D. Jayasinghe, C. Pu, Fábio Oliveira, Florian Rosenberg, T. Eilam
Infrastructure-as-a-Service (IaaS) cloud environments expose to users the infrastructure of a data center while relieving them from the burden and costs associated with its management and maintenance. IaaS clouds provide an interface by means of which users can create, configure, and control a set of virtual machines that will typically host a composite software service. Given the increasing popularity of this computing paradigm, previous work has focused on modeling composite software services to automate their deployment in IaaS clouds. This work is concerned with the runtime state of composite services during and after deployment. We propose AESON, a deployment runtime that automatically detects node (virtual machine) failures and eventually brings the composite service to the desired deployment state by using information describing relationships between the service components. We have designed AESON as a decentralized peer-to-peer publish/subscribe system leveraging IBM's Bulletin Board (BB), a topic-based distributed shared memory service built on top of an overlay network.
{"title":"AESON: A Model-Driven and Fault Tolerant Composite Deployment Runtime for IaaS Clouds","authors":"D. Jayasinghe, C. Pu, Fábio Oliveira, Florian Rosenberg, T. Eilam","doi":"10.1109/SCC.2013.102","DOIUrl":"https://doi.org/10.1109/SCC.2013.102","url":null,"abstract":"Infrastructure-as-a-Service (IaaS) cloud environments expose to users the infrastructure of a data center while relieving them from the burden and costs associated with its management and maintenance. IaaS clouds provide an interface by means of which users can create, configure, and control a set of virtual machines that will typically host a composite software service. Given the increasing popularity of this computing paradigm, previous work has focused on modeling composite software services to automate their deployment in IaaS clouds. This work is concerned with the runtime state of composite services during and after deployment. We propose AESON, a deployment runtime that automatically detects node (virtual machine) failures and eventually brings the composite service to the desired deployment state by using information describing relationships between the service components. We have designed AESON as a decentralized peer-to-peer publish/subscribe system leveraging IBM's Bulletin Board (BB), a topic-based distributed shared memory service built on top of an overlay network.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125312944","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 Application Management Services (AMS), high resource utilization, effective resource planning and optimal assignment of service requests to resources are critical to success. Meeting these objectives requires a systematic and repeatable approach for determining the best way of measuring resource utilization, assessing workload and assigning service requests. In this paper, we present a two-step approach to help achieve the above objectives. We first measure the actual amount of effort that each resource spends on handling each service request (SR) based on a metadata model and a set of SR handling priority rules. Then, we proceed to measure resource utilization and assess SR assignment process based on the effort data calculated in step one.
{"title":"Measuring and Applying Service Request Effort Data in Application Management Services","authors":"Ying Li, K. Katircioglu","doi":"10.1109/SCC.2013.64","DOIUrl":"https://doi.org/10.1109/SCC.2013.64","url":null,"abstract":"In Application Management Services (AMS), high resource utilization, effective resource planning and optimal assignment of service requests to resources are critical to success. Meeting these objectives requires a systematic and repeatable approach for determining the best way of measuring resource utilization, assessing workload and assigning service requests. In this paper, we present a two-step approach to help achieve the above objectives. We first measure the actual amount of effort that each resource spends on handling each service request (SR) based on a metadata model and a set of SR handling priority rules. Then, we proceed to measure resource utilization and assess SR assignment process based on the effort data calculated in step one.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116865425","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 this paper, we propose a skyline computation system UCOS (User Clustering based Online Skyline), which divides the computation into offline and online stages. Based on the truth that QoS similarity implies the skyline similarity, the offline stage of UCOS system dose user clustering according to the historical user-service QoS records by given distance metrics. Then, we compute the representative skyline for each cluster standing for the general characters of the users' skylines. Benefit from those offline results, the online stage is able to give a rapid prediction for online skyline request and achieves good online computation performance by doing refinement on the predicted results.
本文提出了一种基于用户聚类的在线天际线计算系统UCOS (User Clustering based Online skyline)，该系统将计算分为离线和在线两个阶段。基于QoS相似度意味着天际线相似度的事实，UCOS系统的离线阶段根据给定距离度量的历史用户服务QoS记录进行用户聚类。然后，我们计算代表用户天际线一般特征的每个集群的代表性天际线。利用这些离线结果，在线阶段能够对在线天际线请求进行快速预测，并通过对预测结果进行细化，获得良好的在线计算性能。
{"title":"UCOS: Enhanced Online Skyline Computation by User Clustering","authors":"Kehan Chen, Lichuan Ji, Kunyang Jia, Jian Wu","doi":"10.1109/SCC.2013.14","DOIUrl":"https://doi.org/10.1109/SCC.2013.14","url":null,"abstract":"In this paper, we propose a skyline computation system UCOS (User Clustering based Online Skyline), which divides the computation into offline and online stages. Based on the truth that QoS similarity implies the skyline similarity, the offline stage of UCOS system dose user clustering according to the historical user-service QoS records by given distance metrics. Then, we compute the representative skyline for each cluster standing for the general characters of the users' skylines. Benefit from those offline results, the online stage is able to give a rapid prediction for online skyline request and achieves good online computation performance by doing refinement on the predicted results.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124988551","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}
The as a Service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilized on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our general idea of modeling this service composition and recommendation process as Markov Decision Process and of solving it by means of Reinforcement Learning. A case study serves as proof of concept.
{"title":"Learning Recommendation System for Automated Service Composition","authors":"Alexander Jungmann, B. Kleinjohann","doi":"10.1109/SCC.2013.66","DOIUrl":"https://doi.org/10.1109/SCC.2013.66","url":null,"abstract":"The as a Service paradigm reflects the fundamental idea of providing basic coherent functionality in terms of components that can be utilized on demand. These so-called services may also be interconnected in order to provide more complex functionality. Automation of this service composition process is indeed a formidable challenge. In our work, we are addressing this challenge by decomposing service composition into sequential decision making steps. Each step is supported by a recommendation mechanism. If composition requests recur over time and if evaluations of composition results are fed back, a proper recommendation strategy can evolve over time through learning from experience. In this paper, we describe our general idea of modeling this service composition and recommendation process as Markov Decision Process and of solving it by means of Reinforcement Learning. A case study serves as proof of concept.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316816","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}
Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed to achieve higher noise isolation. Furthermore iterative steps of algorithms consider about the similarity of limited number of services such as similarity of cluster centers. This leads to reduce the cluster performance. In this paper we apply a spatial clustering technique called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic similarity values between services as the affinity values. Most of the current clustering approaches use similarity distance measurement such as keyword, ontology and information-retrieval-based methods. These approaches have problem of short of high quality ontology and loss of semantic information. In this paper, we calculate the service similarity by using hybrid term similarity method which uses ontology learning and information retrieval. Experimental results show our clustering approach is able to plot similar services into same area and aid to search Web services by visualization of the service data on a spherical surface.
{"title":"Clustering and Spherical Visualization of Web Services","authors":"B. Kumara, Y. Yaguchi, Incheon Paik, Wuhui Chen","doi":"10.1109/SCC.2013.90","DOIUrl":"https://doi.org/10.1109/SCC.2013.90","url":null,"abstract":"Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed to achieve higher noise isolation. Furthermore iterative steps of algorithms consider about the similarity of limited number of services such as similarity of cluster centers. This leads to reduce the cluster performance. In this paper we apply a spatial clustering technique called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic similarity values between services as the affinity values. Most of the current clustering approaches use similarity distance measurement such as keyword, ontology and information-retrieval-based methods. These approaches have problem of short of high quality ontology and loss of semantic information. In this paper, we calculate the service similarity by using hybrid term similarity method which uses ontology learning and information retrieval. Experimental results show our clustering approach is able to plot similar services into same area and aid to search Web services by visualization of the service data on a spherical surface.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127916236","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}
Jia Zhang, Bob Iannucci, M. Hennessy, Kaushik Gopal, S. Xiao, Sumeet Kumar, David Pfeffer, Basmah Aljedia, Yuan Ren, M. Griss, Steven Rosenberg, J. Cao, Anthony G. Rowe
The Internet of Things (IoT) offers the promise of integrating the digital world of the Internet with the physical world in which we live. But realizing this promise necessitates a systematic approach to integrating the sensors, actuators, and information on which they operate into the Internet we know today. This paper reports the design and development of an open community-oriented platform aiming to support federated sensor data as a service, featuring interoperability and reusability of heterogeneous sensor data and data services. The concepts of virtual sensors and virtual devices are identified as central autonomic units to model scalable and context-aware configurable/reconfigurable sensor data and services. The decoupling of the storage and management of sensor data and platform-oriented metadata enables the handling of both discrete and streaming sensor data. A cloud computing-empowered prototyping system has been established as a proof of concept to host smart community-oriented sensor data and services.
{"title":"Sensor Data as a Service -- A Federated Platform for Mobile Data-centric Service Development and Sharing","authors":"Jia Zhang, Bob Iannucci, M. Hennessy, Kaushik Gopal, S. Xiao, Sumeet Kumar, David Pfeffer, Basmah Aljedia, Yuan Ren, M. Griss, Steven Rosenberg, J. Cao, Anthony G. Rowe","doi":"10.1109/SCC.2013.34","DOIUrl":"https://doi.org/10.1109/SCC.2013.34","url":null,"abstract":"The Internet of Things (IoT) offers the promise of integrating the digital world of the Internet with the physical world in which we live. But realizing this promise necessitates a systematic approach to integrating the sensors, actuators, and information on which they operate into the Internet we know today. This paper reports the design and development of an open community-oriented platform aiming to support federated sensor data as a service, featuring interoperability and reusability of heterogeneous sensor data and data services. The concepts of virtual sensors and virtual devices are identified as central autonomic units to model scalable and context-aware configurable/reconfigurable sensor data and services. The decoupling of the storage and management of sensor data and platform-oriented metadata enables the handling of both discrete and streaming sensor data. A cloud computing-empowered prototyping system has been established as a proof of concept to host smart community-oriented sensor data and services.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"35 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131859720","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}
"Cloud of Things" (CoT) is a concept that provides smart things' functions as a service and allows them to be used by multiple applications. In the CoT, a single smart thing instance should efficiently host multiple applications, called multi-tenancy. However, since multiple applications may simultaneously access the same smart things, they may contend for uses of the same smart things, which is called resource conflicts. Moreover, smart things inherently form complex dependencies, examples of which are include a group of smart things in a room, a group of smart things owned by a person, etc. Since handling resource conflicts and complex dependencies at an application level is typically ad-hoc and error-prone, it results in exacerbating readability of application codes. To address these issues, we propose a middleware for Cloud of Things called ECO. The ECO middleware manages organizations to handle dependency among/between smart things and virtualizes physical smart things to enable isolation between/among multiple applications using shared smart things yet internally controls smart things's sharing to resolve resource conflicts. Also, it provides consolidation by harmonizing different smart things's execution contexts of multiple applications for efficient utilization of the shared smart things. As a result, ECO middleware facilitates development of multiple applications over heterogeneous smart things with efficient sharing. The ECO middleware is implemented with heterogeneous device frameworks like UPnP, ZigBee, and CoAP over 6LoWPAN. We show that ECO middleware provides efficient sharing controls and access controls with negligible virtualization overhead.
{"title":"Multi-tenancy Support with Organization Management in the Cloud of Things","authors":"S. Kim, Daeyoung Kim","doi":"10.1109/SCC.2013.61","DOIUrl":"https://doi.org/10.1109/SCC.2013.61","url":null,"abstract":"\"Cloud of Things\" (CoT) is a concept that provides smart things' functions as a service and allows them to be used by multiple applications. In the CoT, a single smart thing instance should efficiently host multiple applications, called multi-tenancy. However, since multiple applications may simultaneously access the same smart things, they may contend for uses of the same smart things, which is called resource conflicts. Moreover, smart things inherently form complex dependencies, examples of which are include a group of smart things in a room, a group of smart things owned by a person, etc. Since handling resource conflicts and complex dependencies at an application level is typically ad-hoc and error-prone, it results in exacerbating readability of application codes. To address these issues, we propose a middleware for Cloud of Things called ECO. The ECO middleware manages organizations to handle dependency among/between smart things and virtualizes physical smart things to enable isolation between/among multiple applications using shared smart things yet internally controls smart things's sharing to resolve resource conflicts. Also, it provides consolidation by harmonizing different smart things's execution contexts of multiple applications for efficient utilization of the shared smart things. As a result, ECO middleware facilitates development of multiple applications over heterogeneous smart things with efficient sharing. The ECO middleware is implemented with heterogeneous device frameworks like UPnP, ZigBee, and CoAP over 6LoWPAN. We show that ECO middleware provides efficient sharing controls and access controls with negligible virtualization overhead.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130819309","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}
Ke Ning, Zhangbing Zhou, Jianhua Zheng, Dong Liu, Liang-Jie Zhang
In the era of knowledge economy, knowledge resources have become the most valuable assets for enterprises. To better understand and reuse knowledge, it is necessary to relate it with the context in which the knowledge is generated and used. This is a process that usually occurs in an experienced knowledge-worker's mind and without efficient supporting tools. This paper proposes an approach for the acquisition and utilization of context for the enhancement of knowledge and with a particular focus on methods to enable context extraction from industrial settings. The approach adopts a knowledge context ontology, to correlate knowledge and its context in the high-level activities of a knowledge worker. Knowledge context are extracted by utilizing a combination of methods including context identification, context reasoning, and context similarity measurement. Based on the proposed approach, a set of services for context aware knowledge enhancement are developed and applied in The Chinese Enterprise Management Tank (CEMT), a knowledge sharing and reusing platform for business management knowledge workers in all around China.
{"title":"Services for Context Aware Knowledge Enhancement and Its Application in the Chinese Enterprise Management Tank (CEMT)","authors":"Ke Ning, Zhangbing Zhou, Jianhua Zheng, Dong Liu, Liang-Jie Zhang","doi":"10.1109/SCC.2013.32","DOIUrl":"https://doi.org/10.1109/SCC.2013.32","url":null,"abstract":"In the era of knowledge economy, knowledge resources have become the most valuable assets for enterprises. To better understand and reuse knowledge, it is necessary to relate it with the context in which the knowledge is generated and used. This is a process that usually occurs in an experienced knowledge-worker's mind and without efficient supporting tools. This paper proposes an approach for the acquisition and utilization of context for the enhancement of knowledge and with a particular focus on methods to enable context extraction from industrial settings. The approach adopts a knowledge context ontology, to correlate knowledge and its context in the high-level activities of a knowledge worker. Knowledge context are extracted by utilizing a combination of methods including context identification, context reasoning, and context similarity measurement. Based on the proposed approach, a set of services for context aware knowledge enhancement are developed and applied in The Chinese Enterprise Management Tank (CEMT), a knowledge sharing and reusing platform for business management knowledge workers in all around China.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129475407","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}
Nowadays many software services are hosted in the Cloud. When there are more requests on these services, there are also more queries sent to the underlying database. In order to keep up with the increasing workload, it is necessary to have multiple servers hosting the data. Some cloud providers offer the full data replication solution. However, this solution only works when the load mainly consists of the read requests, and when the number of write requests increases, it does not scale well. Although data decomposition has been widely used in data-intensive web sites, not much study has been done on how to decompose the underlying data of software services for the purpose of data replication. In this paper, we propose a data-decomposition-based partial replication model for software services. We devise an automatic algorithm for data decomposition under the constraint of the capacity limit of the host machines. We evaluate our approach from two aspects: scalability and performance, using two benchmarks: RUBiS and TPC-W. In the experiment, we test the algorithm using different workload inputs, and also compare our approach with the full data replication approach.
{"title":"Data Decomposition Based Partial Replication Model for Software Services","authors":"Shuo Chen, Chi-Hung Chi, Chen Ding, R. Wong","doi":"10.1109/SCC.2013.83","DOIUrl":"https://doi.org/10.1109/SCC.2013.83","url":null,"abstract":"Nowadays many software services are hosted in the Cloud. When there are more requests on these services, there are also more queries sent to the underlying database. In order to keep up with the increasing workload, it is necessary to have multiple servers hosting the data. Some cloud providers offer the full data replication solution. However, this solution only works when the load mainly consists of the read requests, and when the number of write requests increases, it does not scale well. Although data decomposition has been widely used in data-intensive web sites, not much study has been done on how to decompose the underlying data of software services for the purpose of data replication. In this paper, we propose a data-decomposition-based partial replication model for software services. We devise an automatic algorithm for data decomposition under the constraint of the capacity limit of the host machines. We evaluate our approach from two aspects: scalability and performance, using two benchmarks: RUBiS and TPC-W. In the experiment, we test the algorithm using different workload inputs, and also compare our approach with the full data replication approach.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128036132","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}
Bo Hu, Yutao Ma, Liang-Jie Zhang, Chunxiao Xing, Jun Zou, Ping Xu
With the incredible popularity of cloud computing, the adoption of mass customization (MC) is significant for building a cloud computing system that could provide services provisioning in a manner of multi-tenancy. Because of lack of a standard architecture that supports MC development for cloud services, the existing metadata or model driven approaches have insufficient abilities to realize personalized requirements with mass production when applied to product development in large-scale enterprises. Aiming at these problems, this paper presents a novel MC-based development approach for enterprise-level business cloud services based on the specification of the Cloud Computing Reference Architecture (CCRA), and shares the practice about how the approach is applied to building Kingdee K/3 Collaboration Development Cloud (CDC). Successful practice has proved that by adopting our MC development approach, we can develop platforms and tools on the cloud at a low cost and more effectively.
{"title":"A CCRA Based Mass Customization Development for Cloud Services","authors":"Bo Hu, Yutao Ma, Liang-Jie Zhang, Chunxiao Xing, Jun Zou, Ping Xu","doi":"10.1109/SCC.2013.113","DOIUrl":"https://doi.org/10.1109/SCC.2013.113","url":null,"abstract":"With the incredible popularity of cloud computing, the adoption of mass customization (MC) is significant for building a cloud computing system that could provide services provisioning in a manner of multi-tenancy. Because of lack of a standard architecture that supports MC development for cloud services, the existing metadata or model driven approaches have insufficient abilities to realize personalized requirements with mass production when applied to product development in large-scale enterprises. Aiming at these problems, this paper presents a novel MC-based development approach for enterprise-level business cloud services based on the specification of the Cloud Computing Reference Architecture (CCRA), and shares the practice about how the approach is applied to building Kingdee K/3 Collaboration Development Cloud (CDC). Successful practice has proved that by adopting our MC development approach, we can develop platforms and tools on the cloud at a low cost and more effectively.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115384774","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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