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Anveshana
Search for the Right Service
Dr Mydhili K. Nair
Associate Professor,
Dept. of ISE, MSRIT
Varun M Deshpande
PhD Student,
Dept. of CSE, Jain University
&
Software QA Engineer,
McAfee Software (India) Pvt. Ltd.
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I2CT 2014
Agenda Background
Problem Domain
Literature Survey
Design and Analysis
Results
Conclusion and Future Scope
Bibliography
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Background
Service Delivery
Requirements
CustomerService Provider
Service Model – Customer requests for service. Service provider provides the same
But each customer is different. They have different needs.
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Problem Domain 4
“Does Quality of a Product/Service
have an impact on its cost (Do you
think that better quality product
costs more)?”
“Do you feel each end user has
different quality requirements?”
“Do you always choose higher
quality product than what you
need/afford (Do you prefer ‘Best’
product in the market even if it is
out of budget)?”
Literature Survey
Mohammad Alrifai [1] et al., in their work define QoS-based service selection
problem as “finding the best component services that satisfy end-to-end quality
requirements.” They model this problem as a multidimensional multi choice 0-1
knapsack problem. They discuss shortcomings of solutions based on linear
programming techniques for large data sets.
Guofeng Chang [2], in his work proposed a genetic algorithm based approach to
solve web service selection problem. His idea was based on traditional evolutionary
ideas of natural selection and genetics. The core philosophy that is focused on is
“Survival of the Fittest.” This means that only the best services are preferred to
during service selection while others are phased out of the competition.
Yilei Zhang et al., [3] recognized finding the desired web service among the
available repository an emergent and challenging research problem. Their search
framework considers both functional and non-functional attributes of publically
available web services which have similarities to user’s request. They further
propose 3 searching strategies which can autonomously fetch the right web service
suitable to the end user.
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Literature Survey
Maheshwari et al. [4] discussed the importance of QoS parameters on top of ranking
provided by users of web services. Their architecture included OWL-S convertor
which converts syntactically described web service into a semantic web service.
Semantic repository which contains the list of advertised web services in OWL-S
format, a QoS broker equipped with match making algorithm.
Tajudeen Adeyemi Ajao et al. [5] have very recently published work related to
current research domain. They are of opinion that identifying the optimal service is
still an active research area. They propose usage of QoS-based Filtering, Ranking
and Selection Algorithm for this purpose. They adopt a similar approach as in [3] and
filter out the services which fall short of the requirements of end user. Later they rank
each service and identify the service with highest score as the optimal web service.
Zibin Zheng et al. [6] in their work lay a firm mathematical model for addressing the
problem of web service recommendation when there are abundant web services
available. They discuss system architecture to solve the problem. They have provided
mathematical representation for key aspects like similarity computation and web
service recommendation.
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Design & Analysis 7
Customer QoS Request ObjectAdvertized parameters of Service
Workflow of Service Selection
Design & Analysis Cont. 8
Steps during execution of Anveshana
Algorithm Step 1: Calculation of Individual Variance
Let Linear Relevance be denoted as “L”. LetRequested value be denoted as
“R”. Let Advertised value be denoted as “A”. Normalization criterian is
denoted as Range. In our case, it is taken as “10”. This may be altered
based on requirements of a particular domain.
For each QoS parameter “i” , Li = |Ri – Ai| / Range
Step 2: Priority Consideration
Suppose there are “n” QoS parameters, priority for any particular QoS
parameter would be unique integer between 1-n. Weight of each QoS
parameter is calculated using below formula.
Let priority values be denoted by “P”. Let weight of a QoS parameter be
denoted by “W”.
For each QoS parameter “i”, Wi = [ n - Pi + 1 ] / ∑n
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Algorithm contd. Step 3: Calculation of Individual Relevance
Individual relevance is calculated for each parameter seperately. This is
done using below formula. This indicates that lesser the variance between
advertised value and requested value, more is the relevance score.
For each QoS parameter “i”, Ii = 1 – [Li * Wi]
Step 4: Calculation of Total Relevance
This is technically, the final step of Anveshana. In this step, each of the
Individual Relevances are integrated to arrive at the final score. Formula for
calculation Total Relevance is given below. Let Total Relevance be denoted
as “T”.
T = ( ∑ Ii ) / n
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Results 11
User requestRanked services for user request
Feedback on usefulness of
Anveshana
• Core aim of this project is to move
towards searching for the “Right”
service than “Best” service.
• This approach is beneficial for end
users who are looking for services
which match their custom
requirements rather that best ( and
hence more costly ) service.
Conclusions and Future Scope
We conducted an online survey to understand end user
perspective of QoS based service selection
Our approach with Anveshana was to help end users get the
“Right” services based on their custom requirements rather than
“Best in class” services.
Need to use real world data and compare with other algorithms for
performance and correctness
This approach can be implemented for customer driven SLA
management to find solutions to some of security and privacy
issues in cloud computing
Other real world applications of Anveshana are search engines for
e-commerce website, web service discovery and composition,
game consoles etc.
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Bibiliography Mohammad Alrifai, Thomas Risse, Perter Dolog and Wolfgang Nejdl, “A Scalable Approach for
QoS-based Web Service Selection,” Service-Oriented Computing – ICSOC 2008 International Workshops, p 190-199
Guofeng Chang, “QoS-Based Web Service Selection Approach”, Software Engineering and Knowledge Engineering: Theory and Practice, 2012, Volume 2, p 887-892
Yilei Zhang, Zibin Zheng, Lyu, M.R., “WSExpress: A QoS-aware Search Engine for Web Services”, Web Services (ICWS), 2010 IEEE International Conference
Maheswari, S. and G.R. Karpagam, “QoS Based Efficient Web Service Selection,” European Journal of Scientific Research, 2011. 66(3): p. 428-440.
Tajudeen Adeyemi Ajao, Safaai Deris, Isiaka Adekunle Obasa, “QoS-based Web Service Selection Using Filtering, Ranking and Selection Algorithm,” International Journal of Scientific & Engineering Research, Volume 4, Issue 7, 2013
Zibin Zheng, Hao Ma, Michael R Lyu and Irwin King, “WSRec: A Collaborative Filtering Based Web Service Recommender System,” 2009 IEEE International Conference on Web Services
Varun M Deshpande, Dr. Mydhili K. Nair, Balaji Sowndararajan, Customer Driven SLA in Cloud Based Systems, In Proceedings published by Elsevier of International Conference of Emerging Computations and Information Technologies, SIT, Tumkur, Karnataka (India), 22-23 November, 2013, pp 508-518
Online Survey on QoS based Service Selection Published Results: https://docs.google.com/forms/d/1VFZ8m7pPsaCSKQplOLz-auOTSd2AwYHV-tsSpMkfNU8/viewanalytics#start=publishanalytics
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