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Internet Service Migration and Placement . Part 1 Instructor: Xiaodong Zhang Xiaoning Ding 11/08/2004. Outline. Background OPUS: An Overlay Peer Utility Service Overview Architecture Research issues Model-based resource provisioning Overview Web service model

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internet service migration and placement

Internet Service Migration and Placement

Part 1

Instructor: Xiaodong Zhang

Xiaoning Ding

11/08/2004

outline
Outline
  • Background
  • OPUS: An Overlay Peer Utility Service
    • Overview
    • Architecture
    • Research issues
  • Model-based resource provisioning
    • Overview
    • Web service model
    • Model-based resource allocator
outsourcing services utility based services
Outsourcing Services & Utility-based services

Utility-based services

  • The service provider owns the infrastructure
  • leases the infrastructure to the customers
  • pay for what you use
  • Example: Internet data center enabling ASPs to deliver ASP services

Outsourcing services

  • Customer-owned or leased system.
  • The service provider takes responsibility for managing the customer’s IT and network system – the computing infrastructure – based on customer-defined service level agreements (SLA).
  • Billed on a monthly or fixed-fee basis.
utility sla
Utility & SLA
  • Utilities deliver IT resources (CPU, storage, and bandwidth) to hosted application and, ultimately, end users
  • much as the electric utility transparently delivers power on demand to customers.
  • Applications agree to Service Level Agreements (SLAs) with the utility
slide5
Static Provisioning
  • Dedicate fixed resources per application
  • Reprovision manually as needed
  • Overprovision for surges
    • High variable cost of capacity
load is dynamic
Load Is Dynamic
  • ibm.com external site
  • February 2001
  • Daily fluctuations
  • Workday cycle
  • Weekends off

M T W Th F S S

  • World Cup soccer site
  • May-June 1998
  • Seasonal fluctuations
  • Event surges (11x)
  • ita.ee.lbl.gov

Week 6 7 8

adaptive provisioning
Adaptive Provisioning

offer economies of scale

  • Network access
  • Power and cooling
  • Administration and security
  • Surge capacity
overlay network and mobile code
Overlay network and Mobile code
  • Increasing number of important network services are deploying overlays
    • CDN, Replicated services, Storage systems...
    • Dynamically map data and functions onto network resources
  • Programs and data will adaptively migrate and replicate in response to changing network conditions, client access characteristics,...
    • Programs dynamically run at optimal network locations
    • Data dynamically flow to where it is required.
outline1
Outline
  • Background
  • OPUS: An Overlay Peer Utility Service
    • Overview
    • Architecture
    • Research issues
  • Model-based resource provisioning
    • Overview
    • Web service model
    • Model-based resource allocator
opus an overlay utility service
OPUS: An Overlay Utility Service

Peering

Overlay node

App demand

(per network region)

Allocate nodes to services based on current demand

opus overview
OPUS: Overview
  • targeting utilities consisting of a distributed set of thousands of server sites, each with potentially 1000's of individual machines, cooperating together to fulfill aggregate SLAs
  • Simultaneously hosts multiple distributed applications
    • replicated web services
    • application-layer multicast
    • content distribution networks.
    • ...
opus tasks
Opus tasks
  • Resource allocation
    • Allocate resources among competing applications
    • Maximize aggregate performance
    • Based on changing application and network characteristics, SLAs
  • Replica placement
    • Closely related to resource allocation
    • Where to place individual application replicas
    • Consider dynamically changing client access patterns, network failures, etc.
opus tasks1
Opus tasks
  • Overlay topology construction
    • create overlays that meet application requirements of performance, delay, and reliability
    • minimize consumed network resources
  • Request routing
    • discover the service replica capable of delivering the highest quality of service
the service overlay
The service overlay
  • Each Opus site runs an instance of site manager coordinating resource usage at that site and exchange status summaries with other opus sites.
  • Interconnects all active nodes and provides overlay services
  • “Backbone” for coordinated, decentralized resource allocation and resource control
the service overlay1
The service overlay
  • Assist the construction and maintenance of application overlay
  • Dynamic and self-healing
  • Scalability issue
    • Hierarchical data dissemination in dicast
    • Think globally but act locally
adaptive per application overlay
Adaptive per-application overlay
  • Each application uses its application overlay to
    • Route internal application traffic
    • Disseminate content
    • Synchronize state information
  • The topology and site allotments are subject to change by resource allocator
security and isolation
Security and isolation
  • Allocating resources to applications at the granularity of individual nodes
  • Future plan: using virtual machine
  • Using VLAN to isolate traffic on the wire
research issues
Research Issues
  • Overlay topology construction
  • Resource allocation
  • Scalable tracking of system characteristics
  • Reliability QoS guarantees
overlay topology construction
Overlay topology construction
  • Emphasize scalability
    • Quantify the benefits of competing structures
    • Develop scalable distributed constructing algorithms
  • Initial work
    • A general overlay topology that enables dynamic tradeoffs between network performance/reliability and cost
    • Focus on network cost and relative delay penalty (RDP) to characterize overlay topology
    • Two candidate overlay topologies: K-spanner and LAST.
overlay topology construction2
Overlay topology construction
  • Distributed algorithms for building and maintaining the topology
    • Selectively probing using probabilistic techniques and hierarchy
    • Using partial, approximate and probabilistic knowledge of network infomation
    • Having each node gradually migrate to its “proper” location in the overlay.
resource allocation
Resource allocation

classical economic model

  • Customers are associated with utility functions specifying the value of the services result from a allotment. (concave functions)
  • Opus maximizes global value across all applications.
  • Optimal solution: the marginal value of an additional resource unit is in equilibrium across all customers.
resource allocation1
Resource allocation

App2

Throughput (Value)

Gradient 2

App1

Gradient 1

Allocated Resources

resource allocation2
Resource allocation
  • Scalability consideration
    • Adapt from economic resource allocation
      • Decentralized federation of autonomous local “markets” exchanging information to converge toward a global equilibrium
    • Celluar structure
      • Cell: an entire Opus site or a portion of large site
      • Cells plan their internal allocation locally
      • Cells operate to trade load or resources
tracking system characteristics
Tracking system characteristics
  • Nodes are partitioned into clusters of size d.
  • Each cluster elects an agent responsible for disseminating local cluster information
  • Agents from d adjacent clusters form second-level clusters
  • All nodes are organized into a tree called dicast tree. Height=logdN
tracking system characteristics1
Tracking system characteristics

Hierarchical data dissemination in dicast

tracking system characteristics2
Tracking system characteristics
  • Data travels up the tree, and may be aggregated with data from the nodes
  • At each level of the tree, an overlay propagates the data among all participating cluster members
  • Updates are buffered awaiting the arrival of further updates until a threshold is reached, and updates are aggregated
  • Each node may has
    • exact information of “nearby” nodes in the same cluster
    • Aggregate information of remote cluster
reliability qos guarantees
Reliability QoS Guarantees

Address network level failures

  • Restricted flooding
    • Redundantly transmit the same data over multiple logical path
  • Minimizing the overhead
    • Intermediate nodes re-evaluate the reliability of the remainder of the path, and choose between forwarding redundant data and suppressing duplicate data
reliability qos guarantees1
A

0.98

0.96

0.99

B

J

D

S

0.97

0.97

Reliability QoS Guarantees

SAD: 0.96*0.98*0.99=0.931

SAD: 0.97*0.97*0.99=0.931

SA and BD: (1-(1-0.96*0.98)*(1-0.97*0.97))

*0.99=0.987

reliability qos guarantees2
Reliability QoS Guarantees
  • To match the overlay topology with the failure characteristics of underlying network
    • Construct overlays with disjoint paths to lower the failure correlation among logical overlay links
    • Collect statistical information about loss correlation
    • Use network topology information
outline2
Outline
  • Background
  • OPUS: An Overlay Peer Utility Service
    • Overview
    • Architecture
    • Research issues
  • Model-based resource provisioning
    • Overview
    • Web service model
    • Model-based resource allocator
overview
Overview
  • Addresses the provisioning problem
    • Multiple competing services hosted by a shared server cluster (utility)
    • How much resource does a service need to meet SLA targets
  • Applications
    • Static web content
    • Heavily resource-intensive
    • Predictable in average per-request resource demands
model based resource allocator
Model-based resource allocator
  • Periodically invoked by the utility OS executive to adjust the allotments
  • Focus on memory and storage resources, ignore CPU constraints
  • Output
    • an allotment vector for each service
    • CPU share,Memory and storage allotment [M, φ]
model based resource allocator1
Model-based resource allocator
  • Resource provisioning primitives
    • Candidate plans initial candidate allotment vectors
    • LocalAdjust modifies a candidate vector to adapt to local resource constraint or surplus
    • GroupAdjust modifies a set of candidate vectors to adapt to a resource constrait or surplus
model based resource allocator2
Model-based resource allocator

Generating Initial Candidates

Ρtarget Rp

Φ=μs

H M (1)

|Φ-Φdesired|<ε

Rp, Φ, ρtarget Rs (4)

λ,H λs (2)

Φ=λs / ρtarget

references
References
  • Utility Computing White Paper: http://www.sun.com/service/utility/FINAL_UC_WP.pdf
  • Service Utilities: http://issg.cs.duke.edu/utilies.html
  • D. G. Andersen, H. Balakrishnan, M. F. Kaashoek, and R. Morris, "Resilient Overlay Networks," in 18th ACM Symposium on Operating Systems Principles (SOSP), October 2001, pp. 131-145.
  • "OPUS: Overlay Utility Service", Rebecca Braynard, Dejan Kostic, Adolfo Rodriguez, Jeff Chase and Amin Vahdat, poster at 18th ACM Symposium on Operating System Principles (SOSP), Banff, Canada, October 2001. ( poster)
  • R. Braynard, D. Kostic, A. Rodriguez, J. Chase, and A. Vahdat. Opus: an Overlay Peer Utility Service. IEEE OPENARCH 2002.
  • Ronald P. Doyle, et. al., ``Model-based resource provisioning in a Web service utility", Proceedings of the 4th USENIX Symposium on Internet Technology, 2003.
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