A Penny a Function: Towards Cost Transparent Cloud Programming

• URL: http://arxiv.org/abs/2309.04954v1
• Date: Sun, 10 Sep 2023 08:02:12 GMT
• Title: A Penny a Function: Towards Cost Transparent Cloud Programming
• Authors: Lukas B\"ohme, Tom Beckmann, Sebastian Baltes, Robert Hirschfeld
• Abstract summary: Existing tools for understanding cost factors are often detached from source code. Existing cost models for cloud applications focus on specific factors such as compute resources. This paper presents initial work toward a cost model based on a directed graph that allows deriving monetary cost estimations directly from code.
• Score: 3.858859576352153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding and managing monetary cost factors is crucial when developing cloud applications. However, the diverse range of factors influencing costs for computation, storage, and networking in cloud applications poses a challenge for developers who want to manage and minimize costs proactively. Existing tools for understanding cost factors are often detached from source code, causing opaqueness regarding the origin of costs. Moreover, existing cost models for cloud applications focus on specific factors such as compute resources and necessitate manual effort to create the models. This paper presents initial work toward a cost model based on a directed graph that allows deriving monetary cost estimations directly from code using static analysis. Leveraging the cost model, we explore visualizations embedded in a code editor that display costs close to the code causing them. This makes cost exploration an integrated part of the developer experience, thereby removing the overhead of external tooling for cost estimation of cloud applications at development time.

Related papers

• Understanding Cost Dynamics of Serverless Computing: An Empirical Study [1.2905826135573395]
  This study delves into how organizations anticipate the costs of adopting serverless. It also aims to com-prehend workload suitability and identify best practices for cost optimization of serverless applications.
  arXiv  Detail & Related papers  (2023-11-22T09:01:23Z)
• URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud [3.9704849108478704]
  We propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell on cloud resource usage. Results show that URegM is capable of accurately predicting resource consumption due to code smell.
  arXiv  Detail & Related papers  (2023-10-22T23:03:35Z)
• ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning [95.64041188351393]
  This paper endeavors to solve both the challenges of limited resources and personalization. We propose a method named ZOOPFL that uses Zeroth-Order Optimization for Personalized Federated Learning. To reduce the computation costs and enhance personalization, we propose input surgery to incorporate an auto-encoder with low-dimensional and client-specific embeddings.
  arXiv  Detail & Related papers  (2023-10-08T12:26:13Z)
• Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge [0.0]
  This paper proposes a transfer learning based approach to address this issue. A ML model is trained on a large amount of computationally inexpensive data from a physics-based process model (source) and then fine-tuned on a smaller amount of costly experimental data (target) Despite extreme functional and quantitative inaccuracies in the source our approach reduces the model development cost by years, experimental cost by 56-76%, computational cost by orders of magnitude, and prediction error by 16-24%.
  arXiv  Detail & Related papers  (2023-04-29T10:54:57Z)
• Scaling ML Products At Startups: A Practitioner's Guide [0.0]
  We break down costs into variable costs-the cost of serving the model and performant-and fixed costs-the cost of developing and training new models. We propose a framework for conceptualizing these costs, breaking them into finer categories, and limn ways to reduce costs.
  arXiv  Detail & Related papers  (2023-04-20T22:02:42Z)
• Mining for Cost Awareness in the Infrastructure as Code Artifacts of Cloud-based Applications: an Exploratory Study [1.5612101323427952]
  This study aims to provide empirical evidence of cost awareness by mining open source repositories of cloud-based applications. A systematic search through 152,735 repositories resulted in the selection of 2,010 relevant ones. The findings indicate that developers are not only concerned with the cost of their application deployments but also take actions to reduce these costs.
  arXiv  Detail & Related papers  (2023-04-15T10:49:35Z)
• Optimizing Data Collection for Machine Learning [87.37252958806856]
  Modern deep learning systems require huge data sets to achieve impressive performance. Over-collecting data incurs unnecessary present costs, while under-collecting may incur future costs and delay. We propose a new paradigm for modeling the data collection as a formal optimal data collection problem.
  arXiv  Detail & Related papers  (2022-10-03T21:19:05Z)
• How Much More Data Do I Need? Estimating Requirements for Downstream Tasks [99.44608160188905]
  Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? Overestimating or underestimating data requirements incurs substantial costs that could be avoided with an adequate budget. Using our guidelines, practitioners can accurately estimate data requirements of machine learning systems to gain savings in both development time and data acquisition costs.
  arXiv  Detail & Related papers  (2022-07-04T21:16:05Z)
• Kubric: A scalable dataset generator [73.78485189435729]
  Kubric is a Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation.
  arXiv  Detail & Related papers  (2022-03-07T18:13:59Z)
• Budget Learning via Bracketing [50.085728094234476]
  The budget learning problem poses the learner's goal as minimising use of the cloud while suffering no discernible loss in accuracy. We propose a new formulation for the BL problem via the concept of bracketings. We empirically validate our theory on real-world datasets, demonstrating improved performance over prior gating based methods.
  arXiv  Detail & Related papers  (2020-04-14T04:38:14Z)
• Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning [100.73223416589596]
  We propose a cost-sensitive portfolio selection method with deep reinforcement learning. Specifically, a novel two-stream portfolio policy network is devised to extract both price series patterns and asset correlations. A new cost-sensitive reward function is developed to maximize the accumulated return and constrain both costs via reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-06T06:28:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.