An efficient cloud scheduler design supporting preemptible instances

Maximizing resource utilization by performing an efficient resource provisioning is a key factor for any cloud provider: commercial actors can maximize their revenues, whereas scientific and non-commercial providers can maximize their infrastructure utilization. Traditionally, batch systems have allowed data centers to fill their resources as much as possible by using backfilling and similar techniques. However, in an IaaS cloud, where virtual machines are supposed to live indefinitely, or at least as long as the user is able to pay for them, these policies are not easily implementable. In this work we present a new scheduling algorithm for IaaS providers that is able to support preemptible instances, that can be stopped by higher priority requests without introducing large modifications in the current cloud schedulers. This scheduler enables the implementation of new cloud usage and payment models that allow more efficient usage of the resources and potential new revenue sources for commercial providers. We also study the correctness and the performace overhead of the proposed scheduler agains existing solutions

Evaluating and Enabling Scalable High Performance Computing Workloads on Commercial Clouds

Performance, usability, and accessibility are critical components of high performance computing (HPC). Usability and performance are especially important to academic researchers as they generally have little time to learn a new technology and demand a certain type of performance in order to ensure the quality and quantity of their research results. We have observed that while not all workloads run well in the cloud, some workloads perform well. We have also observed that although commercial cloud adoption by industry has been growing at a rapid pace, its use by academic researchers has not grown as quickly. We aim to help close this gap and enable researchers to utilize the commercial cloud more efficiently and effectively. We present our results on architecting and benchmarking an HPC environment on Amazon Web Services (AWS) where we observe that there are particular types of applications that are and are not suited for the commercial cloud. Then, we present our results on architecting and building a provisioning and workflow management tool (PAW), where we developed an application that enables a user to launch an HPC environment in the cloud, execute a customizable workflow, and after the workflow has completed delete the HPC environment automatically. We then present our results on the scalability of PAW and the commercial cloud for compute intensive workloads by deploying a 1.1 million vCPU cluster. We then discuss our research into the feasibility of utilizing commercial cloud infrastructure to help tackle the large spikes and data-intensive characteristics of Transportation Cyberphysical Systems (TCPS) workloads. Then, we present our research in utilizing the commercial cloud for urgent HPC applications by deploying a 1.5 million vCPU cluster to process 211TB of traffic video data to be utilized by first responders during an evacuation situation. Lastly, we present the contributions and conclusions drawn from this work

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

Using Workload Prediction and Federation to Increase Cloud Utilization

The wide-spread adoption of cloud computing has changed how large-scale computing infrastructure is built and managed. Infrastructure-as-a-Service (IaaS) clouds consolidate different separate workloads onto a shared platform and provide a consistent quality of service by overprovisioning capacity. This additional capacity, however, remains idle for extended periods of time and represents a drag on system efficiency.The smaller scale of private IaaS clouds compared to public clouds exacerbates overprovisioning inefficiencies as opportunities for workload consolidation in private clouds are limited. Federation and cycle harvesting capabilities from computational grids help to improve efficiency, but to date have seen only limited adoption in the cloud due to a fundamental mismatch between the usage models of grids and clouds. Computational grids provide high throughput of queued batch jobs on a best-effort basis and enforce user priorities through dynamic job preemption, while IaaS clouds provide immediate feedback to user requests and make ahead-of-time guarantees about resource availability.We present a novel method to enable workload federation across IaaS clouds that overcomes this mismatch between grid and cloud usage models and improves system efficiency while also offering availability guarantees. We develop a new method for faster-than-realtime simulation of IaaS clouds to make predictions about system utilization and leverage this method to estimate the future availability of preemptible resources in the cloud. We then use these estimates to perform careful admission control and provide ahead-of-time bounds on the preemption probability of federated jobs executing on preemptible resources. Finally, we build an end-to-end prototype that addresses practical issues of workload federation and evaluate the prototype's efficacy using real-world traces from big data and compute-intensive production workloads

Using Unused: Non-Invasive Dynamic FaaS Infrastructure with HPC-Whisk

Modern HPC workload managers and their careful tuning contribute to the high utilization of HPC clusters. However, due to inevitable uncertainty it is impossible to completely avoid node idleness. Although such idle slots are usually too short for any HPC job, they are too long to ignore them. Function-as-a-Service (FaaS) paradigm promisingly fills this gap, and can be a good match, as typical FaaS functions last seconds, not hours. Here we show how to build a FaaS infrastructure on idle nodes in an HPC cluster in such a way that it does not affect the performance of the HPC jobs significantly. We dynamically adapt to a changing set of idle physical machines, by integrating open-source software Slurm and OpenWhisk. We designed and implemented a prototype solution that allowed us to cover up to 90\% of the idle time slots on a 50k-core cluster that runs production workloads

Towards Fast, Adaptive, and Hardware-Assisted User-Space Scheduling

Modern datacenter applications are prone to high tail latencies since their requests typically follow highly-dispersive distributions. Delivering fast interrupts is essential to reducing tail latency. Prior work has proposed both OS- and system-level solutions to reduce tail latencies for microsecond-scale workloads through better scheduling. Unfortunately, existing approaches like customized dataplane OSes, require significant OS changes, experience scalability limitations, or do not reach the full performance capabilities hardware offers. The emergence of new hardware features like UINTR exposed new opportunities to rethink the design paradigms and abstractions of traditional scheduling systems. We propose LibPreemptible, a preemptive user-level threading library that is flexible, lightweight, and adaptive. LibPreemptible was built with a set of optimizations like LibUtimer for scalability, and deadline-oriented API for flexible policies, time-quantum controller for adaptiveness. Compared to the prior state-of-the-art scheduling system Shinjuku, our system achieves significant tail latency and throughput improvements for various workloads without modifying the kernel. We also demonstrate the flexibility of LibPreemptible across scheduling policies for real applications experiencing varying load levels and characteristics.Comment: Accepted by HPCA202