To date, cloud service consumers usually cannot obtain customized quality guarantees according to their specific business constraints. However, on-demand service provisioning, a large number of services with multiple quality parameters, and a plethora of consumers prevent manual negotiations that are typically conducted in face-to-face meetings. Therefore, the design and realization of appropriate mechanisms for an automated negotiation of service level agreements plays a major role for cloud service markets to emerge. Moreover, from a business point of view, the negotiating parties also have specific demands on the performance of such mechanisms and want to obtain the best result that is achievable while not revealing their private information. In this paper, we propose a negotiation mechanism for the envisioned scenario, that allows to approach efficient results despite private information, and evaluate its performance.
{"title":"Customized Cloud Service Quality: Approaching Pareto-Efficient Outcomes in Concurrent Multiple-Issue Negotiations","authors":"M. Holloway, D. Schuller, R. Steinmetz","doi":"10.1109/UCC.2015.43","DOIUrl":"https://doi.org/10.1109/UCC.2015.43","url":null,"abstract":"To date, cloud service consumers usually cannot obtain customized quality guarantees according to their specific business constraints. However, on-demand service provisioning, a large number of services with multiple quality parameters, and a plethora of consumers prevent manual negotiations that are typically conducted in face-to-face meetings. Therefore, the design and realization of appropriate mechanisms for an automated negotiation of service level agreements plays a major role for cloud service markets to emerge. Moreover, from a business point of view, the negotiating parties also have specific demands on the performance of such mechanisms and want to obtain the best result that is achievable while not revealing their private information. In this paper, we propose a negotiation mechanism for the envisioned scenario, that allows to approach efficient results despite private information, and evaluate its performance.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124451157","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}
Alexander Ditter, D. Fey, Johannes Burner, J. Franke
The transition from conventionally generated energy towards renewable energy sources is an important topic. Besides the need for new concepts for power plants this paradigm shift also implies new structural requirements for the grid. Especially, the on premise generation of energy via photovoltaics underlines the most important difference as compared to conventional energy generation and distribution. The structure of the grid, for most parts, still follows the pattern of a strong root connection from the power plant, transforming the energy for lower power distribution branches multiple times on the way to the consumer. One of the main characteristics of renewable energy sources is its more consumer local and distributed generation, where existing distribution paths will become more and more of a bottleneck in the future. One way to solve this problem would be to relinquish the current grid structure and replace it with a new more suitable one. Yet, such a fundamental structural change would require a very long time to be installed and induce cost far beyond benefit. The current solution, commonly called smart grid, is mostly driven by information, such as energy consumption amounts and times of customers, in order to match energy generation, distribution and consumption. Our SmartEco approach goes one step further and makes it possible, especially for small and local energy suppliers, to even offload some of the energy surplus from the grid into individual customer homes.
{"title":"SmartEco: An Integrated Solution from Load Balancing between the Grid and Consumers to Local Energy Efficiency","authors":"Alexander Ditter, D. Fey, Johannes Burner, J. Franke","doi":"10.1109/UCC.2015.85","DOIUrl":"https://doi.org/10.1109/UCC.2015.85","url":null,"abstract":"The transition from conventionally generated energy towards renewable energy sources is an important topic. Besides the need for new concepts for power plants this paradigm shift also implies new structural requirements for the grid. Especially, the on premise generation of energy via photovoltaics underlines the most important difference as compared to conventional energy generation and distribution. The structure of the grid, for most parts, still follows the pattern of a strong root connection from the power plant, transforming the energy for lower power distribution branches multiple times on the way to the consumer. One of the main characteristics of renewable energy sources is its more consumer local and distributed generation, where existing distribution paths will become more and more of a bottleneck in the future. One way to solve this problem would be to relinquish the current grid structure and replace it with a new more suitable one. Yet, such a fundamental structural change would require a very long time to be installed and induce cost far beyond benefit. The current solution, commonly called smart grid, is mostly driven by information, such as energy consumption amounts and times of customers, in order to match energy generation, distribution and consumption. Our SmartEco approach goes one step further and makes it possible, especially for small and local energy suppliers, to even offload some of the energy surplus from the grid into individual customer homes.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"6 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114115646","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}
Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, capacity planning, and metering decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, we develop and validate a strategy based on exploiting the underlying structure of the signal being monitored to sparsify it prior to transmission to a monitoring station for analysis and logging. Specifically, predictive models are designed to estimate the signals of interest. These models are then used to obtain prediction errors -- the error between the signal and the corresponding estimate -- that are then treated as a sparse representation of the original signal while retaining key information. This transformation allows for far less data to be transmitted to the monitoring station, at which point the signal is reconstructed by simply using the prediction errors. We show that classical techniques such as principal component analysis (PCA) can be applied to the reconstructed signal for anomaly detection. Experimental results using the Trade6 and RuBBoS benchmarks indicate a significant reduction in overall transmission costs -- greater that 95% in some cases -- while retaining sufficient detection accuracy.
{"title":"Efficient Online Performance Monitoring of Computing Systems Using Predictive Models","authors":"Salvador DeCelles, M. Stamm, Nagarajan Kandasamy","doi":"10.1109/UCC.2015.31","DOIUrl":"https://doi.org/10.1109/UCC.2015.31","url":null,"abstract":"Performance monitoring of datacenters provides vital information for dynamic resource provisioning, anomaly detection, capacity planning, and metering decisions. Online monitoring, however, incurs a variety of costs: the very act of monitoring a system interferes with its performance, consuming network bandwidth and disk space. With the goal of reducing these costs, we develop and validate a strategy based on exploiting the underlying structure of the signal being monitored to sparsify it prior to transmission to a monitoring station for analysis and logging. Specifically, predictive models are designed to estimate the signals of interest. These models are then used to obtain prediction errors -- the error between the signal and the corresponding estimate -- that are then treated as a sparse representation of the original signal while retaining key information. This transformation allows for far less data to be transmitted to the monitoring station, at which point the signal is reconstructed by simply using the prediction errors. We show that classical techniques such as principal component analysis (PCA) can be applied to the reconstructed signal for anomaly detection. Experimental results using the Trade6 and RuBBoS benchmarks indicate a significant reduction in overall transmission costs -- greater that 95% in some cases -- while retaining sufficient detection accuracy.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127205908","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}
Provenance information are meta-data that summarize the history of the creation and the actions performed on an artefact e.g. data, process etc. Secure provenance is essential to improve data forensics, ensure accountability and increase the trust in the cloud. In this paper, we survey the existing cloud provenance management schemes and proposed security solutions. We investigate the current related security challenges resulting from the nature of the provenance model and the characteristics of the cloud and we finally identify potential research directions which we feel necessary t should be covered in order to build a secure cloud provenance for the next generation.
{"title":"Towards Secure Provenance in the Cloud: A Survey","authors":"Brian A. Lee, Abir Awad, Mirna Awad","doi":"10.1109/UCC.2015.102","DOIUrl":"https://doi.org/10.1109/UCC.2015.102","url":null,"abstract":"Provenance information are meta-data that summarize the history of the creation and the actions performed on an artefact e.g. data, process etc. Secure provenance is essential to improve data forensics, ensure accountability and increase the trust in the cloud. In this paper, we survey the existing cloud provenance management schemes and proposed security solutions. We investigate the current related security challenges resulting from the nature of the provenance model and the characteristics of the cloud and we finally identify potential research directions which we feel necessary t should be covered in order to build a secure cloud provenance for the next generation.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127716169","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}
Over the last few years there has been a massive proliferation of cloud providers, all using a set of different metrics to describe the service solutions that they offer. This results in a lack of comparability within and between services that precludes end users being able to select the most appropriate service for their needs, based upon their requirements. Here we outline an automated real-time benchmarking platform that can interact with cloud brokers to automatically select the most optimal cloud service provider for a given workload, based upon up to the minute benchmarking results generated, stored, collated and compared by the platform itself. This software package could save end users and enterprises significant amounts of time and money by ensuring that they always use the most appropriate VM flavor, on the most appropriate cloud service, every time they run a workload.
{"title":"Automated Cloud Brokerage Based Upon Continuous Real-Time Benchmarking","authors":"T. Connor, Joel Southgate","doi":"10.1109/UCC.2015.59","DOIUrl":"https://doi.org/10.1109/UCC.2015.59","url":null,"abstract":"Over the last few years there has been a massive proliferation of cloud providers, all using a set of different metrics to describe the service solutions that they offer. This results in a lack of comparability within and between services that precludes end users being able to select the most appropriate service for their needs, based upon their requirements. Here we outline an automated real-time benchmarking platform that can interact with cloud brokers to automatically select the most optimal cloud service provider for a given workload, based upon up to the minute benchmarking results generated, stored, collated and compared by the platform itself. This software package could save end users and enterprises significant amounts of time and money by ensuring that they always use the most appropriate VM flavor, on the most appropriate cloud service, every time they run a workload.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132689453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud providers can be organized in associations of multiple clouds with the objective of achieving all canonical properties expected from the cloud computing paradigm. However, problems inherent to the resource offering and consumption arise as one limitation to furnish cloud computing at its full extent. This work describes a multiple cloud architecture that utilizes a tournament model to promote the equilibrium between resource offering and consumption among participants, and therefore increase the interest of them in providing higher quality standards.
{"title":"Multiclouds Tournament Blueprint","authors":"M. M. Assis, L. Bittencourt","doi":"10.1109/UCC.2015.66","DOIUrl":"https://doi.org/10.1109/UCC.2015.66","url":null,"abstract":"Cloud providers can be organized in associations of multiple clouds with the objective of achieving all canonical properties expected from the cloud computing paradigm. However, problems inherent to the resource offering and consumption arise as one limitation to furnish cloud computing at its full extent. This work describes a multiple cloud architecture that utilizes a tournament model to promote the equilibrium between resource offering and consumption among participants, and therefore increase the interest of them in providing higher quality standards.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132738696","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}
Although pNFS (Parallel Network File System) improves the performance of NFS, to optimize the performance of pNFS, we need to deploy high-end network equipment and the data servers need high-end hard disks, which is expensive for many enterprises. As a result, in this work, we presented hpNFS (Hierarchically Parallel Network File System), which is a hierarchical approach of pNFS to decrease the implementation costs and fulfill storage requirements for enterprise. This work is conducted under the ILM (Information Lifecycle Management), where hpNFS is one of the modules of ILM solution. We left a few issues to be resolved by later stage.
{"title":"hpNFS: A Hierarchical Approach for the Parallel Network File System","authors":"Hsin-Tse Lu, P. Wu, Chia Hung Kao, Yi-Hsuan Lee","doi":"10.1109/UCC.2015.71","DOIUrl":"https://doi.org/10.1109/UCC.2015.71","url":null,"abstract":"Although pNFS (Parallel Network File System) improves the performance of NFS, to optimize the performance of pNFS, we need to deploy high-end network equipment and the data servers need high-end hard disks, which is expensive for many enterprises. As a result, in this work, we presented hpNFS (Hierarchically Parallel Network File System), which is a hierarchical approach of pNFS to decrease the implementation costs and fulfill storage requirements for enterprise. This work is conducted under the ILM (Information Lifecycle Management), where hpNFS is one of the modules of ILM solution. We left a few issues to be resolved by later stage.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131559807","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}
Over the last few years, the context of big data has gained a significant traction due to many factors. While the public cloud model had been deeply studied to face the increasing demand for large-scale data processing capabilities, many organizations are now focusing on the hybrid cloud model, where the classic scenario is enriched with a private (company owned) cloud -- e.g., for the management of sensible data. In this work, we propose HyMR, a policy to enable autonomic cloud bursting for clusters of virtual machines operating MapReduce jobs over a hybrid cloud. This policy -- together with an infrastructure level system for resource provisioning in hybrid clouds -- can be used to face the temporary (or permanent) lack of computational resources on the private cloud, allowing cloud bursting in the context of big data applications. By means of an empirical evaluation of the system scale-up/-down performance, we show that HyMR policy allows the user to significantly reduce the data-processing time.
{"title":"MapReduce over the Hybrid Cloud: A Novel Infrastructure Management Policy","authors":"Daniela Loreti, A. Ciampolini","doi":"10.1109/UCC.2015.33","DOIUrl":"https://doi.org/10.1109/UCC.2015.33","url":null,"abstract":"Over the last few years, the context of big data has gained a significant traction due to many factors. While the public cloud model had been deeply studied to face the increasing demand for large-scale data processing capabilities, many organizations are now focusing on the hybrid cloud model, where the classic scenario is enriched with a private (company owned) cloud -- e.g., for the management of sensible data. In this work, we propose HyMR, a policy to enable autonomic cloud bursting for clusters of virtual machines operating MapReduce jobs over a hybrid cloud. This policy -- together with an infrastructure level system for resource provisioning in hybrid clouds -- can be used to face the temporary (or permanent) lack of computational resources on the private cloud, allowing cloud bursting in the context of big data applications. By means of an empirical evaluation of the system scale-up/-down performance, we show that HyMR policy allows the user to significantly reduce the data-processing time.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134046678","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 complex biological systems, the hypothesis that bio-diversity contributes to stability or robustness is an active debate. The FP7 DIVERSIFY project tests whether this hypothesis holds for software systems, and explores the use of diversity as a heuristic to increase robustness in self-adaptive architectures. Inspired by Ecology, we present here a technique to evaluate diversity of software architectures and we report preliminary investigations of its correlation with robustness. Given existing cloud-based architectures, we artificially inject predefined levels of diversity and measure the resulting robustness. In four out of our five industrial case studies, a higher diversity appeared correlated with a higher robustness.
{"title":"Diversity: A Heuristic to Improve Robustness of Self-Adaptive Cloud Architectures","authors":"Franck Chauvel, Hui Song, Franck Fleurey","doi":"10.1109/UCC.2015.29","DOIUrl":"https://doi.org/10.1109/UCC.2015.29","url":null,"abstract":"In complex biological systems, the hypothesis that bio-diversity contributes to stability or robustness is an active debate. The FP7 DIVERSIFY project tests whether this hypothesis holds for software systems, and explores the use of diversity as a heuristic to increase robustness in self-adaptive architectures. Inspired by Ecology, we present here a technique to evaluate diversity of software architectures and we report preliminary investigations of its correlation with robustness. Given existing cloud-based architectures, we artificially inject predefined levels of diversity and measure the resulting robustness. In four out of our five industrial case studies, a higher diversity appeared correlated with a higher robustness.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134438618","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}
This paper outlines a generic process framework for customisation of software in cloud computing. The flexibility for client-specific customisation of the software offered by software-as-a-service (SaaS) is limited. The challenge for cloud providers is how they customise the software that is hosted in their SaaS model where multiple clients share the same software code with their specific customised needs.
{"title":"A Process Model for Customisation of Software in Multi-tenant SaaS Model","authors":"K. Khan, A. Nhlabatsi, N. Khan","doi":"10.1109/UCC.2015.73","DOIUrl":"https://doi.org/10.1109/UCC.2015.73","url":null,"abstract":"This paper outlines a generic process framework for customisation of software in cloud computing. The flexibility for client-specific customisation of the software offered by software-as-a-service (SaaS) is limited. The challenge for cloud providers is how they customise the software that is hosted in their SaaS model where multiple clients share the same software code with their specific customised needs.","PeriodicalId":381279,"journal":{"name":"2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114475342","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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