Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00045
Guosheng Kang, Jianxun Liu, Buqing Cao, Yong Xiao
With the wide adoption of SOA (Service Oriented Architecture), a massive amount of Web services emerge on the Internet. Finding the desired services becomes a challenge. Thus, service recommendation has become of paramount research to relieve users' difficulty in service selection. Existing Web service recommendation approaches employ utility functions or skyline techniques with the assumption that users can provide numerical QoS (Quality of Service) preferences. However, in practice, it is hard for users, even for professional users, to provide specific QoS preferences in their service requests. Thus, how to effectively recommend services to users when their QoS preferences are uncertain is a challenging issue. To solve the problem, this paper focuses on the characteristics of user requirements, and proposes a diversified QoS-centric service recommendation approach for uncertain QoS preferences, by which a list of services with desired QoS fulfillment and diversity are produced. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approach.
{"title":"Diversified QoS-Centric Service Recommendation for Uncertain QoS Preferences","authors":"Guosheng Kang, Jianxun Liu, Buqing Cao, Yong Xiao","doi":"10.1109/SCC49832.2020.00045","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00045","url":null,"abstract":"With the wide adoption of SOA (Service Oriented Architecture), a massive amount of Web services emerge on the Internet. Finding the desired services becomes a challenge. Thus, service recommendation has become of paramount research to relieve users' difficulty in service selection. Existing Web service recommendation approaches employ utility functions or skyline techniques with the assumption that users can provide numerical QoS (Quality of Service) preferences. However, in practice, it is hard for users, even for professional users, to provide specific QoS preferences in their service requests. Thus, how to effectively recommend services to users when their QoS preferences are uncertain is a challenging issue. To solve the problem, this paper focuses on the characteristics of user requirements, and proposes a diversified QoS-centric service recommendation approach for uncertain QoS preferences, by which a list of services with desired QoS fulfillment and diversity are produced. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approach.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124418650","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}
Handwriting is a natural way to express idea and perceptual service of daily handwriting is in great need. However, current cloud computing paradigm suffers from slow response, excessive networking bandwidth surge and vulnerable security. We propose a hybrid computing architecture incorporating edge computing and wireless sensor network to deliver handwriting perceptual service. Instead of uploading all raw data to the cloud, we offload the handwriting detection and text recognition services to the edge devices. Most bandwidth can be saved. The edge devices are organized to form wireless sensor networks, which possesses high resilience to disasters or damages. Merely the semantic texts than handwritten images are submitted to the cloud and resultant prompts or decisions are sent back. Our computing architecture in general has a fast edge intelligence response with balanced energy utilization. We implement a specific case study to investigate its feasibility. The result show that our design works pretty well.
{"title":"Hybrid Architecture for Handwriting Perceptual Service based on Edge Computing","authors":"Tonghua Su, Huiyan Wen, Mingyue Zhang, Shuchen Liu, Zhongjie Wang, Xiaofei Xu","doi":"10.1109/SCC49832.2020.00066","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00066","url":null,"abstract":"Handwriting is a natural way to express idea and perceptual service of daily handwriting is in great need. However, current cloud computing paradigm suffers from slow response, excessive networking bandwidth surge and vulnerable security. We propose a hybrid computing architecture incorporating edge computing and wireless sensor network to deliver handwriting perceptual service. Instead of uploading all raw data to the cloud, we offload the handwriting detection and text recognition services to the edge devices. Most bandwidth can be saved. The edge devices are organized to form wireless sensor networks, which possesses high resilience to disasters or damages. Merely the semantic texts than handwritten images are submitted to the cloud and resultant prompts or decisions are sent back. Our computing architecture in general has a fast edge intelligence response with balanced energy utilization. We implement a specific case study to investigate its feasibility. The result show that our design works pretty well.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122727054","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}
Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00031
Yiwei Shan, Y. Qiao, Bing Li, Jian Wang
Crossover service is a new type of services in the modern service industry, which aims to provide users with value-added services through converging services across the boundaries of organizations, industries, value chains, and spaces. Convergence is an essential topic in the evolution from traditional services to crossover services. Where and how to converge to achieve user goals is a challenging issue in the convergence process. Towards this issue, in this paper, we focus our attention on the process-level convergence and propose a framework to converge business processes with consideration of the effect of involved roles and goals. In our framework, convergence rules and corresponding convergence operations are defined based on message flow partition and merging. Finally, a case study of a crossover service by converging services from a travel domain and an insurance domain, and an experiment conducted on five use cases, are presented to show the feasibility and efficiency of the proposed approach.
{"title":"A Process Convergence Approach for Crossover Services based on Message Flow Partition and Merging","authors":"Yiwei Shan, Y. Qiao, Bing Li, Jian Wang","doi":"10.1109/SCC49832.2020.00031","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00031","url":null,"abstract":"Crossover service is a new type of services in the modern service industry, which aims to provide users with value-added services through converging services across the boundaries of organizations, industries, value chains, and spaces. Convergence is an essential topic in the evolution from traditional services to crossover services. Where and how to converge to achieve user goals is a challenging issue in the convergence process. Towards this issue, in this paper, we focus our attention on the process-level convergence and propose a framework to converge business processes with consideration of the effect of involved roles and goals. In our framework, convergence rules and corresponding convergence operations are defined based on message flow partition and merging. Finally, a case study of a crossover service by converging services from a travel domain and an insurance domain, and an experiment conducted on five use cases, are presented to show the feasibility and efficiency of the proposed approach.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114392011","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}
Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00061
F. Fahmi, Pei-Shu Huang, Feng-Jian Wang
A microservice architecture is one of the vital assets for financial service industries on their digital transformation. With the architecture, a Service-Oriented Architecture (SOA) application can be composed of a number of smaller but independently concurrent running units for better performance and maintainability of the application. The correctness of artifact states in financial services is significant. Analyzing artifact operations inside each microservice of this application during the design phase is essential since an incorrect artifact state of abnormal artifact operation(s) may corrupt the whole application. In this paper, we identify the properties of artifact associated with a microservice architecture and present a method to detect the anomalies based on these properties.
{"title":"Detecting Artifact Anomalies in Microservice-Based Financial Applications","authors":"F. Fahmi, Pei-Shu Huang, Feng-Jian Wang","doi":"10.1109/SCC49832.2020.00061","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00061","url":null,"abstract":"A microservice architecture is one of the vital assets for financial service industries on their digital transformation. With the architecture, a Service-Oriented Architecture (SOA) application can be composed of a number of smaller but independently concurrent running units for better performance and maintainability of the application. The correctness of artifact states in financial services is significant. Analyzing artifact operations inside each microservice of this application during the design phase is essential since an incorrect artifact state of abnormal artifact operation(s) may corrupt the whole application. In this paper, we identify the properties of artifact associated with a microservice architecture and present a method to detect the anomalies based on these properties.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127675183","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}
Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00026
Junwen Liu, Shiyong Lu, D. Che
This paper provides a survey of the state-of-the-art workflow scheduling algorithms with the assumption of cloud computing being used as the underlying compute infrastructure in support of large-scale scientific workflows involving big data. The survey also reviews a few selected representative scientific workflow systems in light of usability, performance, popularity, and other prominent features. In contrast to existing related surveys, which most try to be comprehensive in coverage and inevitably fall short in the depth of their coverage on workflow scheduling, this survey puts an emphasis on the two dominant factors in workflow scheduling, the makespan and the monetary cost of workflow execution, resulted in a useful taxonomy of workflow scheduling algorithms as an additional contribution. This survey tries to maintain a good balance between width and depth in its coverage – after a broad review, it spotlights on selected top ten representative scheduling algorithms and top five workflow management systems leveraging cloud infrastructure with an emphasis on support for big data scientific workflows.
{"title":"A Survey of Modern Scientific Workflow Scheduling Algorithms and Systems in the Era of Big Data","authors":"Junwen Liu, Shiyong Lu, D. Che","doi":"10.1109/SCC49832.2020.00026","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00026","url":null,"abstract":"This paper provides a survey of the state-of-the-art workflow scheduling algorithms with the assumption of cloud computing being used as the underlying compute infrastructure in support of large-scale scientific workflows involving big data. The survey also reviews a few selected representative scientific workflow systems in light of usability, performance, popularity, and other prominent features. In contrast to existing related surveys, which most try to be comprehensive in coverage and inevitably fall short in the depth of their coverage on workflow scheduling, this survey puts an emphasis on the two dominant factors in workflow scheduling, the makespan and the monetary cost of workflow execution, resulted in a useful taxonomy of workflow scheduling algorithms as an additional contribution. This survey tries to maintain a good balance between width and depth in its coverage – after a broad review, it spotlights on selected top ten representative scheduling algorithms and top five workflow management systems leveraging cloud infrastructure with an emphasis on support for big data scientific workflows.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134444088","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}
Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00015
Shipeng Wang, Li-zhen Cui, Lei Liu, Xudong Lu, Qingzhong Li
With the increasing popularity of social networks, massive digital footprints of individuals in online service platforms are generated. As a result, an emerging technology namely personality trait analysis has drawn much attention. The prediction and analysis of personality trait is an efficient way to voting prediction, review analysis, decision analysis and marketing. The existing studies generally employ classification models while ignore the temporal property of digital footprints, which may lead to unsatisfactory results. To make an improvement, this paper proposes an effective method to predict the personality traits by taking the temporal factors into account through the use of Attention Recurrent Neural Network (AttRNN). The experimental results based on the dataset of 19000 Facebook volunteers suggest the proposed method is effective for predicting personality traits.
{"title":"Personality Traits Prediction Based on Users’ Digital Footprints in Social Networks via Attention RNN","authors":"Shipeng Wang, Li-zhen Cui, Lei Liu, Xudong Lu, Qingzhong Li","doi":"10.1109/SCC49832.2020.00015","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00015","url":null,"abstract":"With the increasing popularity of social networks, massive digital footprints of individuals in online service platforms are generated. As a result, an emerging technology namely personality trait analysis has drawn much attention. The prediction and analysis of personality trait is an efficient way to voting prediction, review analysis, decision analysis and marketing. The existing studies generally employ classification models while ignore the temporal property of digital footprints, which may lead to unsatisfactory results. To make an improvement, this paper proposes an effective method to predict the personality traits by taking the temporal factors into account through the use of Attention Recurrent Neural Network (AttRNN). The experimental results based on the dataset of 19000 Facebook volunteers suggest the proposed method is effective for predicting personality traits.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129539132","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 work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy.
{"title":"An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization","authors":"Naime Ranjbar Kermany, Weiliang Zhao, Jian Yang, Jia Wu, L. Pizzato","doi":"10.1109/SCC49832.2020.00074","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00074","url":null,"abstract":"In this work, we propose an ethical multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail services inclusion, and recommendation accuracy. Experimental results on real-world datasets show that the proposed method significantly improves the novelty and diversity of recommended services and the coverage of providers with minor loss of accuracy.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"247 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121424426","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}
Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00023
Ishtiaq Ahmed, S. Mofrad, Shiyong Lu, Changxin Bai, Fengwei Zhang, D. Che
Recently, cloud platforms play an essential role in large-scale big data analytics and especially running scientific workflows. In contrast to traditional on-premise computing environments, where the number of resources is bounded, cloud computing can provide practically unlimited resources to a workflow application based on a pay-as-you-go pricing model. One challenge of using cloud computing is the protection of the privacy of the confidential workflow’s tasks, whose proprietary algorithm implementations are intellectual properties of the respective stakeholders. Another one is the monetary cost optimization of executing workflows in the cloud while satisfying a user-defined deadline. In this paper, we use the Intel Software Guard eXtensions (SGX) as a Trusted Execution Environment (TEE) to support the confidentiality of individual workflow tasks. Based on this, we propose a deadline-constrained and SGX-aware workflow scheduling algorithm, called SEED (SGX, Efficient, Effective, Deadline Constrained), to address these two challenges. SEED features several heuristics, including exploiting the longest critical paths and reuse of extra times in existing virtual machine instances. Our experiments show that SEED outperforms the representative algorithm, IC-PCP, in most cases in monetary cost while satisfying the given user-defined deadline. To our best knowledge, this is the first workflow scheduling algorithm that considers protecting the confidentiality of workflow tasks in a public cloud computing environment.
{"title":"SEED: Confidential Big Data Workflow Scheduling with Intel SGX Under Deadline Constraints","authors":"Ishtiaq Ahmed, S. Mofrad, Shiyong Lu, Changxin Bai, Fengwei Zhang, D. Che","doi":"10.1109/SCC49832.2020.00023","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00023","url":null,"abstract":"Recently, cloud platforms play an essential role in large-scale big data analytics and especially running scientific workflows. In contrast to traditional on-premise computing environments, where the number of resources is bounded, cloud computing can provide practically unlimited resources to a workflow application based on a pay-as-you-go pricing model. One challenge of using cloud computing is the protection of the privacy of the confidential workflow’s tasks, whose proprietary algorithm implementations are intellectual properties of the respective stakeholders. Another one is the monetary cost optimization of executing workflows in the cloud while satisfying a user-defined deadline. In this paper, we use the Intel Software Guard eXtensions (SGX) as a Trusted Execution Environment (TEE) to support the confidentiality of individual workflow tasks. Based on this, we propose a deadline-constrained and SGX-aware workflow scheduling algorithm, called SEED (SGX, Efficient, Effective, Deadline Constrained), to address these two challenges. SEED features several heuristics, including exploiting the longest critical paths and reuse of extra times in existing virtual machine instances. Our experiments show that SEED outperforms the representative algorithm, IC-PCP, in most cases in monetary cost while satisfying the given user-defined deadline. To our best knowledge, this is the first workflow scheduling algorithm that considers protecting the confidentiality of workflow tasks in a public cloud computing environment.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122913094","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}
Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00062
C. Ardagna, M. Anisetti, B. Carminati, E. Damiani, E. Ferrari, Christian Rondanini
Lack of trustworthiness is a major limit of microservice-based systems, where service composition is mainly driven by functional requirements. In this paper, we propose an approach where composite service certification meets blockchain, to support continuous and trustworthy verification of non-functional requirements. A certification process for composite services is then introduced at the basis of an audit process aiming to support certificates with stable properties. Trustworthiness is built on the blockchain, used as a platform for coordinating collaboration among involved parties such as service orchestrators, certification authorities, and auditors.
{"title":"A Blockchain-based Trustworthy Certification Process for Composite Services","authors":"C. Ardagna, M. Anisetti, B. Carminati, E. Damiani, E. Ferrari, Christian Rondanini","doi":"10.1109/SCC49832.2020.00062","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00062","url":null,"abstract":"Lack of trustworthiness is a major limit of microservice-based systems, where service composition is mainly driven by functional requirements. In this paper, we propose an approach where composite service certification meets blockchain, to support continuous and trustworthy verification of non-functional requirements. A certification process for composite services is then introduced at the basis of an audit process aiming to support certificates with stable properties. Trustworthiness is built on the blockchain, used as a platform for coordinating collaboration among involved parties such as service orchestrators, certification authorities, and auditors.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126192919","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}
Pub Date : 2020-11-01DOI: 10.1109/SCC49832.2020.00033
Lei Liu, Xiang He, Zhiying Tu, Zhongjie Wang
Agile development and the loose coupling of microservices, make continuous delivery/deployment of large, complex service systems become much easier. The microservices are upgraded and released independently and have their own independent version trees. For compatibility, multiple versions of one microservice are to be deployed in the same system to offer slightly different functionalities to different users simultaneously. However, loosely-coupling does not mean multiple microservices keep absolutely independent but there are more or less dependencies among them, and such dependencies occur not only on functionalities but also on the version issue, too. Existing microservice frameworks have no enough capability for multi-version co-deployment and the corresponding version-oriented dependency management. In this paper, a Spring Cloud based framework called MV4MS is introduced for this challenge. It extracts version information from source codes of microservices, builds version dependencies, packs and deploys requisite versions of microservices, and routes user requests to desired versions at run-time. Architecture of MV4MS and detailed design of its components are elaborately introduced. Experiments are conducted in AWS cloud environment, and results show that our framework could reduce the complexity of multi-version microservice co-deployment and ensure the correctness of routing between multi-version microservice.
{"title":"MV4MS: A Spring Cloud based Framework for the Co-Deployment of Multi-Version Microservices","authors":"Lei Liu, Xiang He, Zhiying Tu, Zhongjie Wang","doi":"10.1109/SCC49832.2020.00033","DOIUrl":"https://doi.org/10.1109/SCC49832.2020.00033","url":null,"abstract":"Agile development and the loose coupling of microservices, make continuous delivery/deployment of large, complex service systems become much easier. The microservices are upgraded and released independently and have their own independent version trees. For compatibility, multiple versions of one microservice are to be deployed in the same system to offer slightly different functionalities to different users simultaneously. However, loosely-coupling does not mean multiple microservices keep absolutely independent but there are more or less dependencies among them, and such dependencies occur not only on functionalities but also on the version issue, too. Existing microservice frameworks have no enough capability for multi-version co-deployment and the corresponding version-oriented dependency management. In this paper, a Spring Cloud based framework called MV4MS is introduced for this challenge. It extracts version information from source codes of microservices, builds version dependencies, packs and deploys requisite versions of microservices, and routes user requests to desired versions at run-time. Architecture of MV4MS and detailed design of its components are elaborately introduced. Experiments are conducted in AWS cloud environment, and results show that our framework could reduce the complexity of multi-version microservice co-deployment and ensure the correctness of routing between multi-version microservice.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125666496","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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