, and complex contextual filters. The system combines a domain-aware semantic parser, fuzzy entity matching, and schema completion to produce standardized widget JSON specifications. An interactive clarification loop ensures accuracy in underspecified queries. On a curated dataset of 271 realistic queries, NOVAID achieves promising accuracy (up to 94.10% in metric extraction) across multiple LLMs. A user study with IT engineers yielded a System Usability Scale score of 74.2 for NOVAID, indicating good usability. By bridging natural language intent with operational dashboards, NOVAID demonstrates clear potential and a path for deployment in enterprise ITOps monitoring platforms.

By 2027, over 50 % of enterprises are expected to adopt industry cloud platforms [1], driving potential EBITDA value of $3 trillion by 2030 [2]. In this landscape, software providers rely on Infrastructure-as-a-Service (IaaS) providers to access tailored virtualized resources based on usage. Optimizing resource utilization is crucial to reducing operating costs and maintaining quality standards for SaaS and IaaS providers. This creates an essential need for dynamic scaling mechanisms to adjust resources according to workload variations. The Kubernetes resource Horizontal Pod Autoscaler (HPA) has limitations in scaling applications. However, AI-based algorithms, particularly Reinforcement Learning (RL), offer promising solutions. AI-based methods excel in overcoming fixed parameter constraints, handling sudden load spikes, and supporting custom parameters. We present an RL-based framework for auto scaling applications, demonstrating results from experimental evaluation.

Key-value stores are widely used as storage backends, due to their simple, yet flexible interface for cache, storage, file system, and database systems. However, when used with high performance NVMe devices, their high compute requirements for data management often leave the device bandwidth under-utilized. This leads to a performance mismatch of what the device is capable of delivering and what it actually delivers, and the gains derived from high speed NVMe devices is nullified. In this paper, we introduce KV-SSD (Key-Value SSD) as a key technology in a holistic approach to overcome such performance imbalance. KV-SSD provides better scalability and performance by simplifying the software storage stack and consolidating redundancy, thereby lowering the overall CPU usage and releasing the memory to user applications. We evaluate the performance and scalability of KV-SSDs over state-of-the-art software alternatives built for traditional block SSDs. Our results show that, unlike traditional key-value systems, the overall performance ofKV-SSD scales linearly, and delivers 1.6 to 57x gains depending on the workload characteristics.

We propose and design a new Block I/O schedulingscheme called Bulk I/O Dispatch (BID) suited for disk intensiveMapReduce applications. Large data access by such applicationsresult in a large number of block I/O requests which have thepotential to be sequentialized. However, due to contention byother applications and the way current I/O schedulers operate, the opportunities of a large sequential I/Os are missed. SequentialI/Os, which are faster than random I/Os, can lead to saving theCPU wait times and thus better application performance. The proposed scheduler is designed to work with all blockdevices which have superior sequential performance than random. Through simulation based experiments with MapReducebenchmarks we show that the proposed block I/O schedulerresults in about 27% to 52% lesser time for I/O than the currentlyavailable schedulers.