Pub Date : 2011-07-04DOI: 10.1109/SERVICES.2011.15
Ulrich Lampe
In the public perception, cloud computing is frequently associated with almost unlimited elasticity and scalability of computing capacity. However, Infrastructure as a Service in the form of virtual machines provides limited supplies of virtual resources, due to restrictions of the underlying physical hardware. At the same time, the execution of Software as a Service instances leads to a specific demand for these resources. Based on this observation, I introduce the Software Service Distribution Problem, i.e., the challenge of (cost-)efficiently distributing the execution of software service instances across available cloud infrastructure providers and virtual machine types under resource constraints. I outline my research approach, which aims at the development of optimization algorithms in the context of an integrated Software Service Distribution Broker, and report the progress made to date.
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Pub Date : 2011-07-04DOI: 10.1109/SERVICES.2011.41
Simon Harrer, Andreas Schönberger, G. Wirtz
ebXML BPSS (ebBP) is well-suited to specify Business-to-Business (B2B) interactions as choreographies of so-called Business Transactions. Web Services and WS-BPEL as dedicated interface technologies then can be used to provide the implementation of such choreographies. Tracking and ensuring the progress of choreographies calls for monitoring facilities that require gathering information from log data of the runtime systems that execute WS-BPEL processes. However, the information provided by WS-BPEL monitoring tools is fine-granular so that information about the actual progress in terms of choreographies must be extracted manually. Our approach streamlines the monitoring of ebBP Business Transactions leveraging model-driven engineering. First, hierarchical communicating automata are used to formalize Business Transactions. Second, WS-BPEL implementations of these automata are derived such that monitoring events are propagated to a monitoring service whenever a transition of the underlying automaton fires. Third, the monitoring service translates the monitoring events into choreography progress by visually highlighting the active and visited states within the hierarchical automata. It thus presents a user-friendly model that abstracts from the details of the implementing WS-BPEL processes. This makes tracking the current state of choreographies accessible to business users.
{"title":"A Model-Driven Approach for Monitoring ebBP BusinessTransactions","authors":"Simon Harrer, Andreas Schönberger, G. Wirtz","doi":"10.1109/SERVICES.2011.41","DOIUrl":"https://doi.org/10.1109/SERVICES.2011.41","url":null,"abstract":"ebXML BPSS (ebBP) is well-suited to specify Business-to-Business (B2B) interactions as choreographies of so-called Business Transactions. Web Services and WS-BPEL as dedicated interface technologies then can be used to provide the implementation of such choreographies. Tracking and ensuring the progress of choreographies calls for monitoring facilities that require gathering information from log data of the runtime systems that execute WS-BPEL processes. However, the information provided by WS-BPEL monitoring tools is fine-granular so that information about the actual progress in terms of choreographies must be extracted manually. Our approach streamlines the monitoring of ebBP Business Transactions leveraging model-driven engineering. First, hierarchical communicating automata are used to formalize Business Transactions. Second, WS-BPEL implementations of these automata are derived such that monitoring events are propagated to a monitoring service whenever a transition of the underlying automaton fires. Third, the monitoring service translates the monitoring events into choreography progress by visually highlighting the active and visited states within the hierarchical automata. It thus presents a user-friendly model that abstracts from the details of the implementing WS-BPEL processes. This makes tracking the current state of choreographies accessible to business users.","PeriodicalId":429726,"journal":{"name":"2011 IEEE World Congress on Services","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130591743","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 : 2011-07-01DOI: 10.1109/SERVICES.2011.52
Vitor Viana, Daniel de Oliveira, M. Mattoso
Cloud computing has emerged as a new paradigm that enables scientists to benefit from several distributed resources such as hardware and software. Clouds poses as an opportunity for scientists that need high performance computing infrastructure to execute their scientific experiments. Most of the experiments modeled as scientific workflows manage the execution of several activities and work with large amounts of data. In this way parallel techniques are often a key factor. Parallelizing a scientific workflow in the cloud environment is not trivial. One of the complex tasks is to define the number and types of virtual machines and to design the parallel execution strategy. Due to the number of options for configuring an environment it is a hard task to do it manually and it may produce negative impact on performance. This paper initially proposes a cost model based on concepts of quality of service (QoS) in clouds to help determining an adequate configuration of the environment according to restrictions imposed by scientists.
{"title":"Towards a Cost Model for Scheduling Scientific Workflows Activities in Cloud Environments","authors":"Vitor Viana, Daniel de Oliveira, M. Mattoso","doi":"10.1109/SERVICES.2011.52","DOIUrl":"https://doi.org/10.1109/SERVICES.2011.52","url":null,"abstract":"Cloud computing has emerged as a new paradigm that enables scientists to benefit from several distributed resources such as hardware and software. Clouds poses as an opportunity for scientists that need high performance computing infrastructure to execute their scientific experiments. Most of the experiments modeled as scientific workflows manage the execution of several activities and work with large amounts of data. In this way parallel techniques are often a key factor. Parallelizing a scientific workflow in the cloud environment is not trivial. One of the complex tasks is to define the number and types of virtual machines and to design the parallel execution strategy. Due to the number of options for configuring an environment it is a hard task to do it manually and it may produce negative impact on performance. This paper initially proposes a cost model based on concepts of quality of service (QoS) in clouds to help determining an adequate configuration of the environment according to restrictions imposed by scientists.","PeriodicalId":429726,"journal":{"name":"2011 IEEE World Congress on Services","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128348532","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 : 2011-07-01DOI: 10.1109/SERVICES.2011.49
S. G. Grivas, Marc Schaaf, Michael Kaschesky, Guillaume Bouchard
The viability of cloud computing for information-intensive tasks arising in real-time opinion mining and sentiment analysis of large online text streams is described. We show how a smart distributed architecture enables an efficient and scalable design for opinion mining on internet-based content that answers key challenges, such as integrating heterogeneous data sources and adapting to events through dynamic system configuration. In particular, we present a novel approach of semantic complex event processing in a cloud environment capturing different levels of information, such as event data (e.g. content from various heterogeneous, distributed sources) as well as associations identified during the opinion mining and sentiment analysis process (e.g. dynamic co-reference resolution).
{"title":"Cloud-Based Event-Processing Architecture for Opinion Mining","authors":"S. G. Grivas, Marc Schaaf, Michael Kaschesky, Guillaume Bouchard","doi":"10.1109/SERVICES.2011.49","DOIUrl":"https://doi.org/10.1109/SERVICES.2011.49","url":null,"abstract":"The viability of cloud computing for information-intensive tasks arising in real-time opinion mining and sentiment analysis of large online text streams is described. We show how a smart distributed architecture enables an efficient and scalable design for opinion mining on internet-based content that answers key challenges, such as integrating heterogeneous data sources and adapting to events through dynamic system configuration. In particular, we present a novel approach of semantic complex event processing in a cloud environment capturing different levels of information, such as event data (e.g. content from various heterogeneous, distributed sources) as well as associations identified during the opinion mining and sentiment analysis process (e.g. dynamic co-reference resolution).","PeriodicalId":429726,"journal":{"name":"2011 IEEE World Congress on Services","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129072450","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 : 2011-07-01DOI: 10.1109/SERVICES.2011.55
Reng Zeng, Xudong He, Wil M.P. van der Aalst
Scientific workflows have recently emerged as a new paradigm for representing and managing complex distributed scientific computations and are used to accelerate the pace of scientific discovery. In many disciplines, individual workflows are large and complicated due to the large quantities of data used. As such, the workflow construction is difficult or even impossible when relevant domain knowledge is missing or the workflows require collaboration within multiple domains. Recent efforts from scientific workflow community aiming at large-scale capturing of provenance present a new opportunity for using provenance to provide recommendations during building scientific workflows. This paper presents a method based on provenance to mine models for scientific workflows, including data and control dependency. The mining result can either suggest part of others' workflows for consideration, or make familiar part of workflow easily accessible, thus provide recommendation support for scientific workflow composition.
{"title":"A Method to Mine Workflows from Provenance for Assisting Scientific Workflow Composition","authors":"Reng Zeng, Xudong He, Wil M.P. van der Aalst","doi":"10.1109/SERVICES.2011.55","DOIUrl":"https://doi.org/10.1109/SERVICES.2011.55","url":null,"abstract":"Scientific workflows have recently emerged as a new paradigm for representing and managing complex distributed scientific computations and are used to accelerate the pace of scientific discovery. In many disciplines, individual workflows are large and complicated due to the large quantities of data used. As such, the workflow construction is difficult or even impossible when relevant domain knowledge is missing or the workflows require collaboration within multiple domains. Recent efforts from scientific workflow community aiming at large-scale capturing of provenance present a new opportunity for using provenance to provide recommendations during building scientific workflows. This paper presents a method based on provenance to mine models for scientific workflows, including data and control dependency. The mining result can either suggest part of others' workflows for consideration, or make familiar part of workflow easily accessible, thus provide recommendation support for scientific workflow composition.","PeriodicalId":429726,"journal":{"name":"2011 IEEE World Congress on Services","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130017154","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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