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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


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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