Pub Date : 2013-12-02DOI: 10.1109/CloudCom.2013.121
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom, L. Nicolodi, Nick Preda, Lorenzo Miori, Matteo Angriman, Juha Rikkilä, Xiaofeng Wang, Karim Hamily, Sara Bugoloni
We present our ongoing work building a Raspberry Pi cluster consisting of 300 nodes. The unique characteristics of this single board computer pose several challenges, but also offer a number of interesting opportunities. On the one hand, a single Raspberry Pi can be purchased cheaply and has a low power consumption, which makes it possible to create an affordable and energy-efficient cluster. On the other hand, it lacks in computing power, which makes it difficult to run computationally intensive software on it. Nevertheless, by combining a large number of Raspberries into a cluster, this drawback can be (partially) offset. Here we report on the first important steps of creating our cluster: how to set up and configure the hardware and the system software, and how to monitor and maintain the system. We also discuss potential use cases for our cluster, the two most important being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
{"title":"Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment","authors":"P. Abrahamsson, S. Helmer, Nattakarn Phaphoom, L. Nicolodi, Nick Preda, Lorenzo Miori, Matteo Angriman, Juha Rikkilä, Xiaofeng Wang, Karim Hamily, Sara Bugoloni","doi":"10.1109/CloudCom.2013.121","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.121","url":null,"abstract":"We present our ongoing work building a Raspberry Pi cluster consisting of 300 nodes. The unique characteristics of this single board computer pose several challenges, but also offer a number of interesting opportunities. On the one hand, a single Raspberry Pi can be purchased cheaply and has a low power consumption, which makes it possible to create an affordable and energy-efficient cluster. On the other hand, it lacks in computing power, which makes it difficult to run computationally intensive software on it. Nevertheless, by combining a large number of Raspberries into a cluster, this drawback can be (partially) offset. Here we report on the first important steps of creating our cluster: how to set up and configure the hardware and the system software, and how to monitor and maintain the system. We also discuss potential use cases for our cluster, the two most important being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123382280","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 : 2013-12-02DOI: 10.1109/CloudCom.2013.15
Zhiqiang Ma, David Ke Hong, Lin Gu
Traditional cloud computing technologies, such as MapReduce, use file systems as the system-wide substrate for data storage and sharing. A distributed file system provides a global name space and stores data persistently, but it also introduces significant overhead. Several recent systems use DRAM to store data and tremendously improve the performance of cloud computing systems. However, both our own experience and related work indicate that a simple substitution of distributed DRAM for the file system does not provide a solid and viable foundation for data storage and processing in the data center environment, and the capacity of such systems is limited by the amount of physical memory in the cluster. To overcome the challenge, we construct VOLUME (Virtual On-Line Unified Memory Environment), a distributed virtual memory to unify the physical memory and disk resources on many compute nodes, to form a system-wide data substrate. The new substrate provides a general memory based abstraction, takes advantage of DRAM in the system to accelerate computation, and, transparent to programmers, scales the system to handle large datasets by swapping data to disks and remote servers. The evaluation results show that VOLUME is much faster than Hadoop/HDFS, and delivers 6-11x speedups on the adjacency list workload. VOLUME is faster than both Hadoop/HDFS and Spark/RDD for in-memory sorting. For kmeans clustering, VOLUME scales linearly to 160 compute nodes on the TH-1/GZ supercomputer.
{"title":"VOLUME: Enable Large-Scale In-Memory Computation on Commodity Clusters","authors":"Zhiqiang Ma, David Ke Hong, Lin Gu","doi":"10.1109/CloudCom.2013.15","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.15","url":null,"abstract":"Traditional cloud computing technologies, such as MapReduce, use file systems as the system-wide substrate for data storage and sharing. A distributed file system provides a global name space and stores data persistently, but it also introduces significant overhead. Several recent systems use DRAM to store data and tremendously improve the performance of cloud computing systems. However, both our own experience and related work indicate that a simple substitution of distributed DRAM for the file system does not provide a solid and viable foundation for data storage and processing in the data center environment, and the capacity of such systems is limited by the amount of physical memory in the cluster. To overcome the challenge, we construct VOLUME (Virtual On-Line Unified Memory Environment), a distributed virtual memory to unify the physical memory and disk resources on many compute nodes, to form a system-wide data substrate. The new substrate provides a general memory based abstraction, takes advantage of DRAM in the system to accelerate computation, and, transparent to programmers, scales the system to handle large datasets by swapping data to disks and remote servers. The evaluation results show that VOLUME is much faster than Hadoop/HDFS, and delivers 6-11x speedups on the adjacency list workload. VOLUME is faster than both Hadoop/HDFS and Spark/RDD for in-memory sorting. For kmeans clustering, VOLUME scales linearly to 160 compute nodes on the TH-1/GZ supercomputer.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116488570","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 : 2013-12-02DOI: 10.1109/CloudCom.2013.74
Martin Franke, A. Wuttig, T. Schlegel
In this work, we combine the field of cloud computing with assisted living to utilize an improved activity classification. The proposed system addresses the challenge of enabling heterogeneous, cloud-based sensors in small home environments. With a semantic and model-based approach, we seamlessly integrate Web services as virtual sensors and, therefore, increasing the accuracy of human activity classifiers. We solve the most important compelling issue of interoperability, by using generated wrapper classes and semantically unify all heterogeneous sensor events within the system. Our approach is evaluated with a test installation by means of the requirements for Sensor Web Infrastructures combined with the one for assisted living environments.
{"title":"Enabling Virtual, Cloud-Based Sensors in Assisted Living Environments","authors":"Martin Franke, A. Wuttig, T. Schlegel","doi":"10.1109/CloudCom.2013.74","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.74","url":null,"abstract":"In this work, we combine the field of cloud computing with assisted living to utilize an improved activity classification. The proposed system addresses the challenge of enabling heterogeneous, cloud-based sensors in small home environments. With a semantic and model-based approach, we seamlessly integrate Web services as virtual sensors and, therefore, increasing the accuracy of human activity classifiers. We solve the most important compelling issue of interoperability, by using generated wrapper classes and semantically unify all heterogeneous sensor events within the system. Our approach is evaluated with a test installation by means of the requirements for Sensor Web Infrastructures combined with the one for assisted living environments.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124452116","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 : 2013-12-02DOI: 10.1109/CloudCom.2013.164
Jonathan Stuart Ward, A. Barker
Monitoring is an essential aspect of maintaining and developing computer systems which increases in difficulty proportional to the size of the system. The need for robust monitoring tools has become more evident with the advent of cloud computing. Infrastructure as a Service (IaaS) clouds allow end users to deploy vast numbers of virtual machines as part of dynamic and transient architectures. Current monitoring solutions, including many of those in the open-source domain, rely on outdated concepts including manual configuration and centralised data collection and adapt poorly to membership churn. In this paper we propose the development of a cloud monitoring system to provide scalable and robust lookup, data collection and analysis services for large-scale cloud systems. In lieu of centrally managed monitoring we propose a multi-tier architecture using a layered gossip protocol to aggregate monitoring information and facilitate lookup, information collection and the identification of redundant capacity. This allows for a resource aware data collection and storage architecture that operates over the system being monitored. This in turn enables monitoring to be done in situ without the need for significant additional infrastructure to facilitate monitoring services. We evaluate this approach against alternative monitoring paradigms and demonstrate how our solution is well adapted to usage in a cloud-computing context.
{"title":"Varanus: In Situ Monitoring for Large Scale Cloud Systems","authors":"Jonathan Stuart Ward, A. Barker","doi":"10.1109/CloudCom.2013.164","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.164","url":null,"abstract":"Monitoring is an essential aspect of maintaining and developing computer systems which increases in difficulty proportional to the size of the system. The need for robust monitoring tools has become more evident with the advent of cloud computing. Infrastructure as a Service (IaaS) clouds allow end users to deploy vast numbers of virtual machines as part of dynamic and transient architectures. Current monitoring solutions, including many of those in the open-source domain, rely on outdated concepts including manual configuration and centralised data collection and adapt poorly to membership churn. In this paper we propose the development of a cloud monitoring system to provide scalable and robust lookup, data collection and analysis services for large-scale cloud systems. In lieu of centrally managed monitoring we propose a multi-tier architecture using a layered gossip protocol to aggregate monitoring information and facilitate lookup, information collection and the identification of redundant capacity. This allows for a resource aware data collection and storage architecture that operates over the system being monitored. This in turn enables monitoring to be done in situ without the need for significant additional infrastructure to facilitate monitoring services. We evaluate this approach against alternative monitoring paradigms and demonstrate how our solution is well adapted to usage in a cloud-computing context.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132322897","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 : 2013-12-02DOI: 10.1109/CloudCom.2013.109
Jonathan Scott, R. Laing, Graeme Hogg
This paper concerns the use of cloud-based photogrammetry to collect and process 3D information pertaining to the built heritage. Although modern surveying technologies such as laser scanning are available, their application may be prohibited by cost and the need for specialist knowledge in all but the largest projects. The paper uses a specific case study, that of a small vernacular building, to illustrate the manner in which affordable and cloud based photogrammetry can be used to record buildings. Much previous work has concentrated on the use of mobile cloud based 3D surveying technology to record small objects, yet this paper illustrates an application within the architectural heritage. Recording the intact built heritage in this way provides a rich data set, and one which can be readily incorporated within mainstream building information modelling packages.
{"title":"Built Heritage Digitization: Opportunities Afforded by Emerging Cloud Based Applications","authors":"Jonathan Scott, R. Laing, Graeme Hogg","doi":"10.1109/CloudCom.2013.109","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.109","url":null,"abstract":"This paper concerns the use of cloud-based photogrammetry to collect and process 3D information pertaining to the built heritage. Although modern surveying technologies such as laser scanning are available, their application may be prohibited by cost and the need for specialist knowledge in all but the largest projects. The paper uses a specific case study, that of a small vernacular building, to illustrate the manner in which affordable and cloud based photogrammetry can be used to record buildings. Much previous work has concentrated on the use of mobile cloud based 3D surveying technology to record small objects, yet this paper illustrates an application within the architectural heritage. Recording the intact built heritage in this way provides a rich data set, and one which can be readily incorporated within mainstream building information modelling packages.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132333941","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 : 2013-12-02DOI: 10.1109/CloudCom.2013.115
Nikolaos Tsalis, M. Theoharidou, D. Gritzalis
Cloud migration is a complex decision because of the multiple parameters that contribute for or against it (e.g. available budget, costs, performance, etc.). One of these parameters is information security and the investment required in order to ensure it. A potential client needs to evaluate various deployment options and Cloud Service Providers (CSP). This paper proposes a set of metrics focused on the assessment of security controls of a cloud deployment, in terms of cost and mitigation. Such an approach can support the client to decide whether she selects to deploy part of her services, data or infrastructure to a CSP, or not.
{"title":"Return on Security Investment for Cloud Platforms","authors":"Nikolaos Tsalis, M. Theoharidou, D. Gritzalis","doi":"10.1109/CloudCom.2013.115","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.115","url":null,"abstract":"Cloud migration is a complex decision because of the multiple parameters that contribute for or against it (e.g. available budget, costs, performance, etc.). One of these parameters is information security and the investment required in order to ensure it. A potential client needs to evaluate various deployment options and Cloud Service Providers (CSP). This paper proposes a set of metrics focused on the assessment of security controls of a cloud deployment, in terms of cost and mitigation. Such an approach can support the client to decide whether she selects to deploy part of her services, data or infrastructure to a CSP, or not.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133973669","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 : 2013-12-02DOI: 10.1109/CloudCom.2013.38
M. Airaj, Christophe Blanchet, S. Kenny, C. Loomis
Cloud infrastructures provide compelling features for scientific and engineering applications. Federated clouds additionally promise improved scalability via access to a larger pool of resources and improved service availability through geographically distributed redundant servers. Effective use of federated clouds requires the creation of portable appliances and consistent appliance management techniques. The Stratus Lab Marketplace, a platform-agnostic appliance registry, facilitates appliance management in a federated environment. This paper describes the Marketplace design goals, implementation, and security concerns. It also covers the planned improvements based on our experience of running this service in production for more than two years.
{"title":"Appliance Management for Federated Cloud Environments","authors":"M. Airaj, Christophe Blanchet, S. Kenny, C. Loomis","doi":"10.1109/CloudCom.2013.38","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.38","url":null,"abstract":"Cloud infrastructures provide compelling features for scientific and engineering applications. Federated clouds additionally promise improved scalability via access to a larger pool of resources and improved service availability through geographically distributed redundant servers. Effective use of federated clouds requires the creation of portable appliances and consistent appliance management techniques. The Stratus Lab Marketplace, a platform-agnostic appliance registry, facilitates appliance management in a federated environment. This paper describes the Marketplace design goals, implementation, and security concerns. It also covers the planned improvements based on our experience of running this service in production for more than two years.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133711564","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 : 2013-12-02DOI: 10.1109/CLOUDCOM.2013.47
Abhishek K. Gupta, L. Kalé, F. Gioachin, Verdi March, Chun Hui Suen, Bu-Sung Lee, P. Faraboschi, R. Kaufmann, D. Milojicic
Cloud computing is emerging as an alternative to supercomputers for some of the high-performance computing (HPC) applications that do not require a fully dedicated machine. With cloud as an additional deployment option, HPC users are faced with the challenges of dealing with highly heterogeneous resources, where the variability spans across a wide range of processor configurations, interconnections, virtualization environments, and pricing rates and models. In this paper, we take a holistic viewpoint to answer the question - why and who should choose cloud for HPC, for what applications, and how should cloud be used for HPC? To this end, we perform a comprehensive performance evaluation and analysis of a set of benchmarks and complex HPC applications on a range of platforms, varying from supercomputers to clouds. Further, we demonstrate HPC performance improvements in cloud using alternative lightweight virtualization mechanisms - thin VMs and OS-level containers, and hyper visor- and application-level CPU affinity. Next, we analyze the economic aspects and business models for HPC in clouds. We believe that is an important area that has not been sufficiently addressed by past research. Overall results indicate that current public clouds are cost-effective only at small scale for the chosen HPC applications, when considered in isolation, but can complement supercomputers using business models such as cloud burst and application-aware mapping.
{"title":"The Who, What, Why, and How of High Performance Computing in the Cloud","authors":"Abhishek K. Gupta, L. Kalé, F. Gioachin, Verdi March, Chun Hui Suen, Bu-Sung Lee, P. Faraboschi, R. Kaufmann, D. Milojicic","doi":"10.1109/CLOUDCOM.2013.47","DOIUrl":"https://doi.org/10.1109/CLOUDCOM.2013.47","url":null,"abstract":"Cloud computing is emerging as an alternative to supercomputers for some of the high-performance computing (HPC) applications that do not require a fully dedicated machine. With cloud as an additional deployment option, HPC users are faced with the challenges of dealing with highly heterogeneous resources, where the variability spans across a wide range of processor configurations, interconnections, virtualization environments, and pricing rates and models. In this paper, we take a holistic viewpoint to answer the question - why and who should choose cloud for HPC, for what applications, and how should cloud be used for HPC? To this end, we perform a comprehensive performance evaluation and analysis of a set of benchmarks and complex HPC applications on a range of platforms, varying from supercomputers to clouds. Further, we demonstrate HPC performance improvements in cloud using alternative lightweight virtualization mechanisms - thin VMs and OS-level containers, and hyper visor- and application-level CPU affinity. Next, we analyze the economic aspects and business models for HPC in clouds. We believe that is an important area that has not been sufficiently addressed by past research. Overall results indicate that current public clouds are cost-effective only at small scale for the chosen HPC applications, when considered in isolation, but can complement supercomputers using business models such as cloud burst and application-aware mapping.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128902317","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 : 2013-12-02DOI: 10.1109/CloudCom.2013.68
Qian Zhu, Teresa Tung, Qing Xie
With cloud computing, a cycle of fault diagnosis and recovery becomes the norm. There is a large amount of monitoring data and log events available, but it is hard to figure out which events or metrics are critical in fault diagnosis. Other approaches model faults as a deviation from normal behaviors, and thus are less applicable in cloud where changes in the environment may impact what is considered normal. In this work, we propose an adaptive and flexible fault diagnosis framework to automatically identify the key fault indicators and detect fault patterns. Leveraging ideas from social media, we represent the hierarchical relationships among metrics and events as well as how they relate to faults. We apply the EdgeRank algorithm to decide the key events that contribute to a fault. Our approach works across different environments to detect the potential faults. We evaluated our framework using a cloud-based enterprise system using a list of injected faults that vary from environmental (e.g. virtual machine or network) to application degradation. We considered both private and public clouds. Our solution achieves over 90% detection accuracy with modest overhead. A comparison of our approach shows it is more accurate than alternative approaches in the literature.
{"title":"Automatic Fault Diagnosis in Cloud Infrastructure","authors":"Qian Zhu, Teresa Tung, Qing Xie","doi":"10.1109/CloudCom.2013.68","DOIUrl":"https://doi.org/10.1109/CloudCom.2013.68","url":null,"abstract":"With cloud computing, a cycle of fault diagnosis and recovery becomes the norm. There is a large amount of monitoring data and log events available, but it is hard to figure out which events or metrics are critical in fault diagnosis. Other approaches model faults as a deviation from normal behaviors, and thus are less applicable in cloud where changes in the environment may impact what is considered normal. In this work, we propose an adaptive and flexible fault diagnosis framework to automatically identify the key fault indicators and detect fault patterns. Leveraging ideas from social media, we represent the hierarchical relationships among metrics and events as well as how they relate to faults. We apply the EdgeRank algorithm to decide the key events that contribute to a fault. Our approach works across different environments to detect the potential faults. We evaluated our framework using a cloud-based enterprise system using a list of injected faults that vary from environmental (e.g. virtual machine or network) to application degradation. We considered both private and public clouds. Our solution achieves over 90% detection accuracy with modest overhead. A comparison of our approach shows it is more accurate than alternative approaches in the literature.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131331358","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 : 2013-12-02DOI: 10.1109/CLOUDCOM.2013.18
D. Moldovan, G. Copil, Hong Linh Truong, S. Dustdar
Cloud computing has enabled a wide array of applications to be exposed as elastic cloud services. While the number of such services has rapidly increased, there is a lack of techniques for supporting cross-layered multi-level monitoring and analysis of elastic service behavior. In this paper we introduce novel concepts, namely elasticity space and elasticity pathway, for understanding elasticity of cloud services, and techniques for monitoring and evaluating them. We present MELA, a customizable framework, which enables service providers and developers to analyze cross-layered, multi-level elasticity of cloud services, from the whole cloud service to service units, based on service structure dependencies. Besides support for real-time elasticity analysis of cloud service behavior, MELA provides several customizable features for extracting functions and patterns that characterize that behavior. To illustrate the usefulness of MELA, we conduct several experiments with a realistic data-as-a-service in an M2M cloud platform.
{"title":"MELA: Monitoring and Analyzing Elasticity of Cloud Services","authors":"D. Moldovan, G. Copil, Hong Linh Truong, S. Dustdar","doi":"10.1109/CLOUDCOM.2013.18","DOIUrl":"https://doi.org/10.1109/CLOUDCOM.2013.18","url":null,"abstract":"Cloud computing has enabled a wide array of applications to be exposed as elastic cloud services. While the number of such services has rapidly increased, there is a lack of techniques for supporting cross-layered multi-level monitoring and analysis of elastic service behavior. In this paper we introduce novel concepts, namely elasticity space and elasticity pathway, for understanding elasticity of cloud services, and techniques for monitoring and evaluating them. We present MELA, a customizable framework, which enables service providers and developers to analyze cross-layered, multi-level elasticity of cloud services, from the whole cloud service to service units, based on service structure dependencies. Besides support for real-time elasticity analysis of cloud service behavior, MELA provides several customizable features for extracting functions and patterns that characterize that behavior. To illustrate the usefulness of MELA, we conduct several experiments with a realistic data-as-a-service in an M2M cloud platform.","PeriodicalId":198053,"journal":{"name":"2013 IEEE 5th International Conference on Cloud Computing Technology and Science","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114601897","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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