Paskorn Champrasert and Junichi Suzuki

1
Building Self-Configuring Data Centers with Cross
Layer Coevolution

• Paskorn Champrasert and Junichi Suzuki
• Department of Computer Science
• University of Massachusetts, Boston

2
Content

• Goal
• Design Approach
• Overview of SymbioticSphere
• Simulation Results
• Conclusion

3
Motivation

• Large-scale network systems
• e.g., Internet Data Center
• A tons of servers and network devices (e.g.
  router, load manager) are connected through the
  high speed network.
• A lots of users access several services (e.g. web
  server) and data (web pages) that Internet Data
  Center provides.
• Such network systems stillrapidly keep
  increasing intheir scale.

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Goal

• Making network systems ( e.g. Internet data
  centers and grid clusters) to be
• autonomous to avoid interrupting
  users/administrators frequently
• adaptable to various dynamic changes in network
  conditions
• e.g., network traffic and resource availability
• in order to
• improve user experience (i.e. response time)
• expand systems operational longevity
• (e.g. users and administrators dont want
  applications down for long time)
• reduce maintenance cost
• (e.g. Save money and relieve developers from
  time-consuming maintenance)

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Observation and Approach

• Observation
• Various biological systems have already developed
  the mechanisms to achieve key requirements of
  network systems.
• e.g. autonomy, adaptability
• c.f. bee colonies, bird flocks, fish schools,
  etc.
• Approach
• Apply biological concepts and mechanisms to
  design network systems (i.e. application services
  and middleware platforms).

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SymbioticSphere

• SymbioticSphere is a biologically-inspired
  architecture for network systems( network
  applications and middleware platforms)
• (Symbiosis the living together of two
  dissimilar organisms)
• An application service (Agent)
• is implemented by an autonomous and distributed
  agent.
• an agent may implement a web service and contains
  web pages.
• A middleware platform (Platform)
• runs on a network host and operates agents.
• Each agent/platform is designed as a biological
  entity.
• Some biological principles are applied to design
  agents and platforms

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Design Principles
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Energy Exchange

• Human users the sun
• have unlimited amount of energy.
• Agents producers ( e.g. shrubs)
• gain energy from users
• pay some of its energy level to platforms to
  utilize resources
• Platforms consumers (e.g. hares)
• gain energy from agents
• periodically evaporate some of its energy level.

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Agents and Platforms

• Agent
• Agent ID
• Energy level
• Service name
• Service
• Behaviors
• Behavior policies

Agent/platforms behaviors
When an agent/platform invokes a behavior, it pay
energy.

• Platform
• Platform ID
• Energy level
• Middleware services
• Behaviors
• Behavior policies

• SymbioticSphere service daemon
• runs on network host
• handles
• - platform reproduction requests - host
  resource availability requests
• - forward service requests from users
  when there is no platform

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Behavior Policy

• Each agent/platform has its own policy for each
  behavior.
• A behavior policy
• defines when to and how to invoke a particular
  behavior.
• A behavior policy
• consists of factors (Fi), which evaluate
  environment conditions.
• Each factor is given a weight (Wi) relative to
  its importance.
• A behavior is invoked if the weighted sum of
  its factor values exceeds a threshold.
• Agents/Platforms periodically check weighted sum
  to invoke behaviors

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Agent Behavior Policy

• Factors in agent migration behavior
• Energy Level ( the agent energy level )
• encourages agents to move in response to higher
  energy level.
• Service Request Ratio
• The ratio of of incoming service requests on a
  remote platform to the local platform
• encourages agents to move towards users.
• Resource Availability Ratio
• The ratio of resource availability (--CPU cycles,
  memory space, etc.) on a remote host to the local
  host
• encourages agents to move to platforms running on
  healthier hosts
• Migration Interval Time interval to perform
  migration
• discourages agents to migrate too often

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Platform Behavior Policy

• Factors in platform reproduction behavior
• Energy Level Platform energy level
• encourages platforms to reproduce their offspring
  in response to higher energy level.
• Resource Availability Ratio The ratio of
  resource availability on a remote host to the
  local host.
• encourages platforms to reproduce their offspring
  on healthier neighboring hosts.
• The Number of Agents The number of agents
  working on the local platform
• encourages platforms to reproduce their offspring
  in response to high agent population on them

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Agents/Platforms Cooperation

• Symbiotic behaviors are intended to augment the
  adaptability of agents and platforms by allowing
  two species to cooperate for pursuing their
  mutual benefits
• Each symbiotic behavior is a sequence of regular
  behaviors that an agent and its underlying
  platform perform in order.
• There are two type of symbiotic behaviors
• 1) Agent-initiated symbiotic behaviors (A1 A3)
• An agent proposes the underlying platform to
  perform symbiotic behaviors.
• The platform may accept the proposal and perform
  symbiotic behaviors.
• 2) Platform-initiated symbiotic behaviors
  (P1-P3)
• A platform proposes the agents working on it to
  perform symbiotic behaviors.
• The agent may accept the proposal and perform
  symbiotic behaviors.
• A symbiotic behavior policy is a behavior policy
  that each agent/platform possesses to determine
  whether it invokes a particular symbiotic
  behaviors.
• when to propose/accept to perform symbiotic
  behaviors ( S WSi FSi gt threshold) and (
  condition is true )

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Agent-initiated symbiotic behaviors A1
Condition
- An agent wants to migrate to host that close to
user but there is no platform on that host. - A
Platform has low resource availability
An agent wants to migrate toward a user
migrate
A
A
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Action
Energy for platform replication
1) Agent proposes to perform A1. 2) Agent gives
destination host information and pays energy to
let platform replicate.3) Platform replicates on
the host.4) Agent migrates
Propose
1
2
Platform
replicate
Platform
3
Mutual Benefit
A platform replicated closer to a user
Low resource availability
Agent can migrate toward to user -gt Response time
reduces -gt high chance to get energy Platform
increases resource avail. -gt reduce the chance to
be crashed
A host close to a user
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Evolution

• Agents/Platforms contain behavior policies
  (weight and threshold values) as their genes
• Each agent/platform may have different genes.

Genes
weight
threshold

• Genetic OperationsWhen an agent/platform wants
  to reproduce it finds a mate.
• The mate is the neighboring agent/platform in
  best rank of energy utility, behavior invocation
  efficiency.
• Two parents genes are combined
• Crossover- Mutation

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Simulation Configurations

• A simulated network system is modeled as an
  Internet data center.
• 7x7 grid network topology.
• 49 network hosts
• Each agent implements a web service in its body
• There is one agent and one platform on each host
  at the beginning of simulation.
• 49 agents and 49 platforms

Input This service request rate is taken from a
workload trace of the 1998 Winter Olympic
official website
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Adaptability Measures

• Adaptability is measured as
• Service Adaptation
• Service Availability
• the number of agents
• Quality of Service
• response time of agents for processing service
  requests from users
• Resource Adaptation
• Resource availability
• the number of platforms that makes resources
  available for agents
• Resource efficiency
• indicates how many service requests can be
  processed per resource utilization of agents and
  platforms.

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Regular Behaviors without GA
Input
Output
Service availability ( of agents) and resource
availability ( of platforms) change dynamically
The biological mechanisms in SymbioticSphere
contribute for agents and platforms to
collectively retain response time and throughput
performance by adjusting their populations and
locations.
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R regular behaviorsS regular symbiotic
behaviorsG genetic operations
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Conclusion

• This paper
• presents two different (regular and symbiotic)
  behaviors that agents and platforms implement in
  SymbioticSphere.
• describes how evolution happens in
  SymbioticSphere.
• Simulation results show that
• agents and platforms autonomously adapt to
  dynamic environmental conditions (e.g., user
  location, network traffic and resource
  availability) by using their regular behaviors.
• the symbiotic behaviors improve the adaptability
  of agents and platforms.
• a quality set of behavior policies can be
  obtained through evolution and CoEvolution in
  much shorter time than trial and errors.
• Future works
• Multi-objective optimization GA
• Self adaptation mutation
• Dynamic network topology
• Service composition
• Multiple types of agent

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Other Results

• Adaptability GRIDNETS 05
• Biologically-inspired mechanisms in
  SymbioticSphere contribute for agents and
  platform to adapt to various dynamic changes in
  network conditions (such as workload and resource
  availability) -- improve resource efficiency
• Scalability CIIT 05
• Biologically-inspired mechanisms in
  SymbioticSphere contribute for agents and
  platform to scale to large number of network
  hosts and user request rate.
• Power Saving and Load Balancing ICAS 06
  SymbioticSphere saves nearly 50 power
  consumption at maximum, compared with traditional
  network systems
• Self Healing (Survivability) COMPSAC 06
• Biologically-inspired mechanisms in
  SymbioticSphere contribute for agents to survive
  network link failures (data center failures) and
  maintain high throughput for users.

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Thank you

• http//dssg.cs.umb.edu/paskorn
• paskorn_at_cs.umb.edu

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Request Forwarding
agents
User access point
Service daemon
Network host
Data center

• When a user requests a service
• the user creates a request message and sends to
  the data center.
• When service request arrives a host.
• The service daemon checks whether there is a
  platform and any agents working on its.
• If there is no platform, service daemon sends
  request msg to neighboring hosts.
• If there is a platform and agents on the host
• Service request msg is placed in service request
  queue in the platform
• A request message in the queue will be taken by
  an agent running on the platform