Issues and Opportunities Issues and Opportunities of Cloud Federationsof Cloud Federations

Massimo Coppola Massimo Coppola in collaboration with in collaboration with

Laura Ricci, Emanuele Carlini, Laura Ricci, Emanuele Carlini, Patrizio Dazzi, Ranieri Baraglia Patrizio Dazzi, Ranieri Baraglia

Summary

• Cloud Computing• Where do we come from :

HPC, Parallel Computing, Grids, P2P• Federations of Clouds

• What and why• What we inherit from our past experiences

• Autonomic, P2P, Resource Scheduling

• Cloud applied to virtual environments• Business models for cloud federations

Parallelism, to Grid, to Clouds ...

• To approach today’s Clouds, and boldly go beyond them, many techniques and theoretical results can be reused

• sometimes are reinvented with a different name...• Scheduling and resource management

from Parallel and Grid Computing• P2P techniques to cheaply and widely

spread information• Autonomic management based on

performance models of applications

XtreemOS IP project is funded by the European Commission under contract IST-FP6-033576

4XtreemOS IP project

is funded by the European Commission under contract IST-FP6-033576

Grid and Cloud computing with XtreemOS Part 3 - Basic of System Administration

Massimo Coppola ISTI-CNR, Italy with contributions by Christine Morin and countless

collaborators within XtreemOSEurosys 2010, Paris

SRDS and RSS

SRDS (service and resource discovery service)as part of the XtreemOS releases Requested for node selection by the AEM

New functionalities Support of multiple underlying DHTs (Scalaris, Overlay

Weaver) Support of XACML policy filters Support of the new mutithreaded DIXI

Tested using up to 500 machines from Grid'5000

XtreemOS IP project - EC IST-FP6-033576 - Eurosys 2010 Tutorial, Paris

XtreemOS System

Contrail Iaas Federation

A Contrail Federation integrates in a common platform multiple Clouds,

of public and private kind.User identities, data, and resources are interoperable within the federation, thanks to•common supports for authentication and authorization•common mechanisms for policy definition, monitoring, and enforcing of all aspects of QoS :

SLA, QoP, etc. •the basis of a common economic model

Federation Objectives• Develop a Federation support that integrates

and actively coordinates SLA management provided by single Cloud providers

• Do not disrupt provider’s business model• Cloud administration is not Federation

management• Allow exploiting a Federation as a single Cloud

• Cloudbursting to and from the Federation• Federation Support must be scalable

• Number of apps running, providers, resources, users

Cloud revolutions

• Is there a place for “small” Cloud providers?• they offer lower scalability, are not worldwide

• Large Cloud providers are subject to contrasting forces• concentration data centers where

management is cheaper• placing resources scattered over the internet

structure, to improve the networking cost • m.media streaming and real time enjoy lower

latencies and round-trips, less overall bandwidth

Cloud revolutions

• Federations as a way to flexibly merge separate providers• Smooth the size disadvantage• Increase the “market size”• Provide a competitive edge as small

providers are already geographically distributed

Distributed Architecture• Abstract API is replicated onto each Federation

access point• FAP act as brokers, but share a common view

• Security, provider status, user actions• FAP not restricted to “local” provider

• Policies and auth/authZ are common

• Contention issues• Final resource allocation is

on providers• Shared info helps

management• AP either hosted by provider,

or on independent HW

F

F

F

F

Holistic approach to QoS

• Extend the set of characteristics to be measured on the platform

• Protection• Type of security mechanisms which are in place

• Auth. Protocols, Encryption mechanisms, Isolation• Privacy

• Guarantees offered by storage holder, network infrastructure

• Geo-localization • Can have deep legal implications

• More in the future• E.g. power consumption: overall power,

efficiency

Planning for SLAs

• Choose the best provider(s) and map the application on the virtual resources provided

• Beside constraints, multiple criteria choice• Many user criteria• Federation has its own goals

• balance user satisfaction• balance provider satisfaction

• How do you choose the resources?• What if one provider is not enough?

Application and SLA splitting• Application deployment on multiple

providers : a federation is more than the sum of its providers• Type and amount of resources needed• Sudden elasticity• Peculiar resource dislocation

• Tough issue • Multi-criteria and problem size

• Both at SLA negotiation and at run-time• Matching application structure and SLA• Identifying suitable set of providers and

mapping

Standard interoperation

• Standards are still “flowing” in the Cloud• except de facto ones

• Interoperation is mandatory• We are building an open-source OVF

toolkit a standard converter• with INRIA and XLAB• (de)serialize in memory Java structures from

to OVF and other standards for VM and Application description

• will be extended to deal with SLA standards

Future directions

• Apply autonomic heuristics to Clouds and Federations, and develop new ones.

• New business models to be applied in Cloud Federations• For Service Providers, Federation

aggregators and/or end-users• W.r.t the security and trust counterpart:

24/7 UCON authorization and “geographic” SLA constraints

Digital Virtual Environments

• Player can move and interact with the surrounding environment

• Shared sense of space among players• Modifications of the environment

visible to every players• Area Of Interest (AOI)

Virtual Environments

• Complex and challenging applications• High number of players• Near real-time constraints

• Quadratic (or cubic) load (bandwidth, cpu) depending on the number of players: seasonal

• QoS requirements depends on the user behavior• movements vs interactions

Aim of the work

• Distributed architecture for Virtual Environments• scalable in QoS and cost

• Exploit the (illusion of) infinite resources of Cloud Computing and the free resources of user machines.

Hybrid Architecture?

• Private server-racks are fine... but they are statically sized for the peak load

• Pure P2P should scale up.. but makes it hard to manage the QoS in limit situations

• Only cloud? Costly for large instancesCombination of the Cloud and P2P to

support the DVE in an inexpensive and QoS-aware fashion

Cloud & P2P Combination

Letting the cloud manage the bootstrap and peak load

Concrete Architecture

• State Action Manager (SAM)• manages the state.

Medium rate, No error tolerance, Conflicts

• Positional Action Manager (PAM)• manages the position.

High rate, Some error tolerance, No conflicts

SAM

• Cloud IAASs runs on a DHT together with users machines

• Heuristics decide when moving load from users to Cloud

• Backups for user machines

w/o heuristic

with heuristic

PAM (she likes to gossip!)

• “Wisdom of the Crowds”

• A best-effort gossip-based algorithm

• Storage Cloud as support

• Around 70-80% less requests to the Cloud

accurate, slower heuristic

faster heuristic

Percentage of object retrievalusing gossip

Workload for Simulations

Positions of objects/avatarLoad and number of

players

What’s next?

• Elastic provisioning and Prediction in SAM

• Dynamic management of the AOI in PAM

Some References

Carlini E., Coppola M., Dazzi P., Ricci L., and Righetti G.. “Cloud Federations in Contrail”. Euro-Par 2011: Parallel Processing Workshops, LLNCS 7155, 2012.

Carlini, E., M. Coppola, and L. Ricci. “Flexible Load Distribution for Hybrid Distributed Virtual Environments”. submitted

Carlini, E., M. Coppola, and L. Ricci. “Gossip-Based Best-Effort Interest Management for Distributed Virtual Environments”. submitted

Carlini, E., M. Coppola, and L. Ricci (2010). Integration of P2P and Clouds to Support Massively Multiuser Virtual Environments. In: Network and Systems Support for Games (NetGames), 2010 9th Annual Workshop on. IEEE, pp.1–6. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5679660

Beware!Backup slides behind.

Cloud

Cloud

P2P

Load Characterization

SAM Architecture

PAM: Area Coverage

Find a subset of areas that maximize the coverage is a NP

problemTwo heuristic:

- greedy: slower, but more accurate- score: faster, but less accurate

Some Collaborations