A Dynamic Simulation Approach to Business Continuity of Wireline and Wireless Networks with Cross-Industry. Infrastructures. Gerard P. O'Reilly, Huseyin ...
A Dynamic Simulation Approach to Business Continuity of Wireline and Wireless Networks with Cross-Industry Infrastructures Gerard P. O’Reilly, Huseyin Uzunalioglu, David J. Houck, Thomas B. Morawski
Bell Labs, Lucent Technologies, Holmdel, N. J. USA
Abstract—Critical national infrastructures for transportation, power, finance, and other basic industries rely heavily on information and telecommunications networks (voice, data, Internet) to provide services and conduct business. While these networks tend to be highly reliable, disasters may lead to extended outages requiring days/weeks to repair. These outages cause loss of business continuity and financial transaction failures. This paper describes a dynamic simulation model of communications network disasters, their network performance, and their impact on other critical infrastructures.
simulation code that runs fast enough even for large networks and traffic loads.
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INTRODUCTION
Governments are now investing more resources than ever to reduce the likelihood of disasters occurring, as well as to mitigate the impact when they do occur. Disasters fall into three broad categories: natural (floods, earthquakes, and fires), accidental (undetected software bugs), and sabotage (intentional disaster). A disaster from any of the categories can cause dramatic changes in the loads placed on the infrastructures delivering the services that our economy and our personal lives depend on. Hence it is important to understand how these infrastructures get affected as a result of disasters. Sandia National Laboratories (SNL) and Bell Labs are collaborating on analysis of telecommunications infrastructures and their interdependencies with other infrastructures. SNL has been developing system dynamics simulations to capture the normal and extraordinary performance of cross-industry infrastructures. These simulation capabilities will permit analysis of disaster scenarios, the extent of outages, economic impacts, and loss of life. In this paper, we introduce a model for the telephony infrastructure for metropolitan and national service areas. Our goal is to develop a baseline functional model of the telecom system and then study the behavior in the presence of telecom and/or other infrastructure system failures and overloads. Our modeling approach is fundamentally different than other communications network modeling approaches. The difference arises from the goals of the respective studies. Traditional network models are focused on finer time-scales since the goal is to understand what happens during normal operation of the network and how to optimize its performance. Our model, however, represents a telephony network over a wide time-scale ranging from hoursto days, and even to weeks and months. As a result, our model focuses on longer time-scale issues, while removing features that are not critical to understand performance under stress conditions. This change in the modeling paradigm produces
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TRADITIONAL TELEPHONY SIMULATION
The Network-Simulation Modeling and Analysis Research Tool (N-SMART) has been developed to support detailed wireline and wireless network simulations [1]. It studies telecom network readiness and traffic behavior on various disaster scenarios. It can create scenarios that demonstrate the pattern of telecom traffic loads and user behavior during the time of disaster, such as physical line disconnection and switching system failure. N-SMART is a discrete event (call level) telecom model that simulates capacities, blocking levels, retrials, and time to complete calls for both wireline and wireless networks. It models various network infrastructures, traffic load profiles, simulation scenarios, network management controls, and simulation engine parameters as inputs. Various simulation scenarios analyze how different traffic patterns, traffic loads, user behaviors, and disaster severities impact network performance and recovery. By analyzing the results of simulations, the tool shows how different telecom elements, such as bandwidth deficiency, switch processor overload, and user behavior, impact the performance of the network and its robustness. Fig. 1 depicts the building blocks of the N-SMART-Voice simulation model, information input, and model output [1]. Simulation Engine Parameters
Network model
Switching Network Infrastructure
INPUTS
Keywords-reliability, business continuity, disaster recovery
Section 2 discusses the traditional simulation model; Section 3 discusses the new dynamic simulation model. Sections 4 and 5 apply the model and show some example results.
Traffic Load and Profile Network Failure Scenarios Traffic Overload Scenarios
Simulation Engine -------------------------
Call model Routing model Reattempt model Switch processor model Simulation Output Analysis
Network Management Controls Performance Metrics
System State
OUTPUTS
Fig. 1. N-SMART Model
The simulation engine consists of algorithms to generate events related to calls, re-attempts, network failures, processor overloads, and simulation output. Fig. 2 illustrates the call model.
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WW Call Attempts & Reattempts
WM Call Attempts & Reattempts
MW Call Attempts & Reattempts
MM Call Attempts & Reattempts
ber of call events such as arrivals and departures occur as shown in Fig. 3.
Call Attempts
Setup -
-
Complete
Answering machine call & No Answer
Time Network Busy
Line busy
Switch
- BL
Time Steps: Compute new variables for all Vensim variables.
Net. Mant BL
Trunk BL -
-
Abandon
Fig. 3. Time intervals in Dynamic Simulation.
+
WM WW
Reattempt Reattemp t Endpoints ?
MW
The simulation state is updated at the beginning of each time interval for the aggregate call events occurring within the interval. This method trades off simulation scalability at the expense of simulation precision.
MM
Fig. 2. Call Flow Models WW: Wireline to Wireline; MM: Mobile to Mobile WM: Wireline to Mobile; MW: Mobile to Wireline
Call blocking (trunk or processor) depends on the sequence of arrival/departure events. We approximate the number of arrivals/departures/call blocking at each time-step.
First call attempts arrive at a particular switch for a certain destination switch as described by the traffic load and the traffic profile. The call is accepted and routed through the network (set up) if there are sufficient trunk and processing resources in the switch and the network. The call may be blocked if the called line is busy, or if the network is busy, which may be due to trunk blocking along the route of the call (direct route and alternate routes), switch blocking because of processor congestion, or network management blocking. If a call is blocked, the caller may abandon the call set-up request or re-attempt later on either the wireline or wireless network. Routing decisions are based on the originating and terminating switches of the call. A call is referred to as incomplete if the call attempt results in no answer, line busy, or network busy. An incomplete call may be abandoned or re-attempted. A re-attempt model based on [4] is used in the simulation. According to this model, a call would reattempt with a certain reattempt probability after an exponentially distributed reattempt time. Reattempt parameters are a function of whether the call was blocked due to a line busy or network busy. These dispositions determine how fast reattempts will be made. The reattempt model parameters [4] are shown in Table 1. Table 1 – Reattempt Model Parameters Initial Attempt Disposition Line Busy Network Busy Initial Attempt To be determined Disposition Probability 0.06 during the simulation run Retrial Probability 0.72 0.86 Mean Retrial Times (mins.) 18.2 23.9
3
Multiple arrival/departure events within each interval
DYNAMIC SIMULATION TELECOM MODEL OF WIRELINE AND WIRELESS
The Dynamic Simulation model is a time-driven simulation as compared to N-SMART, which is a call-by-call event driven simulation. We use Vensim [5] as the dynamic simulation tool. A significant difference between the two modeling approaches is that with the event-driven approach, simulation time moves with every call event. With time-driven simulations, time moves in discrete time steps, and within each time step, a num-
Arrivals + Reattempts
Departures
t + ∆t
t Calls in Progress
Calls in Progress − Departures + Admitted Calls
Reattempt Pool
Reattempt Pool − Reattempts + Fraction of Incomplete Calls
Fig. 4. Flow-Based Model
Fig. 4 shows the stocks and flows of a flow-based model. At time t the state of a switch pair is described by one or more variables (stocks). One stock is the number of calls in progress for a pair (i,j). The flows that change this stock in a time step ∆t are call departures and calls admitted in ∆t. Another stock is the reattempt pool for (i,j), a pool of incomplete calls from earlier time intervals. The flows that change this stock are reattempts drawn from the pool and a fraction of incomplete calls (that were not abandoned) and reattempts from other networks. The vertical block arrows could be interpreted as “converters” that set the rates, namely the call arrival rate, the call departure rate and the call reattempt rate. The following discussion on call arrivals, calls in progress, etc. applies to a switch pair ij. To simplify our notations, the subscript ij is suppressed. Arrivals The number of call arrivals per time step is generated from a Poisson distribution,
Pn =
(λ∆t ) n exp(− λ∆t ) n!
(1)
where Pn is the probability of n arrivals in an interval ∆t and λ is the average call arrival rate. Calls in Progress and Departures
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The calls in progress at time t are “leftovers” from previous time intervals. The probability of departure of a call that is in progress at time t in [t, t+∆t) is given by
∆t pd = 1 − exp − τh
(2)
where τh is the mean call holding time. Eq. (2) is the wellstudied negative exponential distribution. One of the interesting properties of this distribution is its lack of memory. The probability of departure of any calls in the stock is independent of how long a call has already been in progress. If np is the number of calls in progress at time t, the probability of having k departures in [t, t+∆t) is given by the binomial distribution,
n k n −k P(k ) = p pd (1 − pd ) p k
(3)
This equation handles departure of calls that are in progress at the beginning of the time interval during the time interval. However, it is also possible that some calls that arrive at the beginning of the time step depart during the time step. If the time step ∆t
