Huawei ELTE2.3 System Reliability Prediction Technical White Paper

Technical White Paper for System Reliability Prediction of eLTE 2.3

INTERNAL

Product Name Confidentiality

eLTE Confidential

Product Version 22 pages in total

V2.3

Technical White Paper for System Reliability

Prediction of eLTE 2.3

(For internal use only)

Prepared by Liu Hao (employee ID: 00273140) Date 2014-06-03

Reviewed by Date

Approved by Date

Huawei Technologies Co., Ltd

All rights reserved.


INTERNAL

2014-06-20 Huawei confidential. No spreading without permission. Page 2 of 22

Change History

Date Issue Description Author

2014-06-20 1.01 Added the eCNS610-related

information.

Liu Hao (employee ID:

00273140)

2014-06-03 1.00 Completed the draft. Liu Hao (employee ID:

00273140)


INTERNAL


Technical White Paper for System Reliability

Prediction of eLTE 2.3 Key words:

eLTE 2.3, reliability, eCNS600, eCNS610, DBS3900

Abstract:

This document describes the methods for calculating the reliability indicators of the network elements (NEs)

in the eLTE 2.3 solution.


INTERNAL


1 Reliability Modeling

The reliability modeling involves the following steps:

1. Defining functions and physical components

2. Defining faults

3. Defining fault properties

4. Constructing reliability models

5. Determining the number of simulations and the life cycle

6. Determining assumptions in reliability prediction

1.2 Defining Functions and Physical Components

The first step for calculating system reliability is to determine the physical components used for reliability

modeling. Not all components in the system need to be incorporated in reliability modeling. The

components that do not impact key services, such as maintenance terminals, can be ignored in reliability

modeling if not required. Component selection for reliability modeling also depends on actual applications.

For example, if the voice broadcast system is used for management assistance, which is not a key service,

the components in the system can be ignored in reliability modeling. However, if the voice broadcast

system is involved in security tracing, the components need to be incorporated in reliability modeling.

In system-level reliability modeling, typical components include the eCNS, eNodeB, terminals involving

security services (for example, a vehicle-mounted terminal for train control), transmission devices, antenna

feeder devices, and power supply modules. Sometimes, a mobile terminal is ignored in system-level

reliability modeling, but its reliability can be independently calculated.

1.3 Defining Faults

System reliability indicators have a close relationship with system fault definitions. Different fault

definitions lead to different calculation results of reliability indicators.

When system or subsystem faults are defined for NEs or boards working in redundancy mode, the

redundancy type needs to be considered. For example, an eNodeB fault is determined for an eNodeB with

three carriers working in 2+1 backup mode only when two or more carriers are faulty. For a co-site network

hosting two eNodeBs working in 1+1 backup mode, a fault in only one eNodeB is not considered as a

system fault.

However, the duration for active/standby switchover needs to be considered in the preceding situations. For

details, see section 1.4 "Defining Fault Properties."

Besides hardware faults, other fault types may be involved in a project, such as software faults, faults

caused by human errors, and faults caused by external factors (for example, lightning stroke and violent

damage).

1.4 Defining Fault Properties

Common fault properties are defined as follows:

Failure distribution curve, failure rate, and mean-time-to-failure (MTTF)/mean time between failures

(MTBF)

Mean time to repair (MTTR) and definition of repairs and spare parts

Fault impact


INTERNAL


Failure Distribution Curve, Failure Rate, and MTTF/MTBF

A failure rate is the average number of failures for a component per unit of time after the component begins

to fail. Generally, a failure rate is expressed as . Accordingly, the failure rate function is expressed as (t)

because the failure rate is a function of the time t. The failure rate function corresponds to the failure

distribution curve, the value of which may vary according to the component. The failure rate for an

electronic component within its life cycle is generally a constant, and exponential distribution is used for

such a failure rate. The failure rate for a mechanical component, however, varies depending on the time

segment. Weibull distribution is generally used for such a failure rate.

MTTF indicates the average interval at which a fault occurs on a component. MTTF is a reciprocal of a

failure rate expressed by exponential distribution.

To calculate the MTTF of a board to determine the reliability of the board, the failure distribution curve (or

failure model) must be first determined, and then the failure rate can be determined.

MTBF indicates the average time between consecutive failures of a component, which can be calculated in

the following formula: MTBF = MTTF + MTTR.

MTTR and Definition of Repairs and Spare Parts

MTTR is closely related to service availability. The value of MTTR equals the sum of Mean Active Repair

Time (MART) and Mean Logistical and administrative Delay Time (MLDT). MART is related to factors

such as architecture design, fault management design, and board startup time and is the intrinsic reliability

feature of components, while MLDT is the mean delay time to ensure network maintenance, such as the

time spent on personnel dispatch, transportation, and acquisition of spare parts. MLDT is related to the

staffing and equipment for the maintenance team.

The definition of repairs and spare parts involves resources and spare parts required for a repair. With the

definition, the number of spare parts in the system and the maintenance cost for the life cycle of a project

can be calculated. The transportation of spare parts also affects MTTR.

Fault Impact

The fault impact determines the impact of a fault on the functions and subsystem. The impact can be

classified into high, medium, and low levels. The impact of a fault that affects the entire system and causes

a failure to provide services is a high-level impact. If a fault leads to short-period service interruption in a

specific area served by multiple eNodeBs, the impact of the fault is a medium-level impact. A low-level

impact indicates short-period service interruption for a single service channel caused by, for example, ring

topology switchover after transmission interruption. An example of a low-level impact is 2s voice service

interruption. The fault impact is defined so that it can be used with the failure rate to determine the list of

critical items in the system, which facilitates the availability optimization of the entire system.

1.5 Constructing Reliability Models

Huawei uses reliability block diagrams (RBDs) to construct systematic reliability models for analysis and

calculation of reliability indicators.

The structure of an RBD indicates the logical relationships of faults in a system. Each block indicates a

fault of a component, a subsystem fault, or an event that affects the system fault. As for a subsystem fault,

the structure of another RBD can be used to indicate its internal logical relationships. The logical flow of an

RBD starts from the input on the left and ends at the output on the right. Between the input and the output

of the RBD, multiple blocks are arranged in serial or parallel connections depending on the characteristics

of a system.

A system with serially connected blocks indicates that a fault in any component will result in a system failure.


INTERNAL


Figure 1-1 System with serially connected blocks

Block 1 Block 2 Block 3

A system with parallel connected blocks indicates that the components work in redundancy mode.

Figure 1-2 System with parallel connected blocks

Block 2

Block 1

Block 3

A complex system consists of both serial and parallel connections, indicating that serially and parallel

connected subsystems exist in the system.

An RBD can also indicate the redundancy relationship of a decision system (k out of n). As indicated by

the number 2 in Figure 1-3, at least two paths of the three parallel paths must work properly. When two

paths are faulty, the system is faulty.

Figure 1-3 Decision system

Block 2

Block 1

Block 3

2

An RBD can also be used to analyze common cause failures. The common cause failure indicates a fault

that can lead to failures of multiple functional components. If there is a common cause failure in a system

with redundancy design, the failure must be expressed as a block serially connected to other blocks in an

RBD. As shown in Figure 1-4, blocks 1 and 2 are failures of independent components working in

redundancy mode, and the failure expressed by block 3 can lead to simultaneous failures of the two

components.


INTERNAL


Figure 1-4 System with a common cause failure

Block 1

Block 2

Block 3

1.6 Determining the Number of Simulations and the Life Cycle

The number of simulations and the life cycle are involved as two important parameters in the calculation of

the system unavailability based on the reliability model and fault model. The number of simulations is the

number of times the system reliability model runs in simulation mode. Each time a system reliability model

runs in simulation mode, the system determines whether it stably runs for a specified period. The specified

period is the life cycle. For example, if the number of simulations is 1000, the life cycle is one year, and the

number of times a fault occurs within the life cycle of the entire system calculated based on the system

reliability model is 50, the system unavailability is 5%.

1.7 Determining Assumptions in Reliability Prediction

1.7.1 MTTR

MTTR is defined depending on the actual maintenance capability in a project. For example, the in-transit

time for the repair of eNodeBs in a remote area is different from that for the repair of eNodeBs near a city.

There is an assumption for the calculation of MTTR for each NE. For details, see the reliability prediction

report of each NE.

1.7.2 Software Availability

Software reliability is the capability that the software possesses to implement the required functionality

under specified conditions within a specified period. Software reliability can be measured by availability.

Software availability is the probability for the software not to cause system failures under specified

conditions within a specified period. Software reliability and hardware reliability have many differences,

which are caused by the fault mechanisms of software and hardware. Therefore, software reliability is

assessed differently from hardware reliability.

No mature quantitative measure is available for the analysis of software availability in the industry.

Therefore, software availability is excluded from the reliability models in this document. If software

availability needs to be considered in a project, an assumed target value can be given to an associated NE.

1.7.3 External Factors

External factors are not considered in the reliability models in this document.

In some projects, however, some external factors need to be considered as required. For example, the

probability of optical fiber faults caused by rodents need to be considered in areas where no rodent-free

measure is used for optical fibers. In areas flooded with violence, the probability of violent damages to the

outdoor equipment needs to be considered.

Lightning protection measures are taken for Huawei outdoor eNodeBs, and therefore the probability of damages caused by lightning stroke can be ignored.


INTERNAL


1.7.4 Human Factors

Human factor analysis or human error analysis is used to measure the impact of human errors on system

availability. Human errors are errors leading to system faults caused by misoperations of system operation

personnel. Humans are flexible in executing tasks. Humans can handle exceptions and faults at any time,

but human errors may also be introduced. Human errors mainly include the failure to execute a specified

function, incorrect execution of a specified function, and execution of an unspecified function.

Human errors are not considered in reliability models in this document. In actual projects, the system

operation behaviors of humans can be determined based on the application scenario of the system to

analyze the impact of human errors so as to calculate the probability of human errors that may lead to

system faults. A large number of methods can be used to analyze the probability of human errors, among

which there is a simple method that mainly depends on the judgment of experts, that is, human error

assessment and reduction technique (HEART).

2 Reliability Prediction Methods

2.1 Board Reliability Prediction Methods

2.1.1 Component Reliability Prediction Method

The formula for calculating the component failure rate is as follows:

TiSiQiGiSSi

where,

Gi is the generic steady-state failure rate for the ith component.

Qi is the quality factor of the ith component.

Si is the stress factor of the ith component.

Ti is the steady-state temperature factor for the ith component under normal working temperature.

Under 40C and 50% stress, S equals T, which has a value of 1.0, and therefore the formula can be simplified as:

Ssi = Gi Qi

2.1.2 Method for Calculating the Board Failure Rate

The board failure rate is the sum of the component failure rates and can be calculated in the following

formula:

n

i

SSiiESS N1

where,

n is the number of component types.

Ni is the number of components of the ith component type.

E is the board environment factor. For the fixed and controlled environment on the ground, E has a value of 1.0.


INTERNAL


2.2 System Reliability Prediction Methods

The calculation of system reliability depends on the system fault definitions to a large extent. Different

system fault definitions lead to use of different algorithms. For example, some board faults in the system

lead to a failure of the entire system, whereas some board faults lead to unavailability of only some

functions in the system. According to Bellcore SR-TSY-001171, there are two kinds of system-level faults:

total system downtime (TSD) and partial system downtime (PSD).

2.2.1 System Fault Definitions and Formula

TSD is an approximate time period during which the entire system breaks down and fails to handle any

requests. TSD is generally expressed as minutes per year.

PSD is the weighted mean of the downtime during which the system only partially fails. The weight factor

is the number of lines impacted by a specific failure mode.

The calculation formulas for the TSD and PSD are as follows:

Ti

LiNTNL

iDTSD

Li NL

Lii NLDPSD0

))/((

where,

Li is the number of subscriber lines impacted by failure mode i.

Ti is the number of trunk lines impacted by failure mode i.

NT is the total number of trunk lines in the system.

NL is the total number of subscriber lines in the system.

Di is the predicted downtime of failure mode i, expressed as minutes per year.

2.2.2 Calculation of Di

According to the preceding formulas, the calculation of system reliability or system availability depends on

the calculated Di value of each board.

The impact of a failure mode is first analyzed to determine which boards in the system will cause TSD and

which will cause PSD. Then, the Di values of these boards are calculated.

During Di calculation, boards with redundancy and boards without redundancy are differentiated.

Calculation of Di for Boards Without Redundancy

The availability of a board without redundancy can be calculated in the following formulas according to the

previously calculated failure rate and determined recovery rate (reciprocal of MTTR):

Availability (A) = MTBF/(MTBF + MTTR)

Downtime (Di) = 525,600 x (1-A) (expressed as minutes/year)

Failure rate (1 Failures In Time or FIT) = 10-9

/hour


INTERNAL


Calculation of Di for Boards with Redundancy

The availability of boards with redundancy can be calculated by using the Markov model for repairable

products.

Boards with redundancy can be differentiated by work mode: active/standby backup and load sharing. The

availability of the two types of boards with redundancy can be calculated in one of the following formulas:

The availability of boards working in N+1 backup mode can be calculated as follows:

})1()1()]1(1[{1 NaCNaNNaCA NN

The availability of boards working in N+1 load sharing mode can be calculated as follows:

)}1()1()1()]1(1[{1 1 NaCNaNNaCA NAN

A

where,

A is the availability of boards working in N+1 redundancy mode.

C is the switchover ratio or the probability of successful switchover times.

CA is the probability of successful switchover times for the active board.

CS is the probability of successful switchover times for a standby board.

CA x CS indicates the switchover ratio of boards working in active/standby backup mode.

CA indicates the switchover rate of the boards working in load sharing mode.

a is the availability of each board working in N+1 redundancy mode, which can be calculated using

MTBF/(MTBF+MTTR).

Di can be calculated based on the availability.

In actual applications, the calculation of Di is complicated with many conditions considered. For example,

the backup mode can be warm backup or hot backup, and the time for an active/standby switchover cannot

be calculated using a simple formula. The lifetime distribution of different components can neither be

calculated using a simple formula.

To accurately calculate the system availability, Huawei adopts professional reliability simulation software.

The software creates system reliability models based on RBDs and then analyzes the system availability

and reliability based on Monte-Carlo simulation to simulate the availability indicators close to the actual

projects.

2.3 Other Relevant Parameters

The MTTR mentioned in this document refers to the onsite repair time and does not include the time

required for personnel transfer or logistics.

In this document, the MTTR of each board and equipment is determined to be 1, 2, 4, or 24 hours

according to the MIL-HDBK-472, engineering experience, and field data.

In addition, according to the reliability engineering baseline of Huawei, the failure detection rate of active

boards is 95%, the failure detection rate of standby boards is 90%, and the switchover success rate is 99%.

3 eCNS600 Configuration and Reliability Prediction

3.1 eCNS600 Reliability Prediction Models

eCNS600 reliability models can be constructed based on boards configured with or without redundancy.


INTERNAL


3.1.1 eCNS600 Reliability Model Constructed with Boards Configured Without Redundancy

PEM EPC FAN

SMU

SMU

SDM

SWU

SWU

SWI OMU USI

ISU OXI

3.1.2 eCNS600 Reliability Model Constructed with Boards Configured with Redundancy

PEM

PEM

FPC

FPC

FAN

FAN

SMU

SMU

SWU

SWU

SWI

SWI

OMU

OMU

SDM

SDM

USI

USI

ISU

ISU

QXI

QXI

3.2 Typical Configurations of eCNS600 Reliability Models

3.2.1 Typical Configuration of eCNS600 Boards Without Redundancy

Board/Module Description Quantity

PEM Power Entry Module 1

FPC Flexible Printed Circuit 1

FAN Fan 1

SMU Service Management Unit 2

SDM Service Data Management 1

SWU Switch Unit 2


INTERNAL



SWI Switch Interface Unit 1

OMU Operation and Maintenance Unit 1

USI Universal Service Interface 1

ISU Integrative Session Unit 1

QXI QUAD 10GE Interface Unit 1

3.2.2 Typical Configuration of eCNS600 Boards with Redundancy


PEM Power Entry Module 2

FPC Flexible Printed Circuit 2

FAN Fan 2

SMU Service Management Unit 2

SDM Service Data Management 2

SWU Switch Unit 2

SWI Switch Interface Unit 2

OMU Operation and Maintenance Unit 2

USI Universal Service Interface 2

ISU Integrative Session Unit 2

QXI QUAD 10GE Interface Unit 2

3.3 eCNS600 Board Reliability Indicators

Board/Module Failure Rate (FITs) MTBF (Hours) MTBF (Years)

OMU 2219.61 450529.60 51.43

USI 403.19 2480220.24 283.13

ISU 1973.01 506839.80 57.86

QXI 403.19 2480220.24 283.13

SMM 683.53 1462993.58 167.01

SDM 101.1 9891196.83 1129.13

SWU 2357.99 424090 48.41


INTERNAL



SWI 518.08 1930203.83 220.34

PEM 487 2053261.6 234.4

FAN 1000 1000000 114.2

FPC 272.7 3667033.4 418.6

3.4 eCNS600 Reliability Prediction

3.4.1 Reliability Prediction for eCNS600 Boards Without Redundancy

MTBF (Years) MTTR (Hours) Availability Interruption Duration (Minutes/Year)

15.73 1 99.99928% 3.81

15.73 2 99.99854% 7.62

3.4.2 Reliability Prediction for eCNS600 Boards with Redundancy


40.0 1 99.99972% 1.50

40.0 2 99.99943% 3.00

4 eCNS610 Configuration and Reliability Prediction

4.1 eCNS610 Reliability Prediction Models

Figure 4-1 eCNS610 reliability prediction model with eight hard disks, fans working in 7+1 backup mode, and power supply modules working in 1+1 backup mode

Mother

board

RAID

controller

card

CPU I/O card

Backplane

connecting

hard disks

Hard disk 1

Hard disk 8

...

Fan 1

Fan 8

...

Power supply

module 1

Power supply

module 2

8 7

Memory 1

Memory 24

... 24


INTERNAL


4.2 Typical Configurations of eCNS610 Reliability Models

Board/Module Quantity

Mother board 1

RAID controller card 1

CPU 1

Memory 24

I/O card 1

Backplane connecting hard disks 1

Hard disk 8

Fan 8

Power supply module 2

4.3 eCNS610 Board Reliability Indicators


Mother board 3721.0 268744.96 30.7

RAID controller card 559.2 1788268.96 204.1

CPU 40.0 25000000 2853.8

Memory 5000.0 200000 22.83

Backplane connecting hard disks 82.9 12062726.2 1377.0

I/O card 82.6 12106537.5 1382.0

Fan 582.0 1718213.06 196.1

Hard disk 3425.0 291970.80 33.33

Power supply module 2000.0 500000 57.1

4.4 eCNS610 Reliability Prediction


12.29 1 99.999071% 4.88

12.29 2 99.998143% 9.76


INTERNAL


5 DBS3900 Configuration and Reliability Prediction

A DBS3900 consists of two independent modules: baseband (BB) module and radio frequency (RF)

module, which are connected over the common public radio interface (CPRI) through an optical cable.

5.1 BBU3900/BBU3910

The BBU3900/BBU3910 is an indoor baseband unit, which fits into 2U space of any standard 19-inch

cabinet. The BBU3900/BBU3910 occupies small space and can be easily installed.

The BBU3900/BBU3910 includes boards such as UMPT, UPEU, and UBFA and is fed with 48 V DC

power.

5.2 RRU3251/RRU3232/RRU3252/RRU3253

The RRU3251, RRU3232, RRU3252, and RRU3253 are remote radio units used outdoors, which can be

installed on a pole or a wall near the antenna.

Figure 5-1 RRU

5.3 DBS3900 Reliability Prediction Models

5.3.1 DBS3900 Reliability Model Constructed with Boards Configured Without Redundancy

Figure 5-2 DBS3900 reliability model 1

UPEUc FANc UMPT LBBP

RRU

RRU

...


INTERNAL


5.3.2 DBS3900 Reliability Model Constructed with UPEUs and LBBPs Configured with Redundancy


UPEUc

UPEUc

FANc UMPT

LBBP

LBBP

RRU

RRU

...

5.3.3 DBS3900 Reliability Model Constructed with UMPTs Configured with Redundancy


UPEUd

UPEUd

FANd

UMPT LBBP

LBBP

RRU

RRUUMPT

...

5.4 Typical Configurations of DBS3900 Reliability Models

5.4.1 Typical Configuration 1 for the DBS3900


UPEUc Power and Environment interface unit 1

FANc BBU Fan Module 1

UMPT Main Processing & Transmission unit 1

LBBPd2 Baseband Process and Radio Interface unit 1

RRU3251 Remote Radio Unit 1-3





INTERNAL






RRU3232/RRU3252/RRU3256 Remote Radio Unit 1-3























INTERNAL











UPEUd Power and Environment interface unit 1

FANd BBU Fan Module 1

















INTERNAL



























INTERNAL


5.5 DBS3900 Board Reliability Indicators


UPEUc 380 2631578.95 300.4

UPEUd 560 1785714.29 203.8

FANc 940 1063829.79 121.4

FANd 950 1052631.58 120.1

UMPT 1550 645161.29 73.6

LBBPd 1120 892857.14 101.9

RRU3253/3259 5710 175131.35 19.9

RRU3251 2460 406504.07 46.4

RRU3232/RRU3252/RRU3256 3100 322580.65 234.4

5.6 DBS3900 Reliability Prediction

5.6.1 Reliability of the DBS3900 Configured in Mode 1


28.99 1 99.99961% 2.07

28.99 3 99.99882% 6.21



28.90 1 99.99960% 2.08

28.90 3 99.99881% 6.23



46.20 1 99.99998% 1.30

46.20 3 99.99926% 3.90


INTERNAL




46.12 1 99.99997% 1.30

46.12 3 99.99926% 3.90



123.54 1 99.999908% 0.48

123.54 3 99.999723% 1.44



122.22 1 99.999907% 0.49

122.22 3 99.999720% 1.47



27.77 1 99.999589% 2.16

27.77 3 99.998767% 6.48



27.54 1 99.999585% 2.18

27.54 3 99.998756% 6.54



46.00 1 99.999752% 1.30


INTERNAL



46.00 3 99.999255% 3.90



45.86 1 99.999751% 1.31

45.86 3 99.999253% 3.92



121.03 1 99.999906% 0.49

121.03 3 99.999717% 1.48



120.61 1 99.999905% 0.50

120.61 3 99.999716% 1.50

Documents

Huawei ELTE2.3 System Reliability Prediction Technical White Paper