Training-as-a-service platforms facilitate users to deploy pre-configured Generative AI training jobs as batch workloads. The immutability of configuration offers minimal flexibility to dynamically adapt to training progress. Existing approaches invariably involve manually monitoring training progress on a dashboard, and the stop-reconfigure-restart of training does not scale well with number of experiments. Relying on pre-configuration, wastes computational resources and makes debugging of training jobs difficult. We address this gap through our training-control-as-code paradigm, which allows users to run user-defined code to analyze the training state and intervene to flag anomalies and save resource wastage. Our framework TrAC offers a declarative interface to allow for declaring desired control and for reusing it at scale. Using real-world open-source data and models we provide estimates on the savings in time and resource due to TrAC. We also provide demo video: https://youtu.be/RmhBfFjd1oA and code: https://github.com/foundation-model-stack/fms-hf-tuning/blob/main/examples/trainercontroller_configs/Readme.md

Sameer Pimparkhede, Mehant Kammakomati, Srikanth G. Tamilselvam, Prince Kumar, Ashok Pon Kumar, Pushpak Bhattacharyya. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

We present Move2Kube, a replatforming framework that automates the creation and transformation of DevOps artifacts of an application for deployment in a Cloud Native environment. Our contributions include a customizable transformer framework that allows for complete control over the artifacts being processed, and output generated. We provide case studies and open-source benchmark-based evidence comparing Move2Kube with similar state-of-the-art tools to demonstrate its effectiveness in terms of effort reduction, diverse utility, and highlight future lines of work. Move2Kube is being developed as an open-source community project and it is available at: https://move2kube.konveyor.io/

Cluster rightsizing facilitates cost-performance trade-off in resource-constrained clouds. Multidimensional bin-packing algorithms can address this rightsizing problem, but these assume that every task on the cluster is always active. In contrast, real-world tasks may be active only during specific time-periods, which allows reusing resources via time sharing and optimal packing. This motivates our generalized problem of rightsizing for time-limited tasks: given a timeline, time-periods and resource demands for tasks, the objective is to place the tasks on a minimum cost cluster of nodes without violating node capacities at any time instance. We design a baseline two-phase algorithm that performs penalty-based mapping of task to node-type and then, solves each node-type independently. We prove that the algorithm has an approximation ratio of O(D. min(m, T)), where D, m and$T$are the number of resources, node-types and timeslots, respectively, We then present an improved linear programming based mapping strategy, enhanced further with a cross-node-type filling mechanism. Our experiments on synthetic and real-world cluster traces show significant cost reduction by LP-based mapping compared to the baseline, and the filling mechanism improves further to produce solutions within 20% of (a lower-bound to) the optimal solution.

We present Move2Kube, a replatforming framework that automates the transformation of the deployment specification and development pipeline of an application from a non-Kubernetes platform to a Kubernetes-based one, minimizing changes to the application's functional implementation and architecture. Our contributions include: (1) a standardized intermediate representation to which diverse application deployment artifacts could be translated, (2) an extension framework for adding support for new source platforms, and target artifacts while allowing customization as per organizational standards. We provide initial evidence of its effectiveness in terms of effort reduction, and highlight the current research challenges and future lines of work. Move2Kube is being developed as an open source community project and it is available at https://move2kube.konveyor.io/

A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns. SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage.

Efficiency is critical to the profitability of software maintenance and support organizations. Managing such organizations effectively requires suitable measures of efficiency that are sensitive enough to detect significant changes, and accurate and timely in detecting them. Mean time to close problem reports is the most commonly used efficiency measure, but its suitability has not been evaluated carefully. We performed such an evaluation by mining and analyzing many years of support data on multiple IBM products. Our preliminary results suggest that the mean is less sensitive and accurate than another measure, percentiles, in cases that are particularly important in the maintenance and support domain. Using percentiles, we also identified statistical techniques to detect efficiency trends and evaluated their accuracy. Although preliminary, these results may have significant ramifications for effectively measuring and improving software maintenance and support processes.