The Bare Metal Imaging (BMI) is a core component of the Massachusetts Open Cloud and a image management system(ims) that (i)provisions numerous nodes as quickly as possible while preserving support for multitenancy using Hardware as a Service (HaaS) and (ii)introduces the image management techniques that are supported by virtual machines, with little to no impact on application performance.
Motivation
Imagine thousands of nodes in a data center that supports a multitenant bare metal cloud. We need a central image management service that can quickly image the nodes to meet the customer’s needs. Upon completion of a customer’s project, the data center administrator should ideally be able to reallocate the resources within few minutes to use them for another customer. As of now, these techniques are in use for Virtual Machines (VMs), but not for bare metal systems. This project aims to bridge this gap by creating a service that can address the above mentioned issues.
Bare metal systems that support Infrastructure as a Service (IaaS) solutions are gaining traction in the cloud. Some of the advantages include:
Best isolation with respect to containers or VMs
Predictable/stable performance when compared to VMs or containers, especially on input/output (I/O) intensive workloads such as Hadoop jobs, which need predictable storage and network I/O
Leveraging benefits of cloud services, such as economics of scale. As of now, VMs are scalable and elastic, as a customer pays for his/her usage based on resource consumption.
The main concerns of a bare metal system are the inherent slowness in provisioning the nodes and the lack of features, such as cloning, snapshotting, etc. For these reasons, IaaS is typically implemented through VMs.
This project proposes a system that includes all of the above advantages and also addresses the fast provisioning issue for a bare metal system. Using this system, we propose to provision and release hundreds of nodes as quickly as possible with little impact on application performance. Current BMI (IMS) Architecture
The current design consists of a pluggable architecture that exposes an API for such features as:
map – Maps a physical node to an image
register –Registers a node with BMI
rm – Removes the image from library
list – Lists the images available
upload – Uploads the image to library
BMIS Architecture
We use Ceph as a storage back-end to save OS images. For every application we support, we have a “golden image,” which acts as a source of truth. When a user logs-in and requests a big data environment, we clone from this golden image and provision nodes using the cloned image and a PXE bootloader. Hardware as a Service (HaaS) serves as a network isolation tool through which we achieve multitenancy. HaaS provides a service for node allocation and deallocation. For more details about HaaS, please visit https://github.com/CCI-MOC/haas.
Project Team
Core Project Team
Professor Gene Cooperman, Northeastern University
Apoorve Mohan, Northeastern University
Pranay Surana, Northeastern University
Ravi Santosh Gudimetla, Northeastern University
Sourabh Bollapragada, Northeastern University
Contributors
Dr. Ata Turk, Boston University
Dr. Jason Hennesey, Boston University
Ugur Kaynar, Boston University
Sahil Tikale, Boston University
Timeline
January – April 2016: Expose an Application Programming Interface (API) for the core functionality of a bare metal imaging system, including snapshotting, cloning, etc. Performance evaluation using ceph on headnode by enabling caching and having a multi-tenant iscsi service
April – June 2016: Develop a scheduler that could be used in conjunction with bare metal imaging for dynamic node allocation and deallocation.
April – June 2016: Develop scripts for turn-key solutions for HPC, BigData and Openstack clusters.
Planning and Getting Involved
To get involved in this project, please send email to (MOC team-list) and/or join the #moc irc channel on freenode.