The increasing demand for business agility mandates enterprises to collaborate on many frontlines to stay competitive and achieve high business performance. This has given rise to the issue of Cross Enterprise Collaboration (CEC), which refers to the collaboration of two or more enterprises in achieving common goals. CEC is facing major people, process and technological challenges today. IT setup in today's enterprises makes them walled gardens, for good reasons, however, it is a prohibitor of successful collaboration among companies, their people and processes towards achieving higher performance. In this paper, we propose a framework that facilitates the understanding of major CEC challenges. Facilitating CEC requires supporting process-level collaboration, and protection of shared IP and data with various enterprise-level and regulatory policies. The framework incorporates a process-based collaboration system for conversation-oriented, flexible and policy-aware process collaboration among people from different enterprises. We focus on the scenario of the collaboration of providers in multi-sourced IT services to exemplify the problem and the proposed solution.
{"title":"A Process- and Policy-Aware Cross Enterprise Collaboration Framework for Multisourced Services","authors":"H. M. Nezhad, C. Bartolini, J. Erbes, S. Graupner","doi":"10.1109/SRII.2012.60","DOIUrl":"https://doi.org/10.1109/SRII.2012.60","url":null,"abstract":"The increasing demand for business agility mandates enterprises to collaborate on many frontlines to stay competitive and achieve high business performance. This has given rise to the issue of Cross Enterprise Collaboration (CEC), which refers to the collaboration of two or more enterprises in achieving common goals. CEC is facing major people, process and technological challenges today. IT setup in today's enterprises makes them walled gardens, for good reasons, however, it is a prohibitor of successful collaboration among companies, their people and processes towards achieving higher performance. In this paper, we propose a framework that facilitates the understanding of major CEC challenges. Facilitating CEC requires supporting process-level collaboration, and protection of shared IP and data with various enterprise-level and regulatory policies. The framework incorporates a process-based collaboration system for conversation-oriented, flexible and policy-aware process collaboration among people from different enterprises. We focus on the scenario of the collaboration of providers in multi-sourced IT services to exemplify the problem and the proposed solution.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132684812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While web shops generate record profits stationary retail shops continuously lose importance. Especially when it comes to non-perishable goods, people tend more and more to buy products online. Therefore, stationary retailers need to come up with new individual service offers to retain existing customers and to attract new ones. Building up new customized service offers requires knowledge about the customers at the point of sale. Especially customer movements are a valuable source of information that reveals a variety of information about customer behavior. First, an approach for video-based extraction of customer movements at the point of sale is presented. Subsequently, methods of customer behavior analysis are outlined. Based on the results applications for retail managers, sales personnel and automated customer services are introduced.
{"title":"Customer Tracking and Tracing Data as a Basis for Service Innovations at the Point of Sale","authors":"Johannes Kröckel, F. Bodendorf","doi":"10.1109/SRII.2012.115","DOIUrl":"https://doi.org/10.1109/SRII.2012.115","url":null,"abstract":"While web shops generate record profits stationary retail shops continuously lose importance. Especially when it comes to non-perishable goods, people tend more and more to buy products online. Therefore, stationary retailers need to come up with new individual service offers to retain existing customers and to attract new ones. Building up new customized service offers requires knowledge about the customers at the point of sale. Especially customer movements are a valuable source of information that reveals a variety of information about customer behavior. First, an approach for video-based extraction of customer movements at the point of sale is presented. Subsequently, methods of customer behavior analysis are outlined. Based on the results applications for retail managers, sales personnel and automated customer services are introduced.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133641931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In multi-tenant cloud systems today, provisioning of resources for new tenancy is based on selection from a catalogue published by the cloud provider. The published images are generally a stack of appliances with Infrastructure (IaaS) and Platform (PaaS) layers and optionally Application layers (SaaS). Such a ready-made model enables quicker and streamlined resource provisioning to clients. However, this approach poses certain challenges to clients in the short run and providers in the long run. Unique tenancy requirements from each client are forcibly generalized by selecting one of the available images from the catalogue as the tenancy requirements are not modeled or validated to start with. Moreover, resource provisioning is mostly done towards addressing the peak load expectations in the tenancy. Such a static approach does not help in adapting to dynamically changing tenancy requirements, most often leading to the tenants owning and subsequently paying for more than what they need. In particular, provisioned resources are expected to perform at the same level of quality without accounting for their changing health. In our paper, we propose an extensible dynamic provisioning framework to address these challenges. We start with defining a Tenancy Requirements Model (TRM) which helps map provisioned resources with tenants. The provisioned and candidate resources are also modeled with their Quality of Service (QoS) characteristics which we call Health Grading Model (HGM); this helps in continuous monitoring and grading of resources based on health parameters and enables health prediction for future provisioning. Together, TRM and HGM allow dynamic re-provisioning for existing tenants based on either changing tenancy requirements or health grading predictions. We also present algorithms for prediction based provisioning and tenancy requirement matching. We illustrate our ideas throughout this paper with a running example, and present a proof-of-concept prototype implementation on IBM's Rational Software Architect modeling tool.
{"title":"Model Driven Provisioning in Multi-tenant Clouds","authors":"A. Gohad, Karthikeyan Ponnalagu, N. Narendra","doi":"10.1109/SRII.2012.12","DOIUrl":"https://doi.org/10.1109/SRII.2012.12","url":null,"abstract":"In multi-tenant cloud systems today, provisioning of resources for new tenancy is based on selection from a catalogue published by the cloud provider. The published images are generally a stack of appliances with Infrastructure (IaaS) and Platform (PaaS) layers and optionally Application layers (SaaS). Such a ready-made model enables quicker and streamlined resource provisioning to clients. However, this approach poses certain challenges to clients in the short run and providers in the long run. Unique tenancy requirements from each client are forcibly generalized by selecting one of the available images from the catalogue as the tenancy requirements are not modeled or validated to start with. Moreover, resource provisioning is mostly done towards addressing the peak load expectations in the tenancy. Such a static approach does not help in adapting to dynamically changing tenancy requirements, most often leading to the tenants owning and subsequently paying for more than what they need. In particular, provisioned resources are expected to perform at the same level of quality without accounting for their changing health. In our paper, we propose an extensible dynamic provisioning framework to address these challenges. We start with defining a Tenancy Requirements Model (TRM) which helps map provisioned resources with tenants. The provisioned and candidate resources are also modeled with their Quality of Service (QoS) characteristics which we call Health Grading Model (HGM); this helps in continuous monitoring and grading of resources based on health parameters and enables health prediction for future provisioning. Together, TRM and HGM allow dynamic re-provisioning for existing tenants based on either changing tenancy requirements or health grading predictions. We also present algorithms for prediction based provisioning and tenancy requirement matching. We illustrate our ideas throughout this paper with a running example, and present a proof-of-concept prototype implementation on IBM's Rational Software Architect modeling tool.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114386764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tere Gonzalez, P. Santos, Fernando Orozco, M. Alcaraz-Mejia, Victor Zaldivar, Alberto De Obeso, A. García
Matching the right people to the right job considering constraints such as qualifications, availability and cost is the cornerstone of IT projects delivery services. We present a study to improve data accuracy and completeness for resource matching by integrating unstructured data sources and introducing text mining techniques to dynamically adapt resource profile for resource planning decisions. Our approach discovers resource categories by extracting and learning new patterns from employee resumes; and incorporating resource experience for the job-matching optimization during the resource planning exercise.
{"title":"Adaptive Employee Profile Classification for Resource Planning Tool","authors":"Tere Gonzalez, P. Santos, Fernando Orozco, M. Alcaraz-Mejia, Victor Zaldivar, Alberto De Obeso, A. García","doi":"10.1109/SRII.2012.67","DOIUrl":"https://doi.org/10.1109/SRII.2012.67","url":null,"abstract":"Matching the right people to the right job considering constraints such as qualifications, availability and cost is the cornerstone of IT projects delivery services. We present a study to improve data accuracy and completeness for resource matching by integrating unstructured data sources and introducing text mining techniques to dynamically adapt resource profile for resource planning decisions. Our approach discovers resource categories by extracting and learning new patterns from employee resumes; and incorporating resource experience for the job-matching optimization during the resource planning exercise.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"2083 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121049247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Networked systems are pervasive in the current Internet age and they manifest in several ways such as online and enterprize social networks, data center networks, cloud service networks, and global business networks. Such networked systems generally consist of entities and connections (or edges) among these entities. As services or applications are deployed over these networks, the success of any service or application is crucially dependent on the high availability of the components (namely, entities and connections) in the network. This calls for an important problem of identifying the critical components in the network to improve the quality of the services offered over these networks. We propose a novel and generic game theoretic framework to identify critical components with respect to a given task (or service or application) in the network. In particular, we apply the proposed generic game theoretic framework to determine critical edges in the context of k-edge connectivity between certain pairs of nodes in a given network. We call a pair of nodes in a network k-edge connected if there exists k-edge disjoint (shortest) paths between these nodes. Identifying critical edges in the setting of the k-edge connectivity is an extremely important problem especially in the context of data center networks and cloud service networks. The following are the specific contributions of this paper: (1) We first formally define a game theoretic model for the k-edge connectivity between certain pairs of nodes in the network. We refer to this as k-edge connectivity game. We then define the criticality of any edge to be the value of its Banzhaf power index in the k-edge connectivity game. In this setting, identifying critical edges boils down to the computation of Banzhaf power index in the k-edge connectivity game; (2) We then show that computing the Banzhaf power index for any edge in the k-edge connectivity game is #P-complete. We show this result by reducing the problem of counting the number of perfect matchings in a given graph to an instance of computing the Banzhaf power index for an edge in the k-edge connectivity game. This implies that the computation of Banzhaf power index for an edge in the k-edge connectivity game is computationally hard; (3) To alleviate this computational issue, we propose an approximation algorithm and also we present a simple heuristic based on the notion of Shapley value in cooperative game theory; (4) We then derive a closed form expression for computing the Banzhaf power index of any edge in the k-edge connectivity game, when the network structure is a tree; and (5) We finally evaluate the proposed approach using thorough experimentation on certain real world network data sets.
{"title":"A Game Theoretic Approach to Identify Critical Components in Networked Systems","authors":"Ramasuri Narayanam","doi":"10.1109/SRII.2012.64","DOIUrl":"https://doi.org/10.1109/SRII.2012.64","url":null,"abstract":"Networked systems are pervasive in the current Internet age and they manifest in several ways such as online and enterprize social networks, data center networks, cloud service networks, and global business networks. Such networked systems generally consist of entities and connections (or edges) among these entities. As services or applications are deployed over these networks, the success of any service or application is crucially dependent on the high availability of the components (namely, entities and connections) in the network. This calls for an important problem of identifying the critical components in the network to improve the quality of the services offered over these networks. We propose a novel and generic game theoretic framework to identify critical components with respect to a given task (or service or application) in the network. In particular, we apply the proposed generic game theoretic framework to determine critical edges in the context of k-edge connectivity between certain pairs of nodes in a given network. We call a pair of nodes in a network k-edge connected if there exists k-edge disjoint (shortest) paths between these nodes. Identifying critical edges in the setting of the k-edge connectivity is an extremely important problem especially in the context of data center networks and cloud service networks. The following are the specific contributions of this paper: (1) We first formally define a game theoretic model for the k-edge connectivity between certain pairs of nodes in the network. We refer to this as k-edge connectivity game. We then define the criticality of any edge to be the value of its Banzhaf power index in the k-edge connectivity game. In this setting, identifying critical edges boils down to the computation of Banzhaf power index in the k-edge connectivity game; (2) We then show that computing the Banzhaf power index for any edge in the k-edge connectivity game is #P-complete. We show this result by reducing the problem of counting the number of perfect matchings in a given graph to an instance of computing the Banzhaf power index for an edge in the k-edge connectivity game. This implies that the computation of Banzhaf power index for an edge in the k-edge connectivity game is computationally hard; (3) To alleviate this computational issue, we propose an approximation algorithm and also we present a simple heuristic based on the notion of Shapley value in cooperative game theory; (4) We then derive a closed form expression for computing the Banzhaf power index of any edge in the k-edge connectivity game, when the network structure is a tree; and (5) We finally evaluate the proposed approach using thorough experimentation on certain real world network data sets.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134039947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Helping locate the patents of the right technologies for licensing to prospective clients is more than one billion USD business annually to IBM. However, searching for right technologies from multiple massive data sources for a value presentation to customers is a typical human labor intensive task in the past. In this paper, we design a prospective client driven technology recommendation system to enable the automatic search of technologies for patent licensing. The idea has been to make use of knowledges from the large-scale encyclopedia Wikipedia in conjunction with 11 millions patent documents to develop an online technology recommendation system for prospective clients of IBM. The live system demands not only the fast response time but also a set of highly relevant patent documents which are technically interesting to a query prospective client.
{"title":"Prospective Client Driven Technology Recommendation","authors":"Qi He, W. Spangler, Bin He, Ying Chen, Linda Kato","doi":"10.1109/SRII.2012.23","DOIUrl":"https://doi.org/10.1109/SRII.2012.23","url":null,"abstract":"Helping locate the patents of the right technologies for licensing to prospective clients is more than one billion USD business annually to IBM. However, searching for right technologies from multiple massive data sources for a value presentation to customers is a typical human labor intensive task in the past. In this paper, we design a prospective client driven technology recommendation system to enable the automatic search of technologies for patent licensing. The idea has been to make use of knowledges from the large-scale encyclopedia Wikipedia in conjunction with 11 millions patent documents to develop an online technology recommendation system for prospective clients of IBM. The live system demands not only the fast response time but also a set of highly relevant patent documents which are technically interesting to a query prospective client.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134343144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developments in radio frequency identification (RFID) technology have made data on customer movement paths in supermarkets available. In this paper, we propose a method for customer behavior modeling by using RFID data and the hidden Markov model (HMM). In this method, "Stop" and "Pass by" behavior are estimated and the proposed method is evaluated by predicting the sales areas where customers actually purchased items. Using this method, we also demonstrate the shopping momentum. This effect, however, is experienced by only some customers, not all.
{"title":"Customer Behavior Modelling Using Radio Frequency Identification Data and the Hidden Markov Model","authors":"K. Yada, Natsuki Sano","doi":"10.1109/SRII.2012.63","DOIUrl":"https://doi.org/10.1109/SRII.2012.63","url":null,"abstract":"Developments in radio frequency identification (RFID) technology have made data on customer movement paths in supermarkets available. In this paper, we propose a method for customer behavior modeling by using RFID data and the hidden Markov model (HMM). In this method, \"Stop\" and \"Pass by\" behavior are estimated and the proposed method is evaluated by predicting the sales areas where customers actually purchased items. Using this method, we also demonstrate the shopping momentum. This effect, however, is experienced by only some customers, not all.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131874129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper outlines the increasing challenges and perspectives of service innovation in new business models and the relationship between service innovation and business development procedures such as service engineering and business model design concepts. It describes the main drivers of service innovation and identifies what makes service innovation so important today, consequences for (service) business modeling as well as the implications for an integrated development approach of service and business model innovation. A comparative analysis shows that in general Osterwalder's Business Model Design approach compliments the strategy-based service engineering process well in most areas. Using the toolbox of the Business Model Canvas complemented by specific methods and tools from the service engineering approach, new complex service systems can be developed in a systematic and holistic manner. Our findings from applying the theoretical results in a case study to developing a new business model for a cloud-based SaaS solution show that business modeling needs to be continuously cross-linked to the entire service engineering process to adapt to ever changing customer needs and vice versa.
{"title":"The Role of Business Model Design in the Service Engineering Process: A Comparative Case Study in the Field of Cloud Computing to Join Service Engineering with Business Model Design","authors":"Christoph Ehrenhofer, E. Kreuzer","doi":"10.1109/SRII.2012.39","DOIUrl":"https://doi.org/10.1109/SRII.2012.39","url":null,"abstract":"This paper outlines the increasing challenges and perspectives of service innovation in new business models and the relationship between service innovation and business development procedures such as service engineering and business model design concepts. It describes the main drivers of service innovation and identifies what makes service innovation so important today, consequences for (service) business modeling as well as the implications for an integrated development approach of service and business model innovation. A comparative analysis shows that in general Osterwalder's Business Model Design approach compliments the strategy-based service engineering process well in most areas. Using the toolbox of the Business Model Canvas complemented by specific methods and tools from the service engineering approach, new complex service systems can be developed in a systematic and holistic manner. Our findings from applying the theoretical results in a case study to developing a new business model for a cloud-based SaaS solution show that business modeling needs to be continuously cross-linked to the entire service engineering process to adapt to ever changing customer needs and vice versa.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129145727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Future service technology will provide an unprecedented access to operational data, which opens up novel opportunities for monitoring, controlling and managing service- oriented business processes. Amongst these opportunities, we consider predictive monitoring to be a major lever for increased efficiency, effectiveness and sustainability in future business networks. Predictive monitoring means that critical events, potential deviations and unplanned situations can be anticipated and proactively managed and mitigated along the execution of business processes. This paper demonstrates the potential of predictive monitoring in practice. We focus on transport & logistics as a major industry sector -- accounting for between 10% to 20% of a country's Gross Domestic Product. Based on widely adopted standards and real operational data, we empirically support the relevance of key issues faced in that industry sector, such as late cancellations of transport bookings and delayed deliveries. As a solution, we describe the design of a novel, cloud- and services-based collaboration and integration platform. Based on this platform we develop short-term prediction capabilities allowing to proactively manage and mitigate the identified issues in the transport & logistics industry, thus promising to increase business efficiency and sustainability.
{"title":"Predictive Monitoring of Heterogeneous Service-Oriented Business Networks: The Transport and Logistics Case","authors":"Andreas Metzger, Rod Franklin, Yagil Engel","doi":"10.1109/SRII.2012.42","DOIUrl":"https://doi.org/10.1109/SRII.2012.42","url":null,"abstract":"Future service technology will provide an unprecedented access to operational data, which opens up novel opportunities for monitoring, controlling and managing service- oriented business processes. Amongst these opportunities, we consider predictive monitoring to be a major lever for increased efficiency, effectiveness and sustainability in future business networks. Predictive monitoring means that critical events, potential deviations and unplanned situations can be anticipated and proactively managed and mitigated along the execution of business processes. This paper demonstrates the potential of predictive monitoring in practice. We focus on transport & logistics as a major industry sector -- accounting for between 10% to 20% of a country's Gross Domestic Product. Based on widely adopted standards and real operational data, we empirically support the relevance of key issues faced in that industry sector, such as late cancellations of transport bookings and delayed deliveries. As a solution, we describe the design of a novel, cloud- and services-based collaboration and integration platform. Based on this platform we develop short-term prediction capabilities allowing to proactively manage and mitigate the identified issues in the transport & logistics industry, thus promising to increase business efficiency and sustainability.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133940957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Karthik, Sreyash Kenkre, Krishnasuri Narayanam, Vinayaka Pandit
Resiliency is a key word for a broad range of service delivery organizations. It is defined as the ability of an organization to rapidly adapt and effectively respond to the disruptions in its operations. A service delivery organization delivers a set of services which are essentially specified by their required set of resources. The organization sets up an infrastructural network of resources required for the service delivery and assigns to each service, its required set of resources. It also keeps sufficient residual capacity of the resources for the purpose of contingency planning. At the time of a disruptive incident, it reallocates the resources to the affected services from its residual capacity to keep the service running while the effects of the disruptions are reversed. Such actions of reallocating the resources to deal with disruptions to the original allocation are called recourse actions. We develop a framework that enables a data and analytics driven approach to achieve efficient recourse actions based resiliency. Our framework is based on abstractions of three important aspects of a service delivery organization, namely, the infrastructural network of resources, the set of services in terms of their requirements of resources, and the set of disruptive scenarios that an organization has to contend with. Our model also captures the different dependencies that exist within the infrastructure network. For instance, if the power supply is affected, our model allows us to infer all the other infrastructure resources which get affected as a consequence of the lack of power supply. There are no benchmark datasets to test the quality of resiliency analytics because of two reasons: nascency of research in this area and the classified nature of the organizational data required for such analytics. So, we have developed a simulation engine aimed at mimicking real-life organizations. We demonstrate how our framework can be used to proactively identify critical scenarios that could have adverse impact on the service delivery of an organization. We then show how such a knowledge can be used to make intelligent allocation of resources to the services so as to enable efficient recourse actions. These two analyses highlight that our framework can essentially serve as a decision support system for resiliency.
{"title":"Resiliency Analytics Framework for Service Delivery Organizations","authors":"S. Karthik, Sreyash Kenkre, Krishnasuri Narayanam, Vinayaka Pandit","doi":"10.1109/SRII.2012.81","DOIUrl":"https://doi.org/10.1109/SRII.2012.81","url":null,"abstract":"Resiliency is a key word for a broad range of service delivery organizations. It is defined as the ability of an organization to rapidly adapt and effectively respond to the disruptions in its operations. A service delivery organization delivers a set of services which are essentially specified by their required set of resources. The organization sets up an infrastructural network of resources required for the service delivery and assigns to each service, its required set of resources. It also keeps sufficient residual capacity of the resources for the purpose of contingency planning. At the time of a disruptive incident, it reallocates the resources to the affected services from its residual capacity to keep the service running while the effects of the disruptions are reversed. Such actions of reallocating the resources to deal with disruptions to the original allocation are called recourse actions. We develop a framework that enables a data and analytics driven approach to achieve efficient recourse actions based resiliency. Our framework is based on abstractions of three important aspects of a service delivery organization, namely, the infrastructural network of resources, the set of services in terms of their requirements of resources, and the set of disruptive scenarios that an organization has to contend with. Our model also captures the different dependencies that exist within the infrastructure network. For instance, if the power supply is affected, our model allows us to infer all the other infrastructure resources which get affected as a consequence of the lack of power supply. There are no benchmark datasets to test the quality of resiliency analytics because of two reasons: nascency of research in this area and the classified nature of the organizational data required for such analytics. So, we have developed a simulation engine aimed at mimicking real-life organizations. We demonstrate how our framework can be used to proactively identify critical scenarios that could have adverse impact on the service delivery of an organization. We then show how such a knowledge can be used to make intelligent allocation of resources to the services so as to enable efficient recourse actions. These two analyses highlight that our framework can essentially serve as a decision support system for resiliency.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"419 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116277211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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