Pub Date : 2014-12-15DOI: 10.1109/CloudCom.2014.22
Vimalraj Venkatesan, Y. Tay, Yi Zhang, Q. Wei
Resource allocation is fundamental to cloud computing, where the memory hierarchy is deep. Space allocation in this hierarchy calls for a model to determine how provisioning at one level affects performance at a lower level. This paper presents a 3-level model that relates the Miss Ratio Curves for two caches at adjacent levels. The model is tested with NEXTmem, which is a transcendent memory used by a Xen hypervisor to cache pages for virtual machines. NEXTmem has a DRAM level and a nonvolatile memory level. The test runs DaCapo benchmarks and shows that the model can be used to enforce fairness at one level, and latency bounds at another level.
{"title":"A 3-Level Cache Miss Model for a Nonvolatile Extension to Transcendent Memory","authors":"Vimalraj Venkatesan, Y. Tay, Yi Zhang, Q. Wei","doi":"10.1109/CloudCom.2014.22","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.22","url":null,"abstract":"Resource allocation is fundamental to cloud computing, where the memory hierarchy is deep. Space allocation in this hierarchy calls for a model to determine how provisioning at one level affects performance at a lower level. This paper presents a 3-level model that relates the Miss Ratio Curves for two caches at adjacent levels. The model is tested with NEXTmem, which is a transcendent memory used by a Xen hypervisor to cache pages for virtual machines. NEXTmem has a DRAM level and a nonvolatile memory level. The test runs DaCapo benchmarks and shows that the model can be used to enforce fairness at one level, and latency bounds at another level.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132299671","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.119
Kebing Wang, Zhaojuan Bian, Qian Chen, Ren Wang, Gen Xu
In the era of big data, Hive has quickly gained popularity for its superior capability to manage and analyze very large datasets, both structured and unstructured, residing in distributed storage systems. However, great opportunity comes with great challenges: Hive query performance is impacted by many factors which makes capacity planning and tuning for Hive cluster extremely difficult. These factors include system software stacks (Hive, MapReduce framework, JVM and OS), cluster hardware configurations (processor, memory, storage, and network) and HIVE data models and distributions. Current planning methods are mostly trial-and-error or very high-level estimation based. These approaches are far from efficient and accurate, especially with the increasing software stack complexity, hardware diversity, and unavoidable data skew in distributed database system. In this paper, we propose a Hive simulation framework based on CSMethod, which simulates the whole hive query execution life cycle, including query plan generation and MapReduce task execution. The framework is validated using typical query operations with varying changes in hardware, software and workload parameters, showing high accuracy and fast simulation speed. We also demonstrate the application of this framework with two real-world use cases: helping customers to perform capacity planning and estimate business query response time before system provisioning.
{"title":"Simulating Hive Cluster for Deployment Planning, Evaluation and Optimization","authors":"Kebing Wang, Zhaojuan Bian, Qian Chen, Ren Wang, Gen Xu","doi":"10.1109/CloudCom.2014.119","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.119","url":null,"abstract":"In the era of big data, Hive has quickly gained popularity for its superior capability to manage and analyze very large datasets, both structured and unstructured, residing in distributed storage systems. However, great opportunity comes with great challenges: Hive query performance is impacted by many factors which makes capacity planning and tuning for Hive cluster extremely difficult. These factors include system software stacks (Hive, MapReduce framework, JVM and OS), cluster hardware configurations (processor, memory, storage, and network) and HIVE data models and distributions. Current planning methods are mostly trial-and-error or very high-level estimation based. These approaches are far from efficient and accurate, especially with the increasing software stack complexity, hardware diversity, and unavoidable data skew in distributed database system. In this paper, we propose a Hive simulation framework based on CSMethod, which simulates the whole hive query execution life cycle, including query plan generation and MapReduce task execution. The framework is validated using typical query operations with varying changes in hardware, software and workload parameters, showing high accuracy and fast simulation speed. We also demonstrate the application of this framework with two real-world use cases: helping customers to perform capacity planning and estimate business query response time before system provisioning.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133154368","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 : 2014-12-15DOI: 10.1109/CLOUDCOM.2014.120
Shanjian Tang, Bu-Sung Lee, Bingsheng He
Recent trends indicate that the pay-as-you-go Infrastructure-as-a-Service (IaaS) cloud computing has become a popular platform for big data processing applications, due to its merits of accessibility, elasticity and flexibility. However, the resource demands of processing workloads are often varying over time for individual users, implying that it is hard for a user to keep the high resource utilization for cost efficiency all the time. Resource sharing is a classic and effective approach to improve the resource utilization via consolidating multiple users' workloads. However, we show that, current existing fair policies such as max-min fairness, widely adopted and implemented in many popular big data processing systems including YARN, Spark, Mesos, and Dryad, are not suitable for pay-as-you-go cloud computing. We show that it is because of their memory less allocation feature which can arise a series of problems in the pay-as-you-go cloud environment, namely, cost-inefficient workload submission, untruthfulness and resource-as-you-pay unfairness. This paper presents these problems and outlines our plans to address them for pay-as-you-go cloud computing. We introduce our preliminary work done on the single-resource fairness and our ongoing work for multi-resource fairness, and outline our future work.
{"title":"Towards Economic Fairness for Big Data Processing in Pay-as-You-Go Cloud Computing","authors":"Shanjian Tang, Bu-Sung Lee, Bingsheng He","doi":"10.1109/CLOUDCOM.2014.120","DOIUrl":"https://doi.org/10.1109/CLOUDCOM.2014.120","url":null,"abstract":"Recent trends indicate that the pay-as-you-go Infrastructure-as-a-Service (IaaS) cloud computing has become a popular platform for big data processing applications, due to its merits of accessibility, elasticity and flexibility. However, the resource demands of processing workloads are often varying over time for individual users, implying that it is hard for a user to keep the high resource utilization for cost efficiency all the time. Resource sharing is a classic and effective approach to improve the resource utilization via consolidating multiple users' workloads. However, we show that, current existing fair policies such as max-min fairness, widely adopted and implemented in many popular big data processing systems including YARN, Spark, Mesos, and Dryad, are not suitable for pay-as-you-go cloud computing. We show that it is because of their memory less allocation feature which can arise a series of problems in the pay-as-you-go cloud environment, namely, cost-inefficient workload submission, untruthfulness and resource-as-you-pay unfairness. This paper presents these problems and outlines our plans to address them for pay-as-you-go cloud computing. We introduce our preliminary work done on the single-resource fairness and our ongoing work for multi-resource fairness, and outline our future work.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133159549","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.123
M. Jaatun, Siani Pearson, Frederic Gittler, R. Leenes
In order to be an accountable organisation, Cloud Providers need to commit to being responsible stewards of other people's information. This implies demonstrating both willingness and capacity for such stewardship. This paper outlines the fundamental requirements that must be met by accountable organisations, and sketches what kind of tools, mechanisms and guidelines support this in practice.
{"title":"Towards Strong Accountability for Cloud Service Providers","authors":"M. Jaatun, Siani Pearson, Frederic Gittler, R. Leenes","doi":"10.1109/CloudCom.2014.123","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.123","url":null,"abstract":"In order to be an accountable organisation, Cloud Providers need to commit to being responsible stewards of other people's information. This implies demonstrating both willingness and capacity for such stewardship. This paper outlines the fundamental requirements that must be met by accountable organisations, and sketches what kind of tools, mechanisms and guidelines support this in practice.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122071729","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.91
S. Abolfazli, Zohreh Sanaei, A. Gani, Feng Xia, Wei-Ming Lin
Mobile devices, especially smartphones are increasingly gaining ground in several domains, particularly healthcare, tele-monitoring, and education to perform Resource-intensive Mobile Applications (RiMA). However, constrained resources, especially CPU and battery hinder their successful adoption. Mobile Cloud Computing (MCC) aims to augment computational capabilities of resource-constraint mobile devices and conserve their native resources by remotely performing intensive tasks. In typical MCC solutions, intensive tasks are offloaded to distant VM-based cloud data centers or cloudlets whose exploitation originates long WAN latency and/or virtualization overhead degrading RiMA execution efficiency. In this paper, a lightweight Resource-oriented MCC (RMCC) architecture is proposed that exploits resources of plethora of Adjacent Service-based Mobile Cloudlets (ASMobiC) as fine-grained mobile service providers. In RMCC, ASMobiCs host prefabricated Restful services to be asynchronously called by mobile service consumers at runtime. RMCC is a Restful cross-platform architecture functional on major mobile OSs (e.g., Android and iOS) and realizes utilization of the computing resources of off-the-shelve outdated or damaged-yet-functioning mobile devices towards green MCC. Results of benchmarking advocate significant mean time- and energy-saving of 87% and 71.45%, respectively when intensive tasks are executed in ASMobiCs.
{"title":"RMCC: Restful Mobile Cloud Computing Framework for Exploiting Adjacent Service-Based Mobile Cloudlets","authors":"S. Abolfazli, Zohreh Sanaei, A. Gani, Feng Xia, Wei-Ming Lin","doi":"10.1109/CloudCom.2014.91","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.91","url":null,"abstract":"Mobile devices, especially smartphones are increasingly gaining ground in several domains, particularly healthcare, tele-monitoring, and education to perform Resource-intensive Mobile Applications (RiMA). However, constrained resources, especially CPU and battery hinder their successful adoption. Mobile Cloud Computing (MCC) aims to augment computational capabilities of resource-constraint mobile devices and conserve their native resources by remotely performing intensive tasks. In typical MCC solutions, intensive tasks are offloaded to distant VM-based cloud data centers or cloudlets whose exploitation originates long WAN latency and/or virtualization overhead degrading RiMA execution efficiency. In this paper, a lightweight Resource-oriented MCC (RMCC) architecture is proposed that exploits resources of plethora of Adjacent Service-based Mobile Cloudlets (ASMobiC) as fine-grained mobile service providers. In RMCC, ASMobiCs host prefabricated Restful services to be asynchronously called by mobile service consumers at runtime. RMCC is a Restful cross-platform architecture functional on major mobile OSs (e.g., Android and iOS) and realizes utilization of the computing resources of off-the-shelve outdated or damaged-yet-functioning mobile devices towards green MCC. Results of benchmarking advocate significant mean time- and energy-saving of 87% and 71.45%, respectively when intensive tasks are executed in ASMobiCs.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125636183","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.110
Sebastian Jeuk, G. Salgueiro, Shi Zhou
Classification ambiguity in Cloud Computing has a catastrophic impact on cloud providers, their services and tenants. It limits the application of various network services to traffic either inside or outside a cloud. This is because IP addresses, VLANs and other transport-level technologies lack the functionality to cope with the highly dynamic, scalable and virtualized environment of cloud-enabled data centers. In this paper, we present the prototype design and discuss its features. We also evaluate the UCC proposal in a cloud-enabled data center environment. Our examination of the compatibility, performance and usability of UCC shows that this scheme is not only feasible, easy to implement, but also has significant advantages over other classification techniques. For example, it enables highly desirable functionality such as traffic volume-based data center billing. Imminent follow-up efforts include scalability testing of UCC on the open Internet. We are confident the UCC scheme can provide a long-term, practical and flexible solution for cloud classification with significant benefits.
{"title":"Universal Cloud Classification (UCC) and its Evaluation in a Data Center Environment","authors":"Sebastian Jeuk, G. Salgueiro, Shi Zhou","doi":"10.1109/CloudCom.2014.110","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.110","url":null,"abstract":"Classification ambiguity in Cloud Computing has a catastrophic impact on cloud providers, their services and tenants. It limits the application of various network services to traffic either inside or outside a cloud. This is because IP addresses, VLANs and other transport-level technologies lack the functionality to cope with the highly dynamic, scalable and virtualized environment of cloud-enabled data centers. In this paper, we present the prototype design and discuss its features. We also evaluate the UCC proposal in a cloud-enabled data center environment. Our examination of the compatibility, performance and usability of UCC shows that this scheme is not only feasible, easy to implement, but also has significant advantages over other classification techniques. For example, it enables highly desirable functionality such as traffic volume-based data center billing. Imminent follow-up efforts include scalability testing of UCC on the open Internet. We are confident the UCC scheme can provide a long-term, practical and flexible solution for cloud classification with significant benefits.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125939198","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.84
B. Agrawal, Antorweep Chakravorty, Chunming Rong, T. Wlodarczyk
In recent years, the amount of time series data generated in different domains have grown consistently. Analyzing large time-series datasets coming from sensor networks, power grids, stock exchanges, social networks and cloud monitoring logs at a massive scale is one of the biggest challenges that data scientists are facing. Big data storage and processing frameworks provides an environment to handle the volume, velocity and frequency attributes associated with time-series data. We propose an efficient and distributed computing framework - R2Time for processing such data in the Hadoop environment. It integrates R with a distributed time-series database (Open TSDB) using a MapReduce programming framework (RHIPE). R2Time allows analysts to work on huge datasets from within a popular, well supported, and powerful analysis environment.
{"title":"R2Time: A Framework to Analyse Open TSDB Time-Series Data in HBase","authors":"B. Agrawal, Antorweep Chakravorty, Chunming Rong, T. Wlodarczyk","doi":"10.1109/CloudCom.2014.84","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.84","url":null,"abstract":"In recent years, the amount of time series data generated in different domains have grown consistently. Analyzing large time-series datasets coming from sensor networks, power grids, stock exchanges, social networks and cloud monitoring logs at a massive scale is one of the biggest challenges that data scientists are facing. Big data storage and processing frameworks provides an environment to handle the volume, velocity and frequency attributes associated with time-series data. We propose an efficient and distributed computing framework - R2Time for processing such data in the Hadoop environment. It integrates R with a distributed time-series database (Open TSDB) using a MapReduce programming framework (RHIPE). R2Time allows analysts to work on huge datasets from within a popular, well supported, and powerful analysis environment.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124220307","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.38
Yu-Jia Chen, Wan-Ling Ho, Li-Chun Wang, Kuo-Chen Wang
In this paper, we propose a cloud-assisted network coded packet retransmission approach to reduce the number of packet retransmission in wireless multicasting. It is shown that the efficiency of packet retransmission can be significantly improved if we include network topology information (e.g., Network connectivity) during the encoding process of network coding. We leverages the capability of software-defined networking (SDN) to dynamically monitor and control the entire network and design a network topology based network coded packet retransmission (NTNCPR) mechanism. The proposed NTNCPR mechanism can easily calculate the good packet combination based on network topology information.
{"title":"A Cloud-Assisted Network Coded Packet Retransmission Approach for Wireless Multicasting","authors":"Yu-Jia Chen, Wan-Ling Ho, Li-Chun Wang, Kuo-Chen Wang","doi":"10.1109/CloudCom.2014.38","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.38","url":null,"abstract":"In this paper, we propose a cloud-assisted network coded packet retransmission approach to reduce the number of packet retransmission in wireless multicasting. It is shown that the efficiency of packet retransmission can be significantly improved if we include network topology information (e.g., Network connectivity) during the encoding process of network coding. We leverages the capability of software-defined networking (SDN) to dynamically monitor and control the entire network and design a network topology based network coded packet retransmission (NTNCPR) mechanism. The proposed NTNCPR mechanism can easily calculate the good packet combination based on network topology information.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129670778","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.145
Ryan Emerson, P. Ezhilchelvan
Atomic Multicasts are central to the management of replicated data in distributed systems. Previous work has proven the effectiveness of utilising atomic multicasts, opposed to the classic two-phase commit, to coordinate transactions in in-memory databases. However, the current family of protocols utilised by such systems do not scale as the number of destinations increases. We propose that atomic multicasts should not occur between database nodes, instead transaction ordering should be exposed as a service that is provided by a dedicated set of nodes. Our performance study shows a clear improvement in transaction throughput and latency as the number of participants in a transaction increases.
{"title":"An Atomic-Multicast Service for Scalable In-Memory Transaction Systems","authors":"Ryan Emerson, P. Ezhilchelvan","doi":"10.1109/CloudCom.2014.145","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.145","url":null,"abstract":"Atomic Multicasts are central to the management of replicated data in distributed systems. Previous work has proven the effectiveness of utilising atomic multicasts, opposed to the classic two-phase commit, to coordinate transactions in in-memory databases. However, the current family of protocols utilised by such systems do not scale as the number of destinations increases. We propose that atomic multicasts should not occur between database nodes, instead transaction ordering should be exposed as a service that is provided by a dedicated set of nodes. Our performance study shows a clear improvement in transaction throughput and latency as the number of participants in a transaction increases.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122855893","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 : 2014-12-15DOI: 10.1109/CloudCom.2014.82
Yasuhiro Watashiba, S. Date, H. Abe, Y. Kido, Koheix Ichikawa, Hiroaki Yamanaka, Eiji Kawai, S. Shimojo, H. Takemura
In the era of cloud computing, data centers that accommodate a series of user-requested jobs with a diversity of resource usage pattern need to have the capability of efficiently distributing resources to each user job, based on individual resource usage patterns. In particular, for high-performance computing as a cloud service which allows many users to benefit from a large-scale computing system, a new framework for resource management that treats not only the CPU resources, but also the network resources in the data center is essential. In this paper, an SDN-enhanced JMS that efficiently handles both network and CPU resources and as a result accelerates the execution time of user jobs is introduced as a building block technology for such a HPC cloud. Our evaluation shows that the SDN-enhanced JMS efficiently leverages the fat-tree interconnect of cluster systems running behind the cloud to suppress the collision of communications generated by different jobs.
{"title":"Performance Characteristics of an SDN-Enhanced Job Management System for Cluster Systems with Fat-Tree Interconnect","authors":"Yasuhiro Watashiba, S. Date, H. Abe, Y. Kido, Koheix Ichikawa, Hiroaki Yamanaka, Eiji Kawai, S. Shimojo, H. Takemura","doi":"10.1109/CloudCom.2014.82","DOIUrl":"https://doi.org/10.1109/CloudCom.2014.82","url":null,"abstract":"In the era of cloud computing, data centers that accommodate a series of user-requested jobs with a diversity of resource usage pattern need to have the capability of efficiently distributing resources to each user job, based on individual resource usage patterns. In particular, for high-performance computing as a cloud service which allows many users to benefit from a large-scale computing system, a new framework for resource management that treats not only the CPU resources, but also the network resources in the data center is essential. In this paper, an SDN-enhanced JMS that efficiently handles both network and CPU resources and as a result accelerates the execution time of user jobs is introduced as a building block technology for such a HPC cloud. Our evaluation shows that the SDN-enhanced JMS efficiently leverages the fat-tree interconnect of cluster systems running behind the cloud to suppress the collision of communications generated by different jobs.","PeriodicalId":249306,"journal":{"name":"2014 IEEE 6th International Conference on Cloud Computing Technology and Science","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128110605","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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