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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

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
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 and RSS System Administration

    • 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

    Contrail iaas federation
    Contrail Iaas Federation Tutorial, Paris

    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
    Federation Objectives Tutorial, Paris

    • 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
    Cloud revolutions Tutorial, Paris

    • 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 revolutions1
    Cloud revolutions Tutorial, Paris

    • 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
    Distributed Architecture Tutorial, Paris

    • 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
    Holistic approach to QoS Tutorial, Paris

    • 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
    Planning for SLAs Tutorial, Paris

    • 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 and SLA splitting Tutorial, Paris

    • 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
    Standard interoperation Tutorial, Paris

    • 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
    Future directions Tutorial, Paris

    • 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
    Digital Virtual Environments Tutorial, Paris

    • 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
    Virtual Environments Tutorial, Paris

    • 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
    Aim of the work Tutorial, Paris

    • 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
    Hybrid Architecture? Tutorial, Paris

    • 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
    Cloud & P2P Combination Tutorial, Paris

    Letting the cloud manage the bootstrap and peak load

    Concrete architecture
    Concrete Architecture Tutorial, Paris

    • 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 Tutorial, Paris

    • 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
    PAM Tutorial, Paris(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
    Workload for Simulations Tutorial, Paris

    Load and number of players

    Positions of objects/avatar

    What s next
    What’s next? Tutorial, Paris

    • Elastic provisioning and Prediction in SAM

    • Dynamic management of the AOI in PAM

    Some references
    Some References Tutorial, Paris

    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.

    Beware! Tutorial, Paris

    • Backup slides behind.

    Load characterization
    Load Characterization Tutorial, Paris




    Sam architecture
    SAM Architecture Tutorial, Paris

    Pam area coverage
    PAM: Area Coverage Tutorial, Paris

    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
    Some Collaborations Tutorial, Paris