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Autopilot: workload autoscaling at google
In many public and private Cloud systems, users need to specify a limit for the amount of
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …
Predictive performance modeling for distributed batch processing using black box monitoring and machine learning
In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …
growing complexity of data analyses, creating new demands for batch processing on …
Pocket: Elastic ephemeral storage for serverless analytics
Serverless computing is becoming increasingly popular, enabling users to quickly launch
thousands of shortlived tasks in the cloud with high elasticity and fine-grain billing. These …
thousands of shortlived tasks in the cloud with high elasticity and fine-grain billing. These …
Performance and cost-efficient spark job scheduling based on deep reinforcement learning in cloud computing environments
Big data frameworks such as Spark and Hadoop are widely adopted to run analytics jobs in
both research and industry. Cloud offers affordable compute resources which are easier to …
both research and industry. Cloud offers affordable compute resources which are easier to …
Llama: A heterogeneous & serverless framework for auto-tuning video analytics pipelines
The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …
diverse set of video analytics applications. These applications rely on video pipelines …
AlloX: Compute allocation in hybrid clusters
Modern deep learning frameworks support a variety of hardware, including CPU, GPU, and
other accelerators, to perform computation. In this paper, we study how to schedule jobs …
other accelerators, to perform computation. In this paper, we study how to schedule jobs …
Taming performance variability
The performance of compute hardware varies: software run repeatedly on the same server
(or a different server with supposedly identical parts) can produce performance results that …
(or a different server with supposedly identical parts) can produce performance results that …
Finding Faster Configurations Using FLASH
Finding good configurations of a software system is often challenging since the number of
configuration options can be large. Software engineers often make poor choices about …
configuration options can be large. Software engineers often make poor choices about …
Smartharvest: Harvesting idle cpus safely and efficiently in the cloud
We can increase the efficiency of public cloud datacenters by harvesting allocated but
temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics …
temporarily idling CPU cores from customer virtual machines (VMs) to run batch or analytics …
Morphling: Fast, near-optimal auto-configuration for cloud-native model serving
Machine learning models are widely deployed in production cloud to provide online
inference services. Efficiently deploying inference services requires careful tuning of …
inference services. Efficiently deploying inference services requires careful tuning of …