Autoscaling involves complex cloud operations that automate the provisioning and de-provisioning of cloud resources to support continuous development of customer services. Autoscaling depends on a number of decisions derived by aggregating metrics at the infrastructure and the platform level. In this paper, we review existing autoscaling techniques deployed in leading cloud providers. We identify core features and entities of the autoscaling operations as variables. We model these variables that quantify the interactions between these entities and incorporate workload time series data to calibrate the model. Hence the model allows proactive analysis of workload patterns and estimation of the responsiveness of the autoscaling operations. We demonstrate the use of this model with Google cluster trace data.
{"title":"Modeling the Autoscaling Operations in Cloud with Time Series Data","authors":"Mehran Khan, Yan Liu, H. Alipour, Samneet Singh","doi":"10.1109/SRDSW.2015.20","DOIUrl":"https://doi.org/10.1109/SRDSW.2015.20","url":null,"abstract":"Autoscaling involves complex cloud operations that automate the provisioning and de-provisioning of cloud resources to support continuous development of customer services. Autoscaling depends on a number of decisions derived by aggregating metrics at the infrastructure and the platform level. In this paper, we review existing autoscaling techniques deployed in leading cloud providers. We identify core features and entities of the autoscaling operations as variables. We model these variables that quantify the interactions between these entities and incorporate workload time series data to calibrate the model. Hence the model allows proactive analysis of workload patterns and estimation of the responsiveness of the autoscaling operations. We demonstrate the use of this model with Google cluster trace data.","PeriodicalId":415692,"journal":{"name":"2015 IEEE 34th Symposium on Reliable Distributed Systems Workshop (SRDSW)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771387","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}
Cognitive radio technology provides a framework for flexible way to utilize the white spaces using the various spectrum sharing techniques. Interference plays an important role in communication when the channels are shared by the licensed and unlicensed users. Further, the signal to interference plus noise ratio also provide the bounds for the channel capacity. For this the authors introduce a conflict graph based approach for optimal channel allocation in cognitive radio networks named as Conflict Graph based Channel Allocation(CGCA) scheme. The proposed CGCA scheme was simulated and observed that the CGCA scheme outperformed Interference Aware Channel Assignment (IACA) scheme in terms of channel allocation. The channel allocation of the proposed CGCA was observed to have increased by 19 channels, when the unlicensed users shared the network as compared to the IACA technique.
{"title":"Conflict Graph Based Channel Allocation in Cognitive Radio Networks","authors":"Vinesh Teotia, Vipin Kumar, S. Minz","doi":"10.1109/SRDSW.2015.19","DOIUrl":"https://doi.org/10.1109/SRDSW.2015.19","url":null,"abstract":"Cognitive radio technology provides a framework for flexible way to utilize the white spaces using the various spectrum sharing techniques. Interference plays an important role in communication when the channels are shared by the licensed and unlicensed users. Further, the signal to interference plus noise ratio also provide the bounds for the channel capacity. For this the authors introduce a conflict graph based approach for optimal channel allocation in cognitive radio networks named as Conflict Graph based Channel Allocation(CGCA) scheme. The proposed CGCA scheme was simulated and observed that the CGCA scheme outperformed Interference Aware Channel Assignment (IACA) scheme in terms of channel allocation. The channel allocation of the proposed CGCA was observed to have increased by 19 channels, when the unlicensed users shared the network as compared to the IACA technique.","PeriodicalId":415692,"journal":{"name":"2015 IEEE 34th Symposium on Reliable Distributed Systems Workshop (SRDSW)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131852971","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}
The Mobile-Edge Computing standard promises co-locating edge servers with mobile phone base stations. Web services running on this new ecosystem will have to address the challenges of this new model in order to see its benefits. In this work, we briefly discuss design guidelines for scaling strongly consistent geo-distributed databases to the MEC environment. Following these guidelines, we present the design of a MEC database tailored for a specific kind of web services and a protocol for ensuring transactional non-monotonic snapshot isolation (NMSI) at MEC scale.
{"title":"Scaling Geo-replicated Databases to the MEC Environment","authors":"Alejandro Z. Tomsic, Tyler Crain, M. Shapiro","doi":"10.1109/SRDSW.2015.13","DOIUrl":"https://doi.org/10.1109/SRDSW.2015.13","url":null,"abstract":"The Mobile-Edge Computing standard promises co-locating edge servers with mobile phone base stations. Web services running on this new ecosystem will have to address the challenges of this new model in order to see its benefits. In this work, we briefly discuss design guidelines for scaling strongly consistent geo-distributed databases to the MEC environment. Following these guidelines, we present the design of a MEC database tailored for a specific kind of web services and a protocol for ensuring transactional non-monotonic snapshot isolation (NMSI) at MEC scale.","PeriodicalId":415692,"journal":{"name":"2015 IEEE 34th Symposium on Reliable Distributed Systems Workshop (SRDSW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127106934","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}
The past decade has witnessed immense developments in the field of network computing thanks to the rise of the cloud computing paradigm, which enables shared access to a wealth of computing and storage resources without needing to own them. While cloud computing facilitates on-demand deployment, mobility and collaboration of services, mechanisms for enforcing security and performance constraints when accessing cloud services are still at an immature state. The highly dynamic nature of networks and clouds makes it difficult to guarantee any service level agreements. On the other hand, providing quality of service guarantees to users of mobile and cloud services that involve collaboration of multiple services is contingent on the existence of mechanisms that give accurate performance estimates and security features for each service involved in the composition. In this paper, we propose a distributed service monitoring and dynamic service composition model for network computing, which provides increased resiliency by adapting service configurations and service compositions to various types of changes in context. We also present a greedy dynamic service composition algorithm to reconfigure service orchestrations to meet user-specified performance and security requirements. Experiments with the proposed algorithm and the ease-of-deployment of the proposed model on standard cloud platforms show that it is a promising approach for agile and resilient network computing.
{"title":"A Distributed Monitoring and Reconfiguration Approach for Adaptive Network Computing","authors":"B. Bhargava, Pelin Angin, R. Ranchal, S. Lingayat","doi":"10.1109/SRDSW.2015.16","DOIUrl":"https://doi.org/10.1109/SRDSW.2015.16","url":null,"abstract":"The past decade has witnessed immense developments in the field of network computing thanks to the rise of the cloud computing paradigm, which enables shared access to a wealth of computing and storage resources without needing to own them. While cloud computing facilitates on-demand deployment, mobility and collaboration of services, mechanisms for enforcing security and performance constraints when accessing cloud services are still at an immature state. The highly dynamic nature of networks and clouds makes it difficult to guarantee any service level agreements. On the other hand, providing quality of service guarantees to users of mobile and cloud services that involve collaboration of multiple services is contingent on the existence of mechanisms that give accurate performance estimates and security features for each service involved in the composition. In this paper, we propose a distributed service monitoring and dynamic service composition model for network computing, which provides increased resiliency by adapting service configurations and service compositions to various types of changes in context. We also present a greedy dynamic service composition algorithm to reconfigure service orchestrations to meet user-specified performance and security requirements. Experiments with the proposed algorithm and the ease-of-deployment of the proposed model on standard cloud platforms show that it is a promising approach for agile and resilient network computing.","PeriodicalId":415692,"journal":{"name":"2015 IEEE 34th Symposium on Reliable Distributed Systems Workshop (SRDSW)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127066097","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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