OPTIMIS – TOWARDS HOLISTIC CLOUD MANAGEMENT 2011-09-20 Johan Tordsson, Department of Computing Science & HPC2N, Umeå Universitet.

OPTIMIS – TOWARDS HOLISTIC CLOUD MANAGEMENT

2011-09-20

Johan Tordsson,

Department of Computing Science & HPC2N,

Umeå Universitet

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OPTIMIS: BACKGROUND AND MOTIVATION

What? IP, Call 5, 10.4 M€ budget, 13 partners (8

academic) www.optimis-project.eu

Why? Multiple cloud models, definitions, etc. Our view:

Private clouds are common practice within the next few years Additional resources to handle load peaks etc. are provided by

public cloud(s) No one-size-fits-all solution to cloud provisioning Need for common abstractions, tools, and methods for various

scenarios

Roles & Challenges

New challenges– New customers– New business models– New collaboration forms– New requirements

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FIVE CONCERNS FOR FUTURE CLOUDS

1. Dependable sociability Management based on non-functional aspects Foundation for eco-system of providers and

consumers of cloud services

2. Many cloud architectures Private, bursted, federated clouds, etc.

3. Service life cycle management optimization

Construction, deployment, operation

4. Adaptive self-preservation Self-* management with respect to functional

and non-functional aspects

5. Market and legal issues Identify business opportunities and legislative

concerns

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1. DEPENDABLE SOCIABILITY Beyond cost-performance tradeoffs Tools for measuring & prediction of

TREC: Trust

Reputation-assessment of actors (SPs, IPs, etc.) Transitivity aspects

Risk Probability of something (bad) happening … … and the consequences Identification, assessment, monitoring, treatment

Eco-efficiency Monitor and predict power, PUE, CO2, etc. Compliance to standards and legislations

Cost Need for economical models beyond list prices Required to balance the above 3 factors

Multi-clouds

Federated Clouds

Infrastructure Provider

Bursted Private Clouds

2. MULTI-CLOUDS: THREE BASIC SCENARIOS

Infrastructure Provider

Infrastructure Provider

Infrastructure Provider

Infrastructure Provider

Service Provider

Broker

Infrastructure Provider

Service Provider

Service Provider

Infrastructure Provider

Private infra-

structure

Cloud providers Eco-System

– Programming Model

– Services Composition

[Legacy & New]

Construction

– Self-management– Risk Evaluation– Eco-efficiency–Data Management

Internal Cloud Operation

Optimization

Plus: – Multi-clouds– Federated clouds– License Management– Eco-efficiency Evaluation– Security

External Cloud Operation

Optimization

– Risk Assessment– Trust Circle– Eco-efficiency Evaluation

– Economic factor

Deployment Optimization

3. SERVICE LIFE CYCLE

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4. ADAPTIVE SELF-PRESERVATION

Clouds are complex and environments change rapidly

We needAutomatic self-* management of infrastructure

self-configuration self-healing self-optimization

Holistic view Cannot do management of services, VMs, data, etc. in isolation

Self-management based also on non-functional aspects Trust, risk, eco-efficiency, and cost

Policy-driven management Adaptable and replacable policies

5. MARKET AND LEGAL ISSUES

Cloud eco-system new and currently evolving Opportunities for new roles, business models,

relationships, value chains, etc.

Legal concernsAcquisition, location, and transfer of dataAcross borders and legal domainsData protection and security mechanisms

needed (CS) Research problem

How to design mechanisms to be used to implement currently not known policies?

OUR APPROACH – THE OPTIMIS TOOLKIT

Addresses the five challengesGeneric toolset to support multiple cloud

architecturesReusable and configurable components Incorporates TREC-management and self-*

abilitiesSupports full service life cycleData protection capabilities

OPTIMIS SYSTEM MODEL What is a service (in OPTIMIS)?

Any functionality offered to clients over a network

Delivered through one or more VMsElastic

#VMs change dynamically during operationDefined by SP in a service manifest

VM images (OVF) SLAs w.r.t. elasticity (service-specific KPIs) Tresholds with acceptable levels of trust, risk,

eco-efficiency, and costDeployed by SP in IP(s)Operated by IP(s)

OPTIMIS TOOLKIT OVERVIEW

Four main groups of components Basic Toolkit SP tools IP tools Tools usable by both SPs and IPs

BASIC TOOLKIT Monitoring

Core functionality for self-managed systems 3 levels

Services Virtual infrastructure Physical infrastructure

Tools for measurement and prediction of Trust Risk Cost Eco-efficiency

Security Identity management, etc. to handle

interconnection of clouds

SP TOOLS Programming model

Implement new service components Integrate existing ones IDE + runtime for workflow style applications

License management Integrate license-protected software in servicesChallenges:

Elastic services Migrating services

SP TOOLS (CONT.)

Service Optimizer (SO)Overall management of services

Tracking state and deployment(s) Performance monitoring Re-deployment

Contextualization mechanismsDynamic runtime setup of VMs and services,

with respect to networking etc.Two step process:

Preparation Attach boot-scripts in ISO image and couple this

with VM image Self-contextualization

Booting from ISO-image

IP TOOLS Admission Control (AC)

Capacity planning and safe overbookingAccept incoming service request or not?

+ Increased revenue- Added provisioning costs? Implications for already hosted services

Services are elastic Degree of elasticity differ Time and duration of spikes differ

Similar problems Network bandwidth multiplexing Selling airline seats

Long-term capacity planning (cf. scheduling)

IP TOOLS (CONT.) VM Management

VM lifecycle managementScheduling: optimal mapping of VMs

to physical hosts in an IP across multiple clouds

Federation and bursting

When? Admission of new service, upon elasticity, faults,

periodically

Optimal? SP perspective:

Performance (hosts, VMs), cost, guarantees, TREC, etc.

IP perspective: Provisioning cost, consolidation, isolation, SLA

violations, etc.

IP TOOLS (CONT.)

Fault Tolerance EngineAutomatic VM checkpointing and restart Intervals configurable

Cloud Optimizer (CO)Combines monitoring and prediction with

IP-level engines to perform self-management

Overall decisions related to local vs. bursted/federated VM placement etc.

Policy reconfiguration

COMMON TOOLS FOR SPS AND IPS

Service Deployment Optimizer (SDO) Coordinates service deployment process

Discovers and filters IPs, negotiates SLAs, assesses TREC-factors, contextualizes services, uploads data, deploys services

Service deployment (SP to IP) Private cloud + multi-cloud service deployment

VM placement (IP to IP) Cloud bursting + federation

Data Management Transfer of VM images and service data for deployment;

SP to IP, and IP to IP Manages distributed file system for service applications Automatic re-location of service data across federated IPs

COMMON TOOLS (CONT.)

SLA Management (CloudQoS)Creation and monitoring of SLAsWS-Agreement term extensions for TRECNegotiation primitives (WS Agreement

Negotiation) Elasticity Engine

Feedback controller for automatic and proactive VM allocation to meet peaks and lows in demand

More about this one later…

EXAMPLE: SERVICE DEPLOYMENT (SP SIDE)

EXAMPLE: SERVICE DEPLOYMENT (IP SIDE)

EXAMPLE: SERVICE OPERATION (IP SIDE)

OPTIMIS TOOLKIT DEPLOYMENT ILLUSTRATIONS

Bursted private clouds

IP

SPSDO SO

CO

AC SDO

SO

Private Cloud

CO

ACIP

CO

AC SDO

SO

IP

SPSDO SO

Federatedclouds

CO

ACIP

CO

ACIP

SPSDO

SO Multi-clouds

CO

ACIP

CO

ACIP

FURTHER FUTURE DIRECTIONS Use cases:

Programming model Service construction/composition Examples in ERP/CRM (SAP) and bio-informatics

Cloud bursting Outsourcing based on TREC Interoperation with OPTIMIS

and non-OPTIMIS Ips E-Education test cases

Cloud brokering, a broker: Acts as IP to SPs Acts as SP to IPs Is independent? Provides value-added

services?Infrastructure Provider

Infrastructure Provider

Infrastructure Provider

Service Provider

Broker

CURRENT & FUTURE DIRECTIONS (CONT.) OPTIMIS (the project) : June 2010 … May

2013 Basic plumbing in place Algorithmical improvements next focus

TREC-aware self-* management policies Holistic management, BLO-driven IP- and SP-

operation Experimentation needed Open for collaborations

ACKNOWLEDGMENTS: THE OPTIMIS CONSORTIUM