SELF-ORGANISING, SELF-MANAGING HETEROGENEOUS CLOUD

OVERVIEW

1. Funding and Challenge

2. Consortium

3. Current IaaS Cloud Usage

4. Objectives

5. Approach

6. CloudLightning Architecture

7. HPC Use Cases

8. Challenges Ahead

HORIZON2020

Horizon 2020 is the biggest EU Research and Innovation

programme ever with nearly €80 billion of funding available over 7 years (2014 to 2020) – in addition to the private investment that this

money will attract.

It promises more breakthroughs, discoveries and world-firsts by taking

great ideas from the lab to the market.

The principal goals:

• world-class science• removal of barriers to innovation• enable public and private sectors to work together

The CloudLightning project was funded under Call H2020-ICT-2014-1Advanced Cloud Infrastructures and Services High performanceheterogeneous cloud infrastructures and runs from Feb 2014 - January 2017

SPECIFICCHALLENGE

The aim is to develop infrastructures, methods

and tools for high performance, adaptive cloud applications and

services that go beyond the current capabilities.

Cloud computing is being transformed by new requirements such as

• heterogeneity of resources and devices,• software-defined data centres,• cloud networking, security, and • the rising demands for better quality of user experience.

Cloud computing research will be oriented towards

• new computational and data management models (at both infrastructure and services levels) that respond to the advent of faster and more efficient machines,

• rising heterogeneity of access modes and devices, • demand for low energy solutions, • widespread use of big data, • federated clouds and • secure multi-actor environments including public administrations.

CONSORTIUM

CloudLightning comprises of eight partners from academia

and industry and is coordinated by University

College Cork.

CURRENT IAAS CLOUD USAGE

Consider the two actors: the customer, looking to

build a solution on provider’s infrastructure,

and the cloud service provider

Customer:

• Hard work • Research various offerings

and build/compile solutions accordingly.

• Sub-optimal• Create a generic solution

to facilitate portability• Opt for provider specific

offering and risk vendor lock-in

Provider:

• Relinquishes control• Over resource utilization

and power management• The cloud is now

approaching 10% of the world’s electricity consumption!

• Offers Resources in limited, discrete sizes

• Precipitates over-provisions and exacerbates waste

PROJECT OBJECTIVES

Customer Level Objectives

• Make cloud computing more accessible

• Make cloud computing more efficient

• Move towards “ease of everything”

Provider level objectives

• Re-establish control over their IaaSofferings

• Facilitate better power management

• Enable fast resource provisioning for quicker service initiation

• Enable seamless exploitation of heterogeneous hardware

• Exploit faster and cheaper service delivery offered by hardware accelerators

• Employ different heterogeneous hardware types for different services or for different invocations of the same service

Project level objectives

• Demonstrate our approach in a very challenging HPC application domain

• Construct small-scale test-bed

• Construct Large-scale simulation

OUR APPROACH

Separate the concerns of the customer and the

provider

1. Create a Service Orient Architecture for the Heterogeneous Cloud

2. Customer focuses on service requirements, workflows and SLAs rather than resources

3. Provider focuses on efficient resource management and service delivery

SERVICE ORIENTED

ARCHITECTURE

This approach moves the management burden

from the customer to the provider.

The resulting complexity for the

provider is very high.

Creator forms the work-flow and stores the Blueprint in the Blueprint Catalogue; the Operator selects a Blueprint from the Blueprint Catalogue and optionally edits its constraints and parameters.

The Operator launches the Blueprint by:(1) requesting an appropriate solution from the CL and(2) deploying the Blueprint on the resources returned as part of that solution.

The End User then interacts with the deployed Blueprint.

SELF-ORGANISATIONAND

SELF-MANAGEMENT

Moving from a distributed customer-based IaaS

Management to centralised provider-based IaaS

management introduces enormous complexity.

This complexity can be addressed by self-organization

and self-management.

Basic tenets:• component autonomy• awareness of the environment• goal-driven behaviour of individual components• self-configuration

Goals include:• minimize energy consumption• Improve service delivery

Goals achieved by collaboration.

Coalitions of resources, working in concert to respond to the needs of a specific service request rather than offering a menu of a limited number of resource packages.

CONCEPTUAL ARCHITECTURE A Cell is a collection of resources.

There may be multiple CellsA Cell Manager is associated witheach CellThe Cell resources are grouped into anumber virtual racks, called vRacks.A vRack Manager is associated witheach vRack. It is self-managed,identifying and creating coalitions, todeliver on specific service requests.vRack Managers cooperate to formvRack Manager Groups.vRacks Managers in the same Groupself-organize to meet specificobjectives. They are aware ofchanges in the environment includingnew and disappearing resources andadapt, on a negotiated basis, withother vRacks Managers within thesame vRack Manager Group to meetsystem objectives.

ARCHITECTURE COMPONENTS

CLOUDLIGHTNING RESOURCES • In a heterogeneous cloud, there will be many different types of

compute resources.• These resources may be available individually or they may be

bundled into subsystems. • Individual resources and subsystems may have pre-installed

software stacks• They may be physically located on interconnects with different

characteristics• A CL-Resource is a generic term used to refer to any of the

above• CL-Resources can thus be bare metal; virtual machines;

containers; networked commodity or specialized hardware, servers with accelerators such as GPUs, MICs and FPGAs; pre-built HPC environments

• In response to a service request, the CL system identifies specific CL-Resources to be used for the delivery of that service.

• Workflows of services may be implemented on a mixture of CL-Resources – one resource type per service

RESOURCE COALITION

A collection of CL-Resources used to execute a service is called aCoalition. A coalition may be composed of one or more CL-Resources of the same type.Multi CL-Resource coalitions support multi-process services.Coalitions are formed by a vRack Manager in response to specific service requirements. Coalitions may be persisted to eliminate delays in CL-Resource creation and so to improve service deliveryDynamic Coalition formation

respects SLA requirements minimizes provider overheadsmaximizes resource utilization

The constituent CL-Resources of a Coalition

may span multiple servers within a single

vRack.

VRACK MANAGER TYPES AND

GROUPS

vRack Managers are typed to • reflect differences in the CL-Resources under their control• constrain how vRack Manager Groups are formed and self-

organized• leverage resource specific optimization opportunities

resulting from grouping vRack Managers together

PLUG AND PLAY

LEVERAGING EXISTING

OPENSTACK COMPONENTS

BENEFICIARIES

The primary beneficiary is the Infrastructure-as-a-Service provider. They benefit from activating

the HPC in the cloud market and a reduction in

cost related to better performance per cost and

performance per watt.

This increased energy efficiency can result in

lower costs throughout the cloud ecosystem and

can increase the accessibility and

performance in a wide range of use cases

including Oil and Gas discovery, Genomics and

Ray Tracing (e.g. 3D Image Rendering)

• Improved physics simulations and higher resolution RTM imaging.

• Energy and cost efficient scalable solution for RTM and OPM/DUNE simulations.

• Reduced risk and costs of dry exploratory wells.

• Improved performance/cost and performance/Watt.

• Faster speed of genome sequence computation.

• Reduced development times.

• Increased volume and quality of related research.

• Reduced CAPEX and IT associated costs.

• Extra capacity for overflow (“surge”) workloads.

• Faster workload processing to meet project timelines.

Ray Tracing (3D Image Rendering)

GenomicsOil and Gas

IN CONCLUSION The Challenges Ahead

• Separate the concerns of the IaaS consumer and the CSP

• Create a Service Oriented Architecture for the emerging heterogeneous cloud

• Reduce energy consumption by improved IaaS management

• Improve service delivery

• Leverage heterogeneity

• Bring HPC to the cloud

• Resource management in hyper-scale cloud deployments

THANK YOUTHANK YOUwww.cloudlightning.eu