Performance Management of IT Service Processes Using a Mashup-based Approach

PERFORMANCE MANAGEMENT OF IT SERVICE PROCESSES USING A MASHUP-BASED APPROACH

Carlos Raniery Paula dos SantosSupervisor: Lisandro Zambenedetti GranvilleThesis defense – June13 rd, 2013

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Outline

• Introduction• Boundaries of the Investigation• Productivity• Reliability• Evaluation• Conclusions and Future Work

INTRODUCTION

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• The increasing complexity and importance of IT environments have been leading researchers to pay more attention to the ITSM area

• Several management frameworks have then been conceived to help IT service providers towards a coherent service management experience

1INTRODUCTIONCONTEXTUALIZATION

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• In a service management operation the presence of humans in the critical path for performing work degrades performance and quality of services

• Due to its unpredictable nature, enforcing and obtaining tight performance bounds in a human-staffed organization is burdensome task

1INTRODUCTIONPROBLEM STATEMENT

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• Automation of IT operations has been of attention for the last two decades– Ideally, a system should be self-managed without

any human intervention

• May not be feasible in some cases because of additional effort to deploy and maintain the automation infrastructure– Specially when processes are constantly changing,

are too complex to be automated, or have a limited lifetime

1INTRODUCTIONCURRENT SOLUTIONS

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• Mashups are Web applications composed from pre-existing Web resources (e.g., interactive maps, Web services, traditional HTML pages)

• Mashups can be composed by users with limited or no programming skills, to create their own Web applications– Basic operators are employed to hide, from mashup

end-users, technical details from the composition

1INTRODUCTIONMASHUPS

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Q I. What are the major causes for a poor performance in the execution of IT Service Management processes?

Hypothesis: The employment of mashups enhances the performance of human-centered ITSM processes

1INTRODUCTIONHYPOTHESIS AND FUNDAMENTAL QUESTIONS

Q II. What methods could be employed in the development of mashups aiming at performance enhancement?

Q III. How models available in the literature can be used to assess performance improvements obtained with mashups?

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BOUNDARIES OF THE INVESTIGATION

A service is a means of delivering value to customers by facilitating outcomes customers want to achieve without the ownership of specific costs and risks

Boundaries of the InvestigationITSM Scenario2

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Supplier CustomerService

Definition

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Boundaries of the InvestigationITSM Scenario2


Service Strategy

Service Design

Service Transition

Service Operation

Continual Service Improvement

Boundaries of the InvestigationInvestigated ITSM Process2

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Service Strategy

Service Design

Service Transition


Service Operation

Functions

• IT Operations Management

• Application Management• Technical management• Service Desk

Processes

• Event Management• Access Management• Problem Management• Incident Management• Request Fulfillment


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Service Strategy

Service Design

Service Transition


Service Operation

Functions

• IT Operations Management

• Application Management• Technical management• Service Desk

Processes

• Event Management• Access Management• Problem Management• Incident Management• Request Fulfillment


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Service Strategy

Service Design

Service Transition


Service Operation

Request Fulfillment

Dispatcher System Administrators

Requests


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Service Strategy

Service Design

Service Transition


Service Operation

Request Fulfillment


Requests

• Human behavior and performance are difficult to model, and consequently, to optimize

• Even if the nature of work is exactly the same, a human may execute it in a different way each time– Interrupted by external factors– Usually employ different systems– Execute multiple and complex tasks– May be overloaded

Boundaries of the InvestigationPerformance2

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• When the natural limits are reached or exceeded, human operators can become:– A bottleneck, slowing down the process execution– Even more error-prone


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Units of work performed per unit of time

Productivity

Non-defective units at the output of the process

Reliability



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Productivity


Reliability



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Productivity


Reliability

• The focus is on steps that can be measured through instrumentation or observation, and can be improved through redesign and partial automation

• The performance analysis covers the steps that a human follows to execute ITSM processes

• The unpredictable nature of external events that affect human behavior is not addressed


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PRODUCTIVITY

• Inefficiencies are portions of a service management process characterized by suboptimal execution of activities

• They can appear at different levels of analysis– Lower level: inefficiencies due to the mechanical

execution involved in performing the activity– Higher level: inefficiencies due to the complexity

of the activity itself

PRODUCTIVITYINEFFICIENCIES3

• We collected descriptions of the tasks performed by a group of operators involved in a common activity and analyzed both levels of inefficiencies– Basic: context-switching, locating data, entering data– Information Management: copy/paste, consistency

checks, information lookups– Skill-dependent: retaining information, combining

information, data transformation– Synchronization: contacting a person, becoming aware

PRODUCTIVITYINEFFICIENCIES3

• These inefficiencies can be tackled by the adoption of mashups and, more specifically, by using Mashup Patterns

• Mashup Patterns are general reusable solutions to a commonly occurring problem, which:– Provide tested and proven development

improvements– Allow mashups to be developed quickly

PRODUCTIVITYMASHUPS3


Housing Maps Chicago Crimes

Retrieve data (houses)

Retrieve data (crimes)

Display on Google maps

Display on Google maps


• Candidate mashup patterns:– Alerter: periodically monitors a system of interest

on behalf of the user and sends notifications – Importer: abstracts the different methods used to

access the external data – Transform: enables the processing of certain types

of data– Displayer: presents information from multiple

sources as independent widgets

• The Keystroke-Level Model (KLM) is used to measure lower level inefficiencies– Predicts the time an expert user takes to perform a

task on a computer system– It is based on the sequence of keystroke-level

actions the user performs

PRODUCTIVITYQUANTITATIVE MODELING3

• The Complexity Model is employed to account for the higher level inefficiencies– Evolved from a methodology for quantitative

benchmarking of configuration complexity to a model of configuration activity based on goals, procedures, and actions

– Provides a set of metrics, e.g., execution, parameter, and memory


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Total time

Human-computer interactions

Cognitive actions

Analyst Domain Expert

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RELIABILITY

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• A planned sequence of physical or mental activities that fails to obtain a result

• May be of two types:– Involuntary actions (i.e., slips and lapses): are

those that deviate from planned intentions and, thus, do not reach their goals

– Intentional actions (i.e., mistakes and violations): are performed consciously but the desired result is not achieved

RELIABILITYHUMAN ERROR4

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• We identified a set of common error-prone activities in ITSM:– Action: are actions that change the state of the

system– Retrieval: are failures to retrieve correct

information, to be used in a further step– Checking: occur when the operator fails to check

some information– Decision: occur when the operator has to make an

explicit choice between multiple alternatives– Communication: occur when the operator fails to

pass information to another person

RELIABILITYHUMAN ERROR4

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• Mashup patterns, can be used to cope with human errors by providing a set of proven and reusable solutions

• We introduce a new type of mashup basic operator, (i.e., Error Prevention modules):– Buffer: allows developers to specify for how long

time a specific action should be delayed before being executed

– Forcing: allows developers to introduce confirmation points in the process workflow

RELIABILITYMASHUPS4

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• HEART is a technique used to quantify human reliability of specific tasks– It is considered the most comprehensive method in

the field of Human Reliability Assessment– Specifies the factors that can affect human

performance, thus making it less reliable (e.g., distractions, overload)

– We employed Linguistic Variables to represent the Assessed Proportion of Affect

• Event Tree Analysis is used to determine the probability of failure in a sequence of events

RELIABILITYQUANTITATIVE MODELING4

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RELIABILITYQUANTITATIVE MODELING4

Analyst Domain Expert

Task HEP1 6.72 2.13 4.5

HEART

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EVALUATION

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EVALUATIONDISPATCH PROCESS5

Service Strategy

Service Design

Service Transition


Service Operation

Request Fulfillment


Requests

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ProblemTicket

IncidentTicket

ChangeTicket

Low-level group(Simple tickets)

High-level group(Higher complexity

tickets)

Mid-level group(Root

Analysis, complex

problems,etc.)

Incomingdemand

Segmentation bycomplexity

Work group structure basedOn ticket complexity

Dispatcher

OtherDispatcher


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1) Open Ticket(ETS)

2) Analyses if theTicket was misrouted

(ETS)

4) Forwards to otherteam (ETS)

5) Analyses the skilllevel to solve the ticket

(ETS)

7) Request for more resources(e-mail)

8) Import the ticket(ETS, ITS)

9) Searches for the SAWith the right skills and

Availability (ITS)

10) Talk with the SA(in person)

11) Makes theassignment

(ETS, ITS)

3) Is the ticket

correct?

6) Have enough

resources?

• Name• Description• Severity• Workload• Skills

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EVALUATIONMASHUP-BASED DISPATCH SYSTEM5

1) Open Ticket(ETS)


(ETS)



(ETS)




Availability (ITS)



(ETS, ITS)

3) Is the ticket

correct?

6) Have enough

resources?


Alerter Pattern{New Ticket}

Displayer Pattern{name, description}

Displayer Pattern{severity}

Displayer Pattern{workload, skills}

ImporterPattern

Transformer Pattern

Input operator{System Admin}

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1) Open Ticket(ETS)


(ETS)



(ETS)




Availability (ITS)



(ETS, ITS)

3) Is the ticket

correct?

6) Have enough

resources?


Alerter Pattern{New Ticket}

Displayer Pattern{name, description}

Displayer Pattern{severity}

Input operator{System Admin}

Displayer Pattern{workload, skills}

ImporterPattern

Transformer PatternBuffer

OperatorForcing

Operator

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• We performed a series of time measurements among five dispatchers in a service delivery center– Using a stopwatch, we took 10 time measurements

for each assignment process and its individual tasks

EVALUATIONPRODUCTIVITY ASSESSMENT5

2 3 5 6 8 9 110

30

60

90

120

150

180

Task #

Tim

e av

g. (s

econ

ds)

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• The KLM model was determined through observation of user interactions

5 EVALUATIONPRODUCTIVITY ASSESSMENT

Copy and Paste Operation Time (sec)

Decide to do the task M 1.35 sec

Change Window (mouse) P + K 1.3 sec

Copy text P + B + P + B 2.4 sec

Paste text P + K + H + K + K 1.9

Ttask8 = M + Fields * CopyPaste = M + Fields * (M + Change Window + CopyText + ChangeWindow + PasteText )= 1.35 + 10 * (1.35 + 1.3 + 2.4 + 1.3 + 1.9) = 83.85 sec

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• The complexity time can be obtained by subtracting the time predicted by the KLM model

1 2 3 5 6 8 9 110

20

40

60

80

100

120

140

160Complexity

KLM

Task #

Tim

e (S

econ

ds)

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• By interviewing dispatchers, it was possible to determine values for all the memory and decision metrics

EVALUATIONPRODUCTIVITY ASSESSMENT5

1 2 3 5 6 8 9 110

2

4

6

8

10

12

Decision

Memory Size

Memory Latency

Memory Depth

Task #

Com

plex

uty

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• It’s required to evaluate how the model’s quality changes as new metrics are included in the evaluation

Step Added Metric R2 RMSE

1 Decision 0.01 39.09

2 Memory size 0.94 24.78

3 Memory latency 0.96 4

4 Memory depth 0.84 5.04

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Step Added Metric R2 RMSE

1 Decision 0.01 39.09

2 Memory size 0.94 24.78

3 Memory latency 0.96 4

4 Memory depth 0.84 5.04

• It’s required to evaluate how the model’s quality changes as new metrics are included in the evaluation

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• It was possible to predict the time (spend at each task) associated with the complexity encountered in carrying out the tasks of the assignment process

• The model can explain 96% of the time variability

1 2 3 5 6 8 9 110

10

20

30

40

50

60

70ComplexityReal

Task #

Tim

e (s

econ

ds)

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• Once the time measures are estimated, it’s possible to evaluate productivity enhancement by using the mashup’s technology


1 2 3 5 6 8 9 110

20

40

60

80

100

120

140

160Real measurements (without mashups)Predicted by model (without mashups)Predicted by model (with mashhups)

Task #

Tim

e (S

econ

ds)

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• It was possible to identify the most common failures in dispatch

• The number of human errors accounted is 10 for each 150 executions (~6.6%)

5 EVALUATIONRELIABILITY ASSESSMENT

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EPCs HEART effect

Assessed proportion of effect

Assessed effect

Channel overload x 6 Medium 3.51

Suppressing information x 9 Medium 5.02

No veracity checks x 2 Low 1.18

No means of conveying info x 8 Low 2.28

• Once the process workflow and root causes of problems are determined, the next step is the determination of Human Error Probability (HEP) of each activity in the workflow

Task 5: Analyses the skill level to solve the ticket


HEP[final] = HEP * (3.51 * 5.02 * 1.18 * 2.28)= 0.0004 * 47.41= 0.019 = 1.9%

Failure Task HEP

Accepts misrouted ticket 3 0.002391857

Underestimates skills 5 0.019094864

Imports wrong info 8 0.022549899

Fails to detect SA 9 0.019094864

Wrong assignment 11 0.002391857 P[failure] = 0.06401= 6.4%

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• The new interaction elements and mashup patterns are feasible mechanisms to reduce the occurrence of human errors


EPCs HEART effect

Assessed proportion of effect

Assessed effect

Suppressing information x 9 Low 2.46

No veracity checks x 2 High 1.18

No means of conveying info x 8 Low 2.28

Task 5: Analyses the skill level to solve the ticket (after mashup usage)

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Failure Task HEP

Accepts misrouted ticket 3 0.00091324

Underestimates skills 5 0.00412552

Imports wrong info 8 0.00000000

Fails to detect SA 9 0.00091324

Wrong assignment 11 0.00476030

HEPfinal = HEP * (2.46 * 1.83 * 2.28)= 0.0004 * 10.31= 0.0041 = 0.41%

Pfailure = 0.011004691 = 1.1 %

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• The results show the reduction from 6.4% to 1.1% in the probability of human error


1 2 3 4 50

0.005

0.01

0.015

0.02

0.025Without mashups With mashups

Failure #

HEP

valu

es

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• The Pearson product-moment correlation r indicate a fair and positive relationship among the variables

5 EVALUATIONCORRELATION ANALYSIS

0 50 100 150 200 250 3000.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%MashupsWithout Mashups

Execution Time (seconds)

Cum

ulati

ve E

rror

Pro

babi

lity

(per

cent

age)

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CONCLUSIONS AND FUTURE WORK

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• Improvements in productivity and reliability provided by the application of mashups demonstrate its viability as a means for improving IT Service Management

• The combined model allowed us to tackle lower level inefficiencies of human-computer interactions, and higher level inefficiencies of performing IT tasks and subtasks

6 CONCLUSIONS AND FUTURE WORKFINAL CONSIDERATIONS

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• The good fit between the real measurements with the times predicted by the combined model indicates its feasibility in predicting labor costs savings

• The presented patterns and interaction elements avoided the occurrence of action and retrieval errors


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• Inefficiencies due to the complexity and mechanical execution involved in performing the activity.

• Four groups of inefficiencies: basic, information management, skill-dependent, and synchronization.

• Errors can occur due to: attention span, memory, situation awareness, and personal resources.

• Five common error-prone activities: action, retrieval, checking, decision, and communication errors.




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• KLM provides a wealth of detail at the lower level human-computer interactions, while the Complexity Model addresses both levels of inefficiencies.

• HEART can be used to quantify the human reliability of individual tasks. ETA was used to evaluate the overall human error probability of ITSM processes.


Q II. How models available in the literature can be used to assess performance improvements obtained with mashups?


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• Mashup patterns to tackle inefficiencies and error-prone activities in service management processes: Alerter, Importer, Transform, and Displayer (SANTOS et al., 2011).

• A new type of interaction elements, called Error Prevention modules (SANTOS et al., 2013).


Q III. What methods could be employed in the development of mashups aiming at performance enhancement?


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• Validation of mashup patterns through additional supporting cases

• Investigate financial models to assess loss due to broken SLAs

• Automation of mashup’s development• Inclusion of confidentiality mechanisms in the

mashup’s development• Investigation of rollback and exception handling in

the mashup composition logic

6 CONCLUSIONS AND FUTURE WORKNEXT STEPS

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• 2009:– Rafael Santos Bezerra, Carlos Raniery Paula dos ; Leandro

Márcio Bertholdo ; Lisandro Zambenedetti Granville ; Liane Margarida Rockenbach Tarouco . Um Sistema de Gerenciamento de Redes Baseado em Mashups . SBRC 2009

– Rafael Santos Bezerra, Carlos Raniery Paula dos ; Leandro Márcio Bertholdo ; Lisandro Zambenedetti Granville ; Liane Margarida Rockenbach Tarouco . Um Sistema de Gerenciamento de Redes Baseado em Mashups . Revista Brasileira de Redes de Computadores e Sistemas Distribuídos (RESD), v. 2, 2009.

6 CONCLUSIONS AND FUTURE WORKPUBLICATIONS

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• 2010:– Rafael Santos Bezerra, Carlos Raniery Paula dos ; Lisandro

Zambenedetti Granville ; Liane Margarida Rockenbach Tarouco . On the Feasibility of Web 2.0 Technologies for Network Management: A Mashup-Based Approach . 12 th Network Operations & Management Symposium (NOMS), 2010, Osaka, Japan

– Carlos Raniery Paula dos Santos, Rafael Santos Bezerra, João Marcelo Ceron, Lisandro Zambenedetti Granville, Liane Margarida Rockenbach Tarouco. On Using Mashups for Composing Network Management Applications . IEEE Communications Magazine, Vol. 48, Issue 12, December 2010

– Carlos Raniery Paula dos Santos, Rafael Santos Bezerra, João Marcelo Ceron, Lisandro Zambenedetti Granville, Liane Margarida Rockenbach Tarouco. Botnet Master Detection Using a Mashup-based Approach . 6th IEEE International Conference on Network and Service Management (CNSM) , 2010, Niagara Falls, Canada


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• 2011:– Carlos Raniery Paula dos Santos, Rafael Santos Bezerra, João

Marcelo Ceron, Lisandro Zambenedetti Granville, Liane Margarida Rockenbach Tarouco. Identifying Botnet Communications Using a Mashup-based Approach . LANOMS, 2011, Quito, Equador

– Carlos Raniery Paula dos Santos, Winnie Cheng, Rafael Santos Bezerra, Lisandro Zambenedetti Granville, Nikos Anerousis. A Data Confidentiality Architecture for Developing Management Mashups. 12th IFIP/IEEE International Symposium on Integrated Network Management , 2011, Dublin, Ireland

– Carlos Raniery P. dos Santos, Lisandro Zambenedetti Granville, Winnie Cheng, David Loewenstern, Larisa Shwartz, Nikos Anerousis. Performance Management and Quantitative Modeling of IT Service Processes Using Mashup Patterns . 7th International Conference on Network and Services Management (CNSM 2011), 2011, Paris, France


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• 2012:– Carlos Raniery Paula Dos Santos, Lisandro Zambenedetti

Granville, Nikolaos Anerousis, David Matthew Loewenstern, Louis Jonh Percello, Winnie Cheng, Larisa Shwartz. Performance Management and Quantitative Modeling of IT Service Processes Using Mashup Patterns . US Patent Pending


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• 2013:– Carlos Raniery Paula dos Santos, Lisandro Zambenedetti

Granville, Larisa Shwartz, Nikos Anerousis, David Loewenstern. Quality Improvement and Quantitative Modeling – Using Mashups for Human Error Prevention . 13th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2013), 2013, Ghent, Belgium


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• Virtual Nodes Monitoring based on Mashup – This paper provides a mashup-based

mechanism to monitor virtualized networks.

6 CONCLUSIONS AND FUTURE WORKNEXT PUBLICATIONS

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• A Mashup-based Solution for Botnet Mitigation – This paper proposes a modular architecture

based on the dynamic integration of pre-existing tools to achieve a more efficiently detection solution.


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• Survey– This work aims at gathering and organizing

the knowledge about mashups properties applied to multiple management scenarios


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• Automation of mashups’ development– Considering that the ITSM operators have a

strong knowledge of the process they use to perform, but may not have expertise in mashups development, the use of an automated development system may ultimately provide a significantly improved orchestration of the process


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• Overview of the Thesis– This paper will present all the performance

metrics analyzed and show the key concepts of this thesis


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Carlos Raniery Paula dos SantosOrientador: Lisandro Zambenedetti Granville

Instituto de Informática – UFRGSInf.ufrgs.br/~crpsantos

Some questions….

ComputerNetworksGroup

Thank you for your attention!

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• [05] CANDEA, G.; KICIMAN, E.; KAWAMOTO, S.; FOX, A. Autonomous recovery in componentized Internet applications. Cluster Computing, Hingham, MA, USA, v.9, p.175–190, April 2006.

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Mashup System

Developer

End User Content Providers


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RELIABILITYMASHUPS4

Adaptation Adaptation

Operation(merge)

Forcing

Buffer

Adaptation

External System

Mashup System

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PRELIMINARY EVALUATIONDISPATCH PROCESS5

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• The focus is on the Request Fulfillment process– Responsible for carrying out service requests, and

that interfaces with Service Desk, Incident Management, and Change Management

• This process includes two types of human operators:– System administrators: technical personnel with

knowledge to resolve specific requests– Dispatchers: responsible for monitoring new

requests, dispatching the requests to the appropriate SA, and monitoring compliance with SLAs

PRELIMINARY EVALUATIONDISPATCH PROCESS5

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• Interaction Components:– Visual: represent way data can be displayed, such

as map, tables, and trees– Control: represent basic programming logic, such

as loops, and conditions– Operation: perform operations over retrieved

information, such as filtering, merging, and arithmetic operations

– Adaptation: represent external resources, these are created based on wrapper meta-information stored

– Reuse: this type of component represent existing mashups, enabling their reuse in other masups

Mashup System

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• Effectiveness: how to match the customers requirements and what the service provides in fact

• Cost: may be defined in SLAs• Security: probability of information leakage

Other metrics

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• Checklists are increasingly being used in Scottish hospitals, for example in pre-operative settings

• Minimize staff interruptions and distractions• Prevent errors: procedures, training, UI design

(allow only valid choices)• Recover errors: undo capability, confirmation

Other system improvements

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Mashup Patterns

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Gesture Time

K Keying 0.2 sec

B Holding/Releasing key 0.1 sec

P Pointing 1.1 sec

H Homing 0.4 sec

M Mentally Preparing 1.35 sec

KLM

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• KLM model: provides a wealth of detail at the lower level of human-computer interactions

• Complexity model: besides it addresses both levels of inefficiencies, we discard all the complexity metrics except the memory and decision metrics, which capture higher level potential inefficiencies not addressed by KLM

Total time spend by a human

Human-computer interactions

Cognitive actions

Quantitative Modeling

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• The approach can be summarized in three steps:– Assessing the complexity and timing a baseline

scenario– Construction of the regression model and its quality

evaluation– Employing the model to predict labor costs

Complexity Model

Performance Management of IT Service Processes Using a Mashup-based Approach

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