Boat

Dalibard, Valentin; Schaarschmidt, Michael; Yoneki, Eiko

doi:10.1145/3038912.3052662

Cited by 54 publications

(7 citation statements)

References 13 publications

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“…First, the DOA paradigm should be developed to identify most efficient approaches and practices around automatic fitting of surrogate models to software components. Existing work mostly focuses on auto-tuning of system parameters [6,34] and has limited scalability potential. Thus, more case studies are needed that illustrate use of shadow emulators as monitoring and explainability tools for software, as well as suggesting scalable ways of automatically building surrogate models of systems' components.…”

Section: Systems Monitoring and Shadow Systemsmentioning

confidence: 99%

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

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With the upsurge of interest in artificial intelligence machine learning (ML) algorithms, originally developed in academic environments, are now being deployed as parts of real-life systems that deal with large amounts of heterogeneous, dynamic, and high-dimensional data. Deployment of ML methods in real life is prone to challenges across the whole system life-cycle from data management to systems deployment, monitoring, and maintenance. Data-Oriented Architecture (DOA) is an emerging software engineering paradigm that has the potential to mitigate these challenges by proposing a set of principles to create data-driven, loosely coupled, decentralised, and open systems. However DOA as a concept is not widespread yet, and there is no common understanding of how it can be realised in practice. This review addresses that problem by contextualising the principles that underpin the DOA paradigm through the ML system challenges. We explore the extent to which current architectures of ML-based real-world systems have implemented the DOA principles. We also formulate open research challenges and directions for further development of the DOA paradigm.

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Section: Systems Monitoring and Shadow Systemsmentioning

confidence: 99%

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In Theorem 1, k(•) in ( 6) denotes the kernel function, such as the squared-exponential and Matern kernels, which define the influence of a solution on the performance and confidence estimations of untested nearby solutions. Apart from that, in variance (7), K represents the covariance matrix, where…”

Section: Multimodal Bayesian Optimizationmentioning

confidence: 99%

“…The BO iteratively builds a statistical model of the objective function according to all the past evaluations and sequentially selects the next evaluation by maximizing an acquisition function. BO has shown tremendous success in a wide range of domains with no analytical formulation of objective functions, including simulation optimizations [1], device tuning/calibration [7,36], material/drug design [43,14], and many more.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Optimization Framework for Finding Local Optima in Expensive Multimodal Functions

Mei,

Lan,

Imani

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Bayesian optimization (BO) is a popular global optimization scheme for sample-efficient optimization in domains with expensive function evaluations. The existing BO techniques are capable of finding a single global optimum solution. However, finding a set of global and local optimum solutions is crucial in a wide range of real-world problems, as implementing some of the optimal solutions might not be feasible due to various practical restrictions (e.g., resource limitation, physical constraints, etc.). In such domains, if multiple solutions are known, the implementation can be quickly switched to another solution, and the best possible system performance can still be obtained. This paper develops a multimodal BO framework to effectively find a set of local/global solutions for expensive-to-evaluate multimodal objective functions. We consider the standard BO setting with Gaussian process regression representing the objective function. We analytically derive the joint distribution of the objective function and its first-order derivatives. This joint distribution is used in the body of the BO acquisition functions to search for local optima during the optimization process. We introduce variants of the well-known BO acquisition functions to the multimodal setting and demonstrate the performance of the proposed framework in locating a set of local optimum solutions using multiple optimization problems.

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“…Structured optimization. Both BOAT [12] and ProBO [27] demonstrated that adding structure to the surrogate model of the BO loop provides significant convergence speedup. BOAT allows the user to define semi-structured models: parametric models to capture trends and GP to generalize and aggregate.…”

Section: Related Workmentioning

confidence: 99%

High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB

Alabed

Yoneki

2021

Proceedings of the 1st Workshop on Machine Learning and Systems

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RocksDB is a general-purpose embedded key-value store used in multiple different settings. Its versatility comes at the cost of complex tuning configurations. This paper investigates maximizing the throughput of RocksDB IO operations by auto-tuning ten parameters of varying ranges. Off-theshelf optimizers struggle with high-dimensional problem spaces and require a large number of training samples.We propose two techniques to tackle this problem: multitask modeling and dimensionality reduction through clustering. By incorporating adjacent optimization in the model, the model converged faster and found complicated settings that other tuners could not find. This approach had an additional computational complexity overhead, which we mitigated by manually assigning parameters to each sub-goal through our knowledge of RocksDB. The model is then incorporated in a standard Bayesian Optimization loop to find parameters that maximize RocksDB's IO throughput.Our method achieved x1.3 improvement when benchmarked against a simulation of Facebook's social graph traffic, and converged in ten optimization steps compared to other state-of-the-art methods that required fifty steps.CCS Concepts: • Information systems → Key-value stores; • Computing methodologies → Gaussian processes.

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Boat

Cited by 54 publications

References 13 publications

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

A Bayesian Optimization Framework for Finding Local Optima in Expensive Multimodal Functions

High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB

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