Pub Date : 2013-06-28DOI: 10.1109/SERVICES.2013.31
Shuai Lu, Yan Liu, Da Meng
To integrate wind and solar energy in electric systems, new technologies, such as energy storage and demand response, have been proposed to increase system flexibility. Control approaches and market rules are being developed accordingly to better manage these resources in multiple time scales. Therefore, models and software tools capable of performing hourly scheduling, intra-hour dispatch, and automatic generation control simulations are needed for testing these control approaches and for evaluating new market rules. At Pacific Northwest National Laboratory, we have developed an Electric System Intra-Hour Operation Simulator (ESIOS). Expanding this simulator as a service platform can benefit a larger community involved in exploring new models and controls and reducing the burden of maintaining a computing platform. Moreover the feedback and contribution from community users can help further improve the features of this simulation ecosystem. In this paper, we describe the function of this simulator. Based on our experience, we discuss the architecture design perspectives for transforming this simulator to an integrated collaborative service platform.
{"title":"Towards a Collaborative Simulation Platform for Renewable Energy Systems","authors":"Shuai Lu, Yan Liu, Da Meng","doi":"10.1109/SERVICES.2013.31","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.31","url":null,"abstract":"To integrate wind and solar energy in electric systems, new technologies, such as energy storage and demand response, have been proposed to increase system flexibility. Control approaches and market rules are being developed accordingly to better manage these resources in multiple time scales. Therefore, models and software tools capable of performing hourly scheduling, intra-hour dispatch, and automatic generation control simulations are needed for testing these control approaches and for evaluating new market rules. At Pacific Northwest National Laboratory, we have developed an Electric System Intra-Hour Operation Simulator (ESIOS). Expanding this simulator as a service platform can benefit a larger community involved in exploring new models and controls and reducing the burden of maintaining a computing platform. Moreover the feedback and contribution from community users can help further improve the features of this simulation ecosystem. In this paper, we describe the function of this simulator. Based on our experience, we discuss the architecture design perspectives for transforming this simulator to an integrated collaborative service platform.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","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":"129768216","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.24
Bo Jiang, Xiao-xiao Zhang, Weifeng Pan, Bo Hu
Cloud computing is an Internet-based computing. It relies on sharing computing resources which are delivered as services on the Internet. Web service is one of the most important types of services that can be used in cloud computing. But many of them may be similar in some functional or nonfunctional properties, making how to recommend a suitable web service a problem facing many developers. Researchers have taken the QoS attributes into consideration. However, their research is on the premise that all the recommended web services are compatible, i.e., the recommended web services can be composed with existing web services. It may not always be true. In this paper, we only take the compatibility of web services into consideration, and present a BIpartite Graph based Service Recommendation (BIGSIR) method to address the service compatibility problem. BIGSIR uses the historical usage data of web services to recommend web services to developers. Different from existing web service recommendation approaches, BIGSIR adopts a bipartite graph to visual the web services and the relationship between them. Based on the graph model, an effective recommendation algorithm is introduced to recommend the suitable web services. Our approach is evaluated on a dataset constructed from myExperiment, a search engine that contains about 1, 851 web services and 2, 000 workflows. Experimental results demonstrate that apart from some isolated web services or workflows, BIGSIR can obtain promising results. And we also explore the factors that will influence the performance of BIGSIR. This work not only provides a new dataset, but also highlights a new perspective for service recommendation, i.e. services as a bipartite network.
{"title":"BIGSIR: A Bipartite Graph Based Service Recommendation Method","authors":"Bo Jiang, Xiao-xiao Zhang, Weifeng Pan, Bo Hu","doi":"10.1109/SERVICES.2013.24","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.24","url":null,"abstract":"Cloud computing is an Internet-based computing. It relies on sharing computing resources which are delivered as services on the Internet. Web service is one of the most important types of services that can be used in cloud computing. But many of them may be similar in some functional or nonfunctional properties, making how to recommend a suitable web service a problem facing many developers. Researchers have taken the QoS attributes into consideration. However, their research is on the premise that all the recommended web services are compatible, i.e., the recommended web services can be composed with existing web services. It may not always be true. In this paper, we only take the compatibility of web services into consideration, and present a BIpartite Graph based Service Recommendation (BIGSIR) method to address the service compatibility problem. BIGSIR uses the historical usage data of web services to recommend web services to developers. Different from existing web service recommendation approaches, BIGSIR adopts a bipartite graph to visual the web services and the relationship between them. Based on the graph model, an effective recommendation algorithm is introduced to recommend the suitable web services. Our approach is evaluated on a dataset constructed from myExperiment, a search engine that contains about 1, 851 web services and 2, 000 workflows. Experimental results demonstrate that apart from some isolated web services or workflows, BIGSIR can obtain promising results. And we also explore the factors that will influence the performance of BIGSIR. This work not only provides a new dataset, but also highlights a new perspective for service recommendation, i.e. services as a bipartite network.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"82 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":"127883186","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.79
A. Santone, Valentina Intilangelo, Domenico Raucci
Model checking is a very useful method to verify concurrent and distributed systems which is traditionally applied to computer system design. We examine the applicability of model checking to validation of Business Processes that are mapped through the systems of Workflow Management. The use of model checking in business domain is affected by the state explosion problem, which says that the state space grows exponentially in the number of concurrent processes. In this paper we consider a property-based methodology developed to combat the state explosion problem. Our focus is two fold; firstly we show how model checking can be applied in the context of business modelling and analysis and secondly we evaluate and test the methodology using as a case study a real-world banking workflow of a loan origination process. Our investigations suggest that the business community, especially in the banking field, can benefit from this efficient methodology developed in formal methods since it can detect errors that were missed by traditional verification techniques, and being cost-efficient, it can be adopted as a standard quality assurance procedure. We show and discuss the experimental results obtained.
{"title":"Efficient Formal Verification in Banking Processes","authors":"A. Santone, Valentina Intilangelo, Domenico Raucci","doi":"10.1109/SERVICES.2013.79","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.79","url":null,"abstract":"Model checking is a very useful method to verify concurrent and distributed systems which is traditionally applied to computer system design. We examine the applicability of model checking to validation of Business Processes that are mapped through the systems of Workflow Management. The use of model checking in business domain is affected by the state explosion problem, which says that the state space grows exponentially in the number of concurrent processes. In this paper we consider a property-based methodology developed to combat the state explosion problem. Our focus is two fold; firstly we show how model checking can be applied in the context of business modelling and analysis and secondly we evaluate and test the methodology using as a case study a real-world banking workflow of a loan origination process. Our investigations suggest that the business community, especially in the banking field, can benefit from this efficient methodology developed in formal methods since it can detect errors that were missed by traditional verification techniques, and being cost-efficient, it can be adopted as a standard quality assurance procedure. We show and discuss the experimental results obtained.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","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":"117348733","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.13
Guang Lin, Binh Han, Jian Yin, I. Gorton
This paper explores cloud computing for large-scale data intensive scientific applications. Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. However, unlike typical commercial applications that often just requires a moderate amount of ordinary resources, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address these challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.
{"title":"Exploring Cloud Computing for Large-Scale Scientific Applications","authors":"Guang Lin, Binh Han, Jian Yin, I. Gorton","doi":"10.1109/SERVICES.2013.13","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.13","url":null,"abstract":"This paper explores cloud computing for large-scale data intensive scientific applications. Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. However, unlike typical commercial applications that often just requires a moderate amount of ordinary resources, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address these challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","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":"130191416","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.29
Changbing Chen, Xia Yang, Z. Bong, Sivadon Chaisiri, Bu-Sung Lee
This paper studies event recognition in a building based on the patterns of power consumption. It is a big challenge to identify what kinds of events happened in a building without additional devices such as camera and motion sensors, etc. Instead, we learn when and how the events happened from the historical record of power consumption and apply the lesson into the design of an event recognition system (ERS). The ERS will find out abnormal power usage to avoid wasting power, which leads to the energy savings in a building. The ERS involves big data analytics with a large size of dataset collected in a real time. Such a data intensive system is usually viewed as a workflow. A workflow management is a significant task of the system requiring data analysis in terms of the system scalability to maintain high throughput or fast speed analysis. We propose a workflow framework that allows users to perform remote and parallel workflow execution, whose tasks are efficiently scheduled and distributed in cloud computing environment. We run the ERS as a target system for the proposed framework with power consumption data (whose size is approximately 20GB or more) collected from each of over 240 rooms in a building at Dept. of Engineering, Tokyo University in 2011. We show that the proposed framework accelerates the speed of data analysis by providing scaling infrastructure and parallel processing feature utilizing cloud computing technologies. We also share our experience and results on the big data analytics and discuss how the studies contribute to achieve Green Campus.
{"title":"A Workflow Framework for Big Data Analytics: Event Recognition in a Building","authors":"Changbing Chen, Xia Yang, Z. Bong, Sivadon Chaisiri, Bu-Sung Lee","doi":"10.1109/SERVICES.2013.29","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.29","url":null,"abstract":"This paper studies event recognition in a building based on the patterns of power consumption. It is a big challenge to identify what kinds of events happened in a building without additional devices such as camera and motion sensors, etc. Instead, we learn when and how the events happened from the historical record of power consumption and apply the lesson into the design of an event recognition system (ERS). The ERS will find out abnormal power usage to avoid wasting power, which leads to the energy savings in a building. The ERS involves big data analytics with a large size of dataset collected in a real time. Such a data intensive system is usually viewed as a workflow. A workflow management is a significant task of the system requiring data analysis in terms of the system scalability to maintain high throughput or fast speed analysis. We propose a workflow framework that allows users to perform remote and parallel workflow execution, whose tasks are efficiently scheduled and distributed in cloud computing environment. We run the ERS as a target system for the proposed framework with power consumption data (whose size is approximately 20GB or more) collected from each of over 240 rooms in a building at Dept. of Engineering, Tokyo University in 2011. We show that the proposed framework accelerates the speed of data analysis by providing scaling infrastructure and parallel processing feature utilizing cloud computing technologies. We also share our experience and results on the big data analytics and discuss how the studies contribute to achieve Green Campus.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"12 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":"130322030","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.40
Puya Ghazizadeh, R. Mukkamala, S. Olariu
Data integrity is a major concern in outsourced IT services like cloud computing. Cloud computing has become popular because of cost reductions, time saving and mobility in service. However data integrity is still an unresolved issue in cloud services. We present an efficient mechanism for evaluating data integrity in cloud database-as-a-service. Our approach is based on inserting fake tuples into the database. In our model the owner of the data is the only trusted party and the server as a service provider or any other users are not trusted. We refer to distrusted party as a potentially malicious attacker. In our approach we define generating functions to create fake tuples with uniform distribution. Malicious attackers are not able to distinguish between fake tuples and real tuples. Our approach does not use encryption which makes it more efficient. We explore the strengths and limitations of these generating functions by describing our approach.
{"title":"Data Integrity Evaluation in Cloud Database-as-a-Service","authors":"Puya Ghazizadeh, R. Mukkamala, S. Olariu","doi":"10.1109/SERVICES.2013.40","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.40","url":null,"abstract":"Data integrity is a major concern in outsourced IT services like cloud computing. Cloud computing has become popular because of cost reductions, time saving and mobility in service. However data integrity is still an unresolved issue in cloud services. We present an efficient mechanism for evaluating data integrity in cloud database-as-a-service. Our approach is based on inserting fake tuples into the database. In our model the owner of the data is the only trusted party and the server as a service provider or any other users are not trusted. We refer to distrusted party as a potentially malicious attacker. In our approach we define generating functions to create fake tuples with uniform distribution. Malicious attackers are not able to distinguish between fake tuples and real tuples. Our approach does not use encryption which makes it more efficient. We explore the strengths and limitations of these generating functions by describing our approach.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"29 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":"123246028","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.69
S. Sakr, Anna Liu
Elasticity has been recognized as one of the most appealing features for users of cloud services. It represents the ability to dynamically and rapidly scale up or down the allocated computing resources on demand. In practice, it is difficult to understand the elasticity requirements of a given application and workload, and to assess if the elasticity provided by a cloud service will meet these requirements. In this experience paper, we take the position that a deep understanding of the capabilities of cloud-hosted database services is a crucial requirement for cloud users in order to bring forward the vision of deploying data-intensive applications on cloud platforms. We argue that it is important that cloud users become able to paint a comprehensive picture of the relationship between the capabilities of the different type of cloud database services, the application characteristics and workloads, and the geographical distribution of the application clients and the underlying database replicas. We discuss the current elasticity capabilities of the different categories of cloud database services and identify some of the main challenges for deploying a truly elastic database tier on cloud environments. Finally, we propose a benchmarking mechanism that can evaluate the elasticity capabilities of cloud database services in different application scenarios and workloads.
{"title":"Is Your Cloud-Hosted Database Truly Elastic?","authors":"S. Sakr, Anna Liu","doi":"10.1109/SERVICES.2013.69","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.69","url":null,"abstract":"Elasticity has been recognized as one of the most appealing features for users of cloud services. It represents the ability to dynamically and rapidly scale up or down the allocated computing resources on demand. In practice, it is difficult to understand the elasticity requirements of a given application and workload, and to assess if the elasticity provided by a cloud service will meet these requirements. In this experience paper, we take the position that a deep understanding of the capabilities of cloud-hosted database services is a crucial requirement for cloud users in order to bring forward the vision of deploying data-intensive applications on cloud platforms. We argue that it is important that cloud users become able to paint a comprehensive picture of the relationship between the capabilities of the different type of cloud database services, the application characteristics and workloads, and the geographical distribution of the application clients and the underlying database replicas. We discuss the current elasticity capabilities of the different categories of cloud database services and identify some of the main challenges for deploying a truly elastic database tier on cloud environments. Finally, we propose a benchmarking mechanism that can evaluate the elasticity capabilities of cloud database services in different application scenarios and workloads.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","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":"127443413","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.57
Wenhong Tian, Ruini Xue, Jun Cao, Qin Xiong, Yunjun Hu
This paper considers online energy-efficient scheduling of real-time virtual machines (VMs) for Cloud data centers. Each request is associated with a starttime, a end-time, a processing time and demand for a Physical Machine (PM) capacity. The goal is to schedule all of the requests non-preemptively in their start-timeend- time windows, subjecting to PM capacity constraints, such that total busy time of all used PMs is minimized (called MinTBT-ON for abbreviation). This problem is a fundamental scheduling problem for parallel jobs allocation on mutliple machines, it has important applications in power-aware scheduling in cloud computing, optical network design and customer service systems and other related areas. Offline scheduling to minimize busy time is NP-hard already in the special case where all jobs have the same processing time and can be scheduled in a fixed time interval. One best-known result for MinTBT-ON problem is a g-competitive algorithm for general instances using First-Fit algorithm for unit-size jobs, where g is the total capacity of a PM. In this paper, a B-competitive algorithm, GRID is proposed and proved for general case, where B is a natural number and 1 <; B <; g. More results are obtained and applied to Cloud computing to improve energy-efficiency.
{"title":"An Energy-Efficient Online Parallel Scheduling Algorithm for Cloud Data Centers","authors":"Wenhong Tian, Ruini Xue, Jun Cao, Qin Xiong, Yunjun Hu","doi":"10.1109/SERVICES.2013.57","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.57","url":null,"abstract":"This paper considers online energy-efficient scheduling of real-time virtual machines (VMs) for Cloud data centers. Each request is associated with a starttime, a end-time, a processing time and demand for a Physical Machine (PM) capacity. The goal is to schedule all of the requests non-preemptively in their start-timeend- time windows, subjecting to PM capacity constraints, such that total busy time of all used PMs is minimized (called MinTBT-ON for abbreviation). This problem is a fundamental scheduling problem for parallel jobs allocation on mutliple machines, it has important applications in power-aware scheduling in cloud computing, optical network design and customer service systems and other related areas. Offline scheduling to minimize busy time is NP-hard already in the special case where all jobs have the same processing time and can be scheduled in a fixed time interval. One best-known result for MinTBT-ON problem is a g-competitive algorithm for general instances using First-Fit algorithm for unit-size jobs, where g is the total capacity of a PM. In this paper, a B-competitive algorithm, GRID is proposed and proved for general case, where B is a natural number and 1 <; B <; g. More results are obtained and applied to Cloud computing to improve energy-efficiency.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"2005 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":"125816577","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.52
K. Rajaraman, Le Duy Ngan, Yuzhang Feng, Anitha Veeramani, Joel Koo Chong En, C. C. Keong, F. S. Tsai, A. Andrzejak
Return on investment is a critical decision factor for end-users going for cloud deployments. However, major cloud vendors typically provide a myriad of interdependent cloud service options in a variety of purchasing models, that severely complicates cost estimation and optimization. In this paper, we propose a novel Amazon EC2 cost optimization system, called EC2 Bargain Hunter, that innovatively combines services and cloud computing principles with ideas from semantic technologies. The system supports the entire-range of EC2 instance types, and can be used in real-time to perform live cost optimization. We demonstrate that unprecedented cost savings, by a factor of 30, on Amazon EC2 offerings can be found with this system in a few clicks. Furthermore, our approach can be adapted to other IaaS providers, which enables truly real-life cloud cost optimization and thus is a significant step towards making the cloud really cost-effective for the end-users.
{"title":"EC2BargainHunter: It's Easy to Hunt for Cost Savings on Amazon EC2!","authors":"K. Rajaraman, Le Duy Ngan, Yuzhang Feng, Anitha Veeramani, Joel Koo Chong En, C. C. Keong, F. S. Tsai, A. Andrzejak","doi":"10.1109/SERVICES.2013.52","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.52","url":null,"abstract":"Return on investment is a critical decision factor for end-users going for cloud deployments. However, major cloud vendors typically provide a myriad of interdependent cloud service options in a variety of purchasing models, that severely complicates cost estimation and optimization. In this paper, we propose a novel Amazon EC2 cost optimization system, called EC2 Bargain Hunter, that innovatively combines services and cloud computing principles with ideas from semantic technologies. The system supports the entire-range of EC2 instance types, and can be used in real-time to perform live cost optimization. We demonstrate that unprecedented cost savings, by a factor of 30, on Amazon EC2 offerings can be found with this system in a few clicks. Furthermore, our approach can be adapted to other IaaS providers, which enables truly real-life cloud cost optimization and thus is a significant step towards making the cloud really cost-effective for the end-users.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"115 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":"126862435","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 : 2013-06-28DOI: 10.1109/SERVICES.2013.75
George Chatzikonstantinou, Michael Athanasopoulos, K. Kontogiannis
Since its inception, Service Orientation allowed for distributed clients to invoke remote operations utilizing standardized protocols, programming paradigms and architectures. Furthermore, the problem of compiling complex service compositions, based on contextual information and user preferences, has been also extensively investigated by the research community. However, these techniques are mostly used within a single, or within coupled service domains that utilize predefined orchestration and composition service flows. In this paper, we propose an approach whereby service providers can specify complex service tasks as collections of goal model templates that can be instantiated and customized by the invoking clients. A reasoning process evaluates whether instantiated goals can be fulfilled based on the clients selections and consequently generates service flows that are compliant to the goal model and to the clients preferences. The major difference from existing context aware service computing frameworks is the introduction of a reasoning process that allows for the evaluation of various and possibly synergetic client goals and the on-time initiation and enactment of goal compliant service compositions. A proof of concept prototype has been implemented utilizing SOA technologies for service invocation and flow control.
{"title":"Towards a Goal Driven Task Personalization Specification Framework","authors":"George Chatzikonstantinou, Michael Athanasopoulos, K. Kontogiannis","doi":"10.1109/SERVICES.2013.75","DOIUrl":"https://doi.org/10.1109/SERVICES.2013.75","url":null,"abstract":"Since its inception, Service Orientation allowed for distributed clients to invoke remote operations utilizing standardized protocols, programming paradigms and architectures. Furthermore, the problem of compiling complex service compositions, based on contextual information and user preferences, has been also extensively investigated by the research community. However, these techniques are mostly used within a single, or within coupled service domains that utilize predefined orchestration and composition service flows. In this paper, we propose an approach whereby service providers can specify complex service tasks as collections of goal model templates that can be instantiated and customized by the invoking clients. A reasoning process evaluates whether instantiated goals can be fulfilled based on the clients selections and consequently generates service flows that are compliant to the goal model and to the clients preferences. The major difference from existing context aware service computing frameworks is the introduction of a reasoning process that allows for the evaluation of various and possibly synergetic client goals and the on-time initiation and enactment of goal compliant service compositions. A proof of concept prototype has been implemented utilizing SOA technologies for service invocation and flow control.","PeriodicalId":169370,"journal":{"name":"2013 IEEE Ninth World Congress on Services","volume":"7 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":"116019798","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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