Investigating Survivability Strategies for UltraLarge Scale ULS Systems

1
Investigating Survivability Strategies for
Ultra-Large Scale (ULS) Systems
Jaiganesh Balasubramanian jai_at_dre.vanderbilt.edu w
ww.dre.vanderbilt.edu/jai
Dr. Aniruddha Gokhale gokhale_at_dre.vanderbilt.edu w
ww.dre.vanderbilt.edu/gokhale
Dr. Douglas C. Schmidt schmidt_at_dre.vanderbilt.ed
u www.dre.vanderbilt.edu/schmidt
Dr. Sherif Abdelwahed sherif_at_isis.vanderbilt.edu w
ww.isis.vanderbilt.edu/sherif
2
Ultra-Large Scale (ULS) System Characteristics

• Key characteristics of the problem space
• Network-centric, dynamic, very large-scale
  systems of systems
• Stringent simultaneous QoS demands, e.g., never
  die, time-critical, etc.
• Highly diverse, complex, increasingly
  integrated/autonomous application domains

3
Motivating Scenario for ULS

• Impact of Service-Oriented Architectures on
  enterprise distributed real-time embedded (DRE)
  ULS systems
• Applications composed of an operational string
  of services
• A service is an assembly of components
• Dynamic (re)deployment of services into
  operational strings is necessary
• Performability performance survivability
  requirements

• Key challenges
• Regulating adapting to (dis)continuous changes
  in runtime environments
• e.g., online prognostics, dependable upgrades
• Satisfying tradeoffs between multiple (often
  conflicting) QoS demands
• e.g., secure, real-time, reliable, etc.
• Satisfying QoS demands in face of fluctuating
  and/or insufficient resources
• e.g., mobile ad hoc networks (MANETs)

4
Some Performability Challenges for ULS Systems

• Performability challenges in dynamic provisioning
  of operational strings services
• Service workloads resource capacity issues
  service placement depends on workloads
  available resources
• Service accessibility patterns service
  survivability depends on its sharing degree

• Differentiated levels of QoS affects resource
  provisioning survivability strategies
• Operational string service failover different
  failover possibilities e.g., as a whole or part
  operational string or one service at a time
• No one-size-fits-all dependability strategy
  cannot dictate one survivability strategy on all
  services operational strings

Application performability addressed by resolving
service placement survivability problems
5
Model of Approach

• Model addresses various concerns
• Per-service concern Choice of implementation
• Depends on resources, compatibility with other
  components in assembly
• Coupling concern Choice of invocation
  communication mechanism used
• Sharing concern Shared services will need
  proactive survivability since it affects several
  services simultaneously
• Failure recovery concern What is the unit of
  failover?

• Availability concerns What is the degree of
  redundancy? What replication styles to use? Does
  it apply to whole assembly?
• Deployment concerns How to select resources? How
  much sharing?
• Assembly concerns What components to assemble
  dynamically? Configurations optimizations for
  end-to-end performability?

Service placement service survivability
strategies address these concerns
6
Addressing the Service Placement Problem

• Service placement problem must consider
• Set of computation nodes attributed by
• Processing index or capacity
• Memory index or capacity
• Survivability index
• Set of communication links attributed by
• Bandwidth index
• Survivability index
• Set of components attributed by
• Different implementations offering performance
  tradeoffs across quality dimensions
• Different implementations consuming various
  amounts of resources
• Constraints on being deployed as an assembly to
  offer a complete service

• Replica placement issues involve
• Different availability requirements for different
  assemblies of components
• Multiple replicas needed, tolerate
  non-availability of replicas based on importance
  of assemblies
• Replica resource provisioning depending on
  replication schemes used
• Load balancing of replicas if resources available
  but introduce run-time problems on consistency

Service placement algorithms must consider
tradeoffs between providing performance to
applications providing survivability to
applications, allocating resources either to
primaries or replicas
7
Addressing the Survivability Problem

• A configurable approach to survivability
  including micro- (infrastructure) macro-
  (assembly operational string) level strategies

• Micro-level strategies monitor infrastructure
  state to make proactive decisions at
• Component level (swapping migration)
• Middleware level (configurations)
• Component Server Level (process resource
  allocations)
• Node level (multiple components)
• Macro-level strategies monitor assembly health to
  make failover decisions

• Failover based on type of failover unit
• Affects service placement decisions
• May involve load balancing
• State synchronization issues
• Replication styles (hidden by FT strategies)

• Initial prototype developed using
  Component-Integrated ACE ORB (CIAO) Deployment
  Configuration Engine (DAnCE) (www.dre.vanderbilt
  .edu)
• Future work on Data Distribution Service (DDS)
  Distributed Real-time Specification for Java
  (DRTSJ)