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

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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

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Parallelism, to Grid, to Clouds ...

• To approach todays 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

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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
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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

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XtreemOS System
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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

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Federation Objectives

• Develop a Federation support that integrates and
  actively coordinates SLA management provided by
  single Cloud providers
• Do not disrupt providers 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

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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

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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

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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

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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

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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?

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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

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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

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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

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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)

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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

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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.

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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
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Cloud P2P Combination
Letting the cloud manage the bootstrap and peak
load
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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

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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
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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
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Workload for Simulations
Load and number of players
Positions of objects/avatar
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Whats next?

• Elastic provisioning and Prediction in SAM
• Dynamic management of the AOI in PAM

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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.16.
http//ieeexplore.ieee.org/xpl/articleDetails.jsp?
tparnumber5679660
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Beware!

• Backup slides behind.

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Load Characterization
Cloud
P2P
Cloud
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SAM Architecture
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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
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Some Collaborations