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Issues and Opportunities of Cloud Federations. Massimo Coppola in collaboration with Laura Ricci, Emanuele Carlini, Patrizio Dazzi, Ranieri Baraglia . Summary. Cloud Computing Where do we come from : HPC, Parallel Computing, Grids, P2P Federations of Clouds What and why

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Issues and Opportunities of Cloud Federations

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Issues and Opportunities of Cloud Federations

Massimo Coppola in collaboration with Laura Ricci, Emanuele Carlini, Patrizio Dazzi, Ranieri Baraglia


  • 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

  • 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 XtreemOS

    Eurosys 2010, Paris

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

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


    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

        • 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

    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 instances

    Combination 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


    • 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

    Percentage of object retrieval

    using gossip

    accurate, slower heuristic

    faster heuristic

    Workload for Simulations

    Load and number of players

    Positions of objects/avatar

    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.


    • Backup slides behind.

    Load Characterization




    SAM Architecture

    PAM: Area Coverage

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

    Two heuristic:

    - greedy: slower, but more accurate

    - score: faster, but less accurate

    Some Collaborations

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