Continual Learning in Practice

This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine Learning. We describe the challenges and proposes a reference architecture.Comment: Presented at the NeurIPS 2018 workshop on Continual Learning https://sites.google.com/view/continual2018/hom

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Using Utility to Provision Storage Systems (CMU-PDL-07-106)

Provisioning a storage system requires balancing the costs of the solution with the benefits that the solution will provide. Previous provisioning approaches have started with a fixed set of requirements and the goal of automatically finding minimum cost solutions to meet them. Those approaches neglect the cost-benefit analysis of the purchasing decision. Purchasing a storage system involves an extensive set of trade-offs between metrics such as purchase cost, performance, reliability, availability, power, etc. Increases in one metric have consequences for others, and failing to account for these trade-offs can lead to a poor return on the storage investment. Using a collection of storage acquisition and provisioning scenarios, we show that utility functions enable this cost-benefit structure to be conveyed to an automated provisioning tool, enabling the tool to make appropriate trade-offs between different system metrics including performance, data protection, and purchase cost

Challenges in Building a Two-Tiered Learning Architecture for Disk Layout

Choosing the correct settings for large systems can be a daunting task. The performance of the system is often heavily dependent upon these settings, and the "correct" settings are often closely coupled with the workload. System designers usually resort to using a set of heuristic approaches that are known to work well in some cases. However, hand-combining these heuristics is painstaking and fragile. We propose a two-tiered architecture that makes this combination transparent and robust, and describe an application of the architecture to the problem of disk layout optimization. This two-tiered architecture consists of a set of independent heuristics, and an adaptive method of combining them. However, building such a system has proved to be more difficult than expected. Each heuristic depends heavily on decisions from other heuristics, making it difficult to break the problem into smaller pieces. This paper outlines our approaches and how they have worked, discusses the biggest challenges in building the system, and mentions additional possible solutions. Whether this problem is solvable is still open to debate, but the experiences reported provide a cautionary tale; system policy automation is complex and difficult

Challenges in Building a Two-Tiered Learning Architecture for Disk Layout (CMU-PDL-04-109)

Choosing the correct settings for large systems can be a daunting task. The performance of the system is often heavily dependent upon these settings, and the “correct” settings are often closely coupled with the workload. System designers usually resort to using a set of heuristic approaches that are known to work well in some cases. However, hand-combining these heuristics is painstaking and fragile. We propose a two-tiered architecture that makes this combination transparent and robust, and describe an application of the architecture to the problem of disk layout optimization. This two-tiered architecture consists of a set of independent heuristics, and an adaptive method of combining them. However, building such a system has proved to be more difficult than expected. Each heuristic depends heavily on decisions from other heuristics, making it difficult to break the problem into smaller pieces. This paper outlines our approaches and how they have worked, discusses the biggest challenges in building the system, and mentions additional possible solutions. Whether this problem is solvable is still open to debate, but the experiences reported provide a cautionary tale; system policy automation is complex and difficult

A reliable power management scheme for consistent hashing based distributed key value storage systems

Distributed key value storage systems are among the most important types of distributed storage systems currently deployed in data centers. Nowadays, enterprise data centers are facing growing pressure in reducing their power consumption. In this paper, we propose GreenCHT, a reliable power management scheme for consistent hashing based distributed key value storage systems. It consists of a multi-tier replication scheme, a reliable distributed log store, and a predictive power mode scheduler (PMS). Instead of randomly placing replicas of each object on a number of nodes in the consistent hash ring, we arrange the replicas of objects on nonoverlapping tiers of nodes in the ring. This allows the system to fall in various power modes by powering down subsets of servers while not violating data availability. The predictive PMS predicts workloads and adapts to load fluctuation. It cooperates with the multi-tier replication strategy to provide power proportionality for the system. To ensure that the reliability of the system is maintained when replicas are powered down, we distribute the writes to standby replicas to active servers, which ensures failure tolerance of the system. GreenCHT is implemented based on Sheepdog, a distributed key value storage system that uses consistent hashing as an underlying distributed hash table. By replaying 12 typical real workload traces collected from Microsoft, the evaluation results show that GreenCHT can provide significant power savings while maintaining a desired performance. We observe that GreenCHT can reduce power consumption by up to 35%–61%