This document proposes a method to estimate dependability attributes (risks) in virtual network environments. It uses a hierarchical modeling approach combining reliability block diagrams and stochastic Petri nets to model large virtual networks. The method was evaluated using a case study that generated virtual network requests and assessed dependability metrics like availability and reliability. The results demonstrate how dependability measures can vary between virtual networks and be impacted by common mode failures. Future work involves improving dependability through fault tolerance and incorporating metrics into resource allocation algorithms.

1. Stenio Fernandes, Eduardo Tavares, Marcelo Santos,
Victor Lira, Paulo Maciel
Federal University of Pernambuco (UFPE)
Center for Informatics
Recife, Brazil
Dependability Assessment of Virtualized
Networks

2. Outline
 Motivation, Problem Statement, and Proposal
 Related Work
 Technical Background
 Hierarchical Dependability Modeling and
Evaluation
 Dependability Assessment of VNs
 Contributions and Future Work

3. MOTIVATION, PROBLEM
STATEMENT, AND PROPOSAL

4. Motivation (1/3)
 Network Virtualization is a paradigm shift to allow
highly flexible networks deployment
 Virtual Networks (VN)
– have intrinsic dynamic aspects
 It allows operators to have on-demand negotiation of a
variety of services
 Important properties: concurrent use of the underlying
resources, along with router, host, and link isolation and
abstraction
– resources reuse is performed through appropriate
resource allocation and partitioning techniques

5. Motivation (2/3)
 Network Virtualization management strategies
– rely on dynamic resource allocation mechanisms for
deploying efficient high-performance VNs
 Goal: achieve efficient resource allocation of the physical
network infrastructure
– heuristic approaches due to its NP-hardness nature
– Efficient partitioning and allocation of network
resources is the fundamental issue to be tackled
PhysicalNetworks
Composed Network - Virtual

6. Motivation (3/3)
 However, from the point of view of the end-user
– a Service Provider or any entity that wants to build VN
to offer services
 there is still a missing point:
– What are the risks associated to a certain VN?

7. Problem statement
 Argument & hypotheses:
– risks are inherent to virtualized infrastructures since
the underlying physical network components are failure-
prone
 E.g., subject to hardware and software components
failures
– Understanding Network Failures in Data Centers:
Measurement, Analysis, and Implications, SIGCOMM 2011
– A first look at problems in the cloud. USENIX HotCloud 2010
– Risk is a crucial factor to the establishment of Service
Level Agreements (SLA) between NV engineering and
business players

8. Problem statement
 Risk evaluation and analysis, from assessment of
dependability attributes, can quantify and give
concrete measures to be used for network
management and control tasks
 Risk evaluation must be taken into account when
formulating an optimization problem for resource
allocation and provisioning of components at the
physical network

9. Proposal
 This paper proposes and evaluates a method to
estimate dependability attributes (risks) in virtual
network environments,
– It adopts an hierarchical methodology to mitigate the
complexity of representing large VNs
 Reliability Block Diagram (RBD)
 Stochastic Petri Nets (SPN)
 Assessment of dependability attributes could be
adopted as a critical factor for accurate SLA
contracts

10. RELATED WORK

11. Related Work
 Xia et al. tackle the problem of resource provisioning in
the context of routing in optical Wavelength-Division
Multiplexing (WDM) mesh networks
– Risk-Aware Provisioning scheme that elegantly minimizes the
probability of SLA violation
 "Risk-Aware Provisioning for Optical WDM Mesh Networks,"
Networking, IEEE/ACM Transactions on, June 2011
 Sun et al. proposes a cloud dependability model using
System-level Virtualization (CDSV), which adopts
quantitative metrics to evaluate the dependability
– They focus on cloud security and evaluate the impact of
dependability properties of the virtualized components at
system-level
 "A Dependability Model to Enhance Security of Cloud
Environment Using System-Level Virtualization Techniques,"
1st Conference on Pervasive on Computing Signal
Processing and Applications (PCSPA), 2010

12. Related Work
 Techniques for assessing dependability attributes
have been evaluated in virtual computing systems.
– SPN and Markov models have been adopted to assess them
in VMs and Oses.
 Koslovski et al. takes into account reliability only
support in virtual networks
– it has a general view on nodes and links at the physical
infrastructure
– it does not take into account the hierarchical nature of real
systems,
 Composed of virtual machines, disks, operating systems, etc.
– "Reliability Support in Virtual Infrastructures”, IEEE CloudCom
2010

13. Related Work
 In general
– Simplified views
 Specific to components, sub-systems, etc OR
 Consider only a direct mapping between the
physical infrastructure and a given VN
– little effort on research studies that provide
dependability measures for risk assessment
 They could be adopted as input for resource
allocation algorithms and provisioning techniques

14. TECHNICAL BACKGROUND

15. Technical Background
 Dependability of a system can be understood as
the ability to deliver a set of services that can be
justifiably trusted
– It is also related to fault tolerance, availability, and
reliability disciplines
 Dependability metrics can be calculated by
– Combinatorial Models
 Reliability Block Diagrams (RBD) and Fault Trees
– State-based stochastic models
 Markov chains and Stochastic Petri Nets (SPN)

16. Technical Background
 Some dependability metrics
– Availability (A) of a given device, component, or system
it is related to its uptime and downtime
 Time to Failure (TTF) or Time to Repair (TTR)
 Mean Time to Failure (MTTF) and Mean Time To Repair
(MTTR)
– Steady-state availability (A) may be represented by the
MTTF and MTTR, as:

17. Technical Background
 MTTF can be computed considering the system
reliability (R) as
 Exponential, Erlang, and Hyperexponential
distributions are commonly adopted for
representing TTFs and TTR
– i.e., adoption of semi-markovian solution methods

18. HIERARCHICAL DEPENDABILITY
MODELLING AND EVALUATION

19. Hierarchical Dependability modelling and
evaluation
Proposed methodology for dependability
evaluation of virtualized networks
Three
steps
System
specification
Subsystem
model
generation
System
model
construction

20. Hierarchical Dependability modelling and
evaluation
• information concerning the
dependences of VNs and
possible mutual impacts,
such as Common Mode
Failure (CMF)
• information related to the
TTF of each component or
sub-components and the
respective TTR
System
specification

21. Hierarchical Dependability modelling and
evaluation
• the system may be
represented either by one
model or split into smaller
models that comprise system
parts (i.e., subsystems).
• Such an approach mitigates
possible state space size
explosion for large and
detailed models
Subsystem
model
generation

22. Hierarchical Dependability modelling and
evaluation
• intermediate results are combined
into a higher level model using the
most suitable representation
• For instance, physical nodes are
initially represented by a RBD
model (using series composition)
and the obtained results are
adopted into a SPN model.
• Final model is then constructed by
using the metrics obtained in
previous activity and, lastly, such a
model is evaluated.
System
model
construction

23. Hierarchical Dependability modelling and
evaluation
 Proposed method provides the basis for
obtaining the dependability metrics and for
evaluating quantitative properties
 It utilizes Mercury/ASTRO environment for
modeling and evaluating dependability models
– Tools available to academics (under request)

24. DEPENDABILITY ASSESSMENT OF
VIRTUAL NETWORKS

25. Dependability Assessment of VNs
Evaluation Methodology
• Generation of several VNs requests that must be allocated
on the top of a common physical network
• For each new allocated VN, we assess dependability
metrics for each system and subsystem in the physical and
virtual network
• We assume that dependability metrics are known for
each component of the network, including their subsystems.
• Information from real measurements and data are
available in the literature
• Depending on the chosen model, dependability metrics
may change for each new VN allocation

26. Dependability Assessment of VNs
 Virtual Network Topology Generation (R-ViNE)
– the substrate network topologies are randomly generated
using the GT-ITM tool;
– Pairs of nodes are randomly attached with probability 0.5;
 500 VN requests during the simulation time (50,000
time units) in a network substrate with 50 nodes.
– VN requests follow a Poisson process with mean λ = 4
(average of 4 VNs per 100 time units);
– Each VN follows an exponential distribution for its lifetime
with λ = 1000 (i.e., an average of 1000 time units);
– For each request, the number of virtual nodes per VN
follows a uniform distribution in the interval [2, 10].

27. Dependability Assessment of VNs
 Case Study
 mapping algorithm proposed in [3]
– "Virtual Network Embedding with Coordinated Node and
Link Mapping”, IEEE INFOCOM 2009
– The algorithm provides VN allocations in an
infrastructure provider satisfying CPU, link, and other
constraints.
– It does not assume dependability issues, which may
impact the feasibility of a given allocated VN
 We applied the resource allocation algorithm to
evaluate the dependability features for each
allocated VN

28. Dependability Assessment of VNs
 Case study (cont.)
– demonstrate the estimation of point availability (i.e.,
availability at a time t) and reliability
– assuming independent allocations and common mode
failure (CMF)
 we assume that the components are connected via series
composition
– if a component fails, the virtualized network fails

29. Dependability Assessment of VNs
 Typical MTTFs and MTTRs
Node MTTF (h) MTTR (h)
CPU 2500000 1
Hard Disk 200000 1
Memory 480000 1
Network Interface Card 6200000 1
Operating Systems 1440 2
Virtual Machines (VM) 2880 2
VM Monitor 2880 2
Switch/Router 320000 1
Optical Link 19996 12

30. Dependability Assessment of VNs
 VN net0 has a lower availability level, when CMF is
assumed
 the algorithm could avoid overload in some links
and nodes with smaller MTTFs

31. Dependability Assessment of VNs
 Availability measures for the sampled VNs are very
similar
– In more complex environments, dispersion metrics can
vary significantly

32. Extensions to the resource allocation algorithm
 Mapping algorithm might have to take into account
one or more dependability measures
– To meet strict requirements
 For instance, a Service Provider can require an availability
of 0.95 and minimum reliability of 0.99 during the lifetime
of a certain VN.
 Allocation alternatives
– to minimize the impact on availability and reliability of
previously defined VNs
– to improve the dependability measures of a new VN
allocation

33. CONTRIBUTIONS AND FUTURE
WORK

34. Contributions and Future Work
 Contributions
– an approach for dependability modeling and evaluation
of virtual networks using a hybrid modeling technique
that considers representative combinatorial and state-
based models.
– The proposed approach provides a basis for estimating
dependability metrics, such as reliability and availability,
which we consider important for heuristics dealing with
resource allocation in VNs

35. Contributions and Future Work
 Future Work
– analysis of fault-tolerant techniques to improve
dependability levels
 when the ordinary components are not able to achieve the
required service level
– formulate an efficient optimization model in the way that
dependability metrics can be handled as range of values