Stephan Kraft, G. Casale, Alin Jula, P. Kilpatrick, D. Greer
We propose a novel admission control policy for database queries. Our methodology uses system measurements of CPU utilization and query backlogs to determine interference between queries in execution on the same database server. Query interference may arise due to the concurrent access of hardware and software resources and can affect performance in positive and negative ways. Specifically our admission control considers the mix of jobs in service and prioritizes the query classes consuming CPU resources more efficiently. The policy ignores I/O subsystems and is therefore highly appropriate for in-memory databases. We validate our approach in trace-driven simulation and show performance increases of query slowdowns and throughputs compared to first-come first-served and shortest expected processing time first scheduling. Simulation experiments are parameterized from system traces of a SAP HANA in-memory database installation with TPC-H type workloads.
{"title":"WIQ: Work-Intensive Query Scheduling for In-Memory Database Systems","authors":"Stephan Kraft, G. Casale, Alin Jula, P. Kilpatrick, D. Greer","doi":"10.1109/CLOUD.2012.120","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.120","url":null,"abstract":"We propose a novel admission control policy for database queries. Our methodology uses system measurements of CPU utilization and query backlogs to determine interference between queries in execution on the same database server. Query interference may arise due to the concurrent access of hardware and software resources and can affect performance in positive and negative ways. Specifically our admission control considers the mix of jobs in service and prioritizes the query classes consuming CPU resources more efficiently. The policy ignores I/O subsystems and is therefore highly appropriate for in-memory databases. We validate our approach in trace-driven simulation and show performance increases of query slowdowns and throughputs compared to first-come first-served and shortest expected processing time first scheduling. Simulation experiments are parameterized from system traces of a SAP HANA in-memory database installation with TPC-H type workloads.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115207749","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}
Lei Yang, Jiannong Cao, Shaojie Tang, Tao Li, A. Chan
The advances in technologies of cloud computing and mobile computing enable the newly emerging mobile cloud computing paradigm. Three approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study the computation partitioning, which aims at optimizing the partition of a data stream application between mobile and cloud such that the application has maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the make span of executions in other applications. We first propose a framework to provide runtime support for the dynamic partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm to perform the optimal partition. We have conducted extensive simulations. The results show that our method can achieve more than 2X better performance over the execution without partitioning.
{"title":"A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing","authors":"Lei Yang, Jiannong Cao, Shaojie Tang, Tao Li, A. Chan","doi":"10.1145/2479942.2479946","DOIUrl":"https://doi.org/10.1145/2479942.2479946","url":null,"abstract":"The advances in technologies of cloud computing and mobile computing enable the newly emerging mobile cloud computing paradigm. Three approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study the computation partitioning, which aims at optimizing the partition of a data stream application between mobile and cloud such that the application has maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the make span of executions in other applications. We first propose a framework to provide runtime support for the dynamic partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm to perform the optimal partition. We have conducted extensive simulations. The results show that our method can achieve more than 2X better performance over the execution without partitioning.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114380202","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}
MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines. Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-of-gravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%.
{"title":"Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic","authors":"Mohammad Hammoud, M. S. Rehman, M. Sakr","doi":"10.1109/CLOUD.2012.92","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.92","url":null,"abstract":"MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines. Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-of-gravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114688975","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}
M. Rodríguez-Martínez, H. Valdivia, Jose Rivera, J. Seguel, Melvin Greer
Electronic health records (EHR) and electronic billing systems have been proposed as mechanisms to help curb the rising costs of health care in the United States. Given this scenario, our research efforts have targeted the idea of using open-source cloud computing technologies as the mechanism to build an affordable, secure, and scalable platform that supports billing as well as EHR operations. We call this platform MedBook, and in this paper we present the architecture and implementation status of this system. MedBook provides patients, health care providers, and health care payers a platform for exchange of information about EHR, billing activities, and benefits inquiries. MedBook is a Software-as-a-Service (SaaS) application built on top of open source technologies and running on an Infrastructure-as-a-Service platform. The client applications are mobile apps run from iPhone and iPad devices.
{"title":"MedBook: A Cloud-Based Healthcare Billing and Record Management System","authors":"M. Rodríguez-Martínez, H. Valdivia, Jose Rivera, J. Seguel, Melvin Greer","doi":"10.1109/CLOUD.2012.133","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.133","url":null,"abstract":"Electronic health records (EHR) and electronic billing systems have been proposed as mechanisms to help curb the rising costs of health care in the United States. Given this scenario, our research efforts have targeted the idea of using open-source cloud computing technologies as the mechanism to build an affordable, secure, and scalable platform that supports billing as well as EHR operations. We call this platform MedBook, and in this paper we present the architecture and implementation status of this system. MedBook provides patients, health care providers, and health care payers a platform for exchange of information about EHR, billing activities, and benefits inquiries. MedBook is a Software-as-a-Service (SaaS) application built on top of open source technologies and running on an Infrastructure-as-a-Service platform. The client applications are mobile apps run from iPhone and iPad devices.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123540246","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}
Sharing of physical infrastructure using virtualization presents an opportunity to improve the overall resource utilization. It is extremely important for a Software as a Service (SaaS) provider to understand the characteristics of the business application workload in order to size and place the virtual machine (VM) containing the application. A typical business application has a multi-tier architecture and the application workload is often predictable. Using the knowledge of the application architecture and statistical analysis of the workload, one can obtain an appropriate capacity and a good placement strategy for the corresponding VM. In this paper we propose a tool iCirrus-WoP that determines VM capacity and VM collocation possibilities for a given set of application workloads. We perform an empirical analysis of the approach on a set of business application workloads obtained from geographically distributed data centers. The iCirrus-WoP tool determines the fixed reserved capacity and a shared capacity of a VM which it can share with another collocated VM. Based on the workload variation, the tool determines if the VM should be statically allocated or needs a dynamic placement. To determine the collocation possibility, iCirrus-WoP performs a peak utilization analysis of the workloads. The empirical analysis reveals the possibility of collocating applications running in different time-zones. The VM capacity that the tool recommends, show a possibility of improving the overall utilization of the infrastructure by more than 70% if they are appropriately collocated.
{"title":"Analysis of SaaS Business Platform Workloads for Sizing and Collocation","authors":"R. Ganesan, S. Sarkar, Akshay Narayan","doi":"10.1109/CLOUD.2012.73","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.73","url":null,"abstract":"Sharing of physical infrastructure using virtualization presents an opportunity to improve the overall resource utilization. It is extremely important for a Software as a Service (SaaS) provider to understand the characteristics of the business application workload in order to size and place the virtual machine (VM) containing the application. A typical business application has a multi-tier architecture and the application workload is often predictable. Using the knowledge of the application architecture and statistical analysis of the workload, one can obtain an appropriate capacity and a good placement strategy for the corresponding VM. In this paper we propose a tool iCirrus-WoP that determines VM capacity and VM collocation possibilities for a given set of application workloads. We perform an empirical analysis of the approach on a set of business application workloads obtained from geographically distributed data centers. The iCirrus-WoP tool determines the fixed reserved capacity and a shared capacity of a VM which it can share with another collocated VM. Based on the workload variation, the tool determines if the VM should be statically allocated or needs a dynamic placement. To determine the collocation possibility, iCirrus-WoP performs a peak utilization analysis of the workloads. The empirical analysis reveals the possibility of collocating applications running in different time-zones. The VM capacity that the tool recommends, show a possibility of improving the overall utilization of the infrastructure by more than 70% if they are appropriately collocated.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"63 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126116907","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}
Madhavi Maiya, Sai Dasari, Ravi Yadav, S. Shivaprasad, D. Milojicic
Cloud computing has changed the way companies are managing their IT infrastructure and application development and/or deployment. While there are a number of Cloud platforms available, it is a challenge to identify a cloud platform that best suits the business needs. As a consequence Cloud service providers may end up selecting a platform that is either too low- or too high-level to manage, or does not offer the paradigms needed. In this paper, we introduce the Cloud manageability metrics and an approach to quantify the manageability of cloud platforms using the introduced metrics. We then use the metrics and approach to compare manageability of different cloud IaaS and PaaS platforms for specific use case scenarios. The values for the metrics are derived by executing the use cases in each platform using test environments. Based on the results of the comparison, we recommend a cloud platform that is best suited for different organizations needs with respect to cloud management. We expect that the proposed approach will help organizations to evaluate various cloud platforms and choose the platform that best matches their needs.
{"title":"Quantifying Manageability of Cloud Platforms","authors":"Madhavi Maiya, Sai Dasari, Ravi Yadav, S. Shivaprasad, D. Milojicic","doi":"10.1109/CLOUD.2012.111","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.111","url":null,"abstract":"Cloud computing has changed the way companies are managing their IT infrastructure and application development and/or deployment. While there are a number of Cloud platforms available, it is a challenge to identify a cloud platform that best suits the business needs. As a consequence Cloud service providers may end up selecting a platform that is either too low- or too high-level to manage, or does not offer the paradigms needed. In this paper, we introduce the Cloud manageability metrics and an approach to quantify the manageability of cloud platforms using the introduced metrics. We then use the metrics and approach to compare manageability of different cloud IaaS and PaaS platforms for specific use case scenarios. The values for the metrics are derived by executing the use cases in each platform using test environments. Based on the results of the comparison, we recommend a cloud platform that is best suited for different organizations needs with respect to cloud management. We expect that the proposed approach will help organizations to evaluate various cloud platforms and choose the platform that best matches their needs.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128369539","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 the new era of computing, software can be sold and delivered as a cloud service, and software renting has become as a strategic tool to compete in the market. Software renting has several advantages from the customer's point of view. However, for software providers it is challenging to ensure a profitable revenue stream when a license fee is replaced by a periodic rental fee. In this study, software renting was found to help the case firms to differentiate themselves from competitors; it also increased their competitive advantage by making the software available for a larger customer group. However, the negotiating power of larger customers impacted on software pricing, rental agreements, and the revenue model.
{"title":"Software Renting in the Era of Cloud Computing","authors":"Arto Ojala","doi":"10.1109/CLOUD.2012.71","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.71","url":null,"abstract":"In the new era of computing, software can be sold and delivered as a cloud service, and software renting has become as a strategic tool to compete in the market. Software renting has several advantages from the customer's point of view. However, for software providers it is challenging to ensure a profitable revenue stream when a license fee is replaced by a periodic rental fee. In this study, software renting was found to help the case firms to differentiate themselves from competitors; it also increased their competitive advantage by making the software available for a larger customer group. However, the negotiating power of larger customers impacted on software pricing, rental agreements, and the revenue model.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130744127","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}
Jingzheng Wu, Liping Ding, Yuqi Lin, N. Min-Allah, Yongji Wang
Cloud computing security has become the focus in information security, where much attention has been drawn to the user privacy leakage. Although isolation and some other security policies have been provided to protect the security of cloud computing, confidential information can be still stolen by timing channels without being detected. In this paper, a new method named XenPump is presented aiming to mitigate the threat of the timing channels by adding latency. XenPump is designed as a module located in hypervisor, monitoring the hypercalls used by the timing channels and adding latencies to lower the threat into an acceptable level. The prototype of XenPump has been implemented in Xen virtualization platform, and the performance is evaluated by the shared memory based timing channel. The experiment results show that XenPump can mitigate the threat of the timing channel by interrupting both the capacity and transmission accuracy. It is believed that after small extension, XenPump can mitigate the incoming timing channels.
{"title":"XenPump: A New Method to Mitigate Timing Channel in Cloud Computing","authors":"Jingzheng Wu, Liping Ding, Yuqi Lin, N. Min-Allah, Yongji Wang","doi":"10.1109/CLOUD.2012.28","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.28","url":null,"abstract":"Cloud computing security has become the focus in information security, where much attention has been drawn to the user privacy leakage. Although isolation and some other security policies have been provided to protect the security of cloud computing, confidential information can be still stolen by timing channels without being detected. In this paper, a new method named XenPump is presented aiming to mitigate the threat of the timing channels by adding latency. XenPump is designed as a module located in hypervisor, monitoring the hypercalls used by the timing channels and adding latencies to lower the threat into an acceptable level. The prototype of XenPump has been implemented in Xen virtualization platform, and the performance is evaluated by the shared memory based timing channel. The experiment results show that XenPump can mitigate the threat of the timing channel by interrupting both the capacity and transmission accuracy. It is believed that after small extension, XenPump can mitigate the incoming timing channels.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122592252","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}
F. Meng, Jian Wang, Changhua Sun, Dongxu Duan, Yi-Min Chee
To move applications to the cloud is not only a technical decision but also a business-oriented decision, in which both business and technical factors (e.g. transformation effort, multi-tenancy and auto-scaling enablement, scalability and extensibility) should be considered. However, existing approaches and tools do not support a consumable business oriented cloud transformation decision to select more suitable transformation solution with the right cloud delivery model, services type, affordable transformation effort and etc. In this paper, we introduce a practical three-step approach and a tool, CTA (Cloud Transformation Advisor) to enable decision makers to identify the most suitable cloud transformation solution to satisfy their business goals based on a well-structured cloud transformation knowledge base.
{"title":"Facilitating Business-Oriented Cloud Transformation Decision with Cloud Transformation Advisor","authors":"F. Meng, Jian Wang, Changhua Sun, Dongxu Duan, Yi-Min Chee","doi":"10.1109/CLOUD.2012.142","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.142","url":null,"abstract":"To move applications to the cloud is not only a technical decision but also a business-oriented decision, in which both business and technical factors (e.g. transformation effort, multi-tenancy and auto-scaling enablement, scalability and extensibility) should be considered. However, existing approaches and tools do not support a consumable business oriented cloud transformation decision to select more suitable transformation solution with the right cloud delivery model, services type, affordable transformation effort and etc. In this paper, we introduce a practical three-step approach and a tool, CTA (Cloud Transformation Advisor) to enable decision makers to identify the most suitable cloud transformation solution to satisfy their business goals based on a well-structured cloud transformation knowledge base.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122781495","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 isolation is a key requirement for application-level multi-tenant sharing hosting environments. It requires knowledge of the resource consumption of the various tenants. It is of great importance not only to be aware of the resource consumption of a tenant's given kind of transaction mix, but also to be able to be aware of the resource consumption of a given transaction type. However, direct measurement of CPU resource consumption requires instrumentation and incurs overhead. Recently, regression analysis has been applied to indirectly approximate resource consumption, but challenges still remain for cases with non-determinism and multicollinearity. In this work, we adapts Kalman filter to estimate CPU consumptions from easily observed data. We also propose techniques to deal with the non-determinism and the multicollinearity issues. Experimental results show that estimation results are in agreement with the corresponding measurements with acceptable estimation errors, especially with appropriately tuned filter settings taken into account. Experiments also demonstrate the utility of the approach in avoiding performance interference and CPU overloading.
{"title":"Application-Level CPU Consumption Estimation: Towards Performance Isolation of Multi-tenancy Web Applications","authors":"Wei Wang, Xiang Huang, Xiulei Qin, Wen-bo Zhang, Jun Wei, Hua Zhong","doi":"10.1109/CLOUD.2012.81","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.81","url":null,"abstract":"Performance isolation is a key requirement for application-level multi-tenant sharing hosting environments. It requires knowledge of the resource consumption of the various tenants. It is of great importance not only to be aware of the resource consumption of a tenant's given kind of transaction mix, but also to be able to be aware of the resource consumption of a given transaction type. However, direct measurement of CPU resource consumption requires instrumentation and incurs overhead. Recently, regression analysis has been applied to indirectly approximate resource consumption, but challenges still remain for cases with non-determinism and multicollinearity. In this work, we adapts Kalman filter to estimate CPU consumptions from easily observed data. We also propose techniques to deal with the non-determinism and the multicollinearity issues. Experimental results show that estimation results are in agreement with the corresponding measurements with acceptable estimation errors, especially with appropriately tuned filter settings taken into account. Experiments also demonstrate the utility of the approach in avoiding performance interference and CPU overloading.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122822542","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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