Dynamic voltage and frequency scaling (DVFS) has been a useful power management strategy in embedded systems, mobile devices, and wireless sensor networks. Recently, it has also been proposed for servers and data centers in conjunction with service consolidation and optimal resource-pool sizing. In this paper, we experimentally investigate the scope and usefulness of DVFS in a server environment. We set up a multimedia server which will be used in two different scenarios. In the first scenario, the server will host requests to download video files of known and available formats. In the second scenario, videos of unavailable formats can be accepted; in which case the server employs a trans coder to convert between AVI, MPEG and SLV formats before the videos are downloaded. The workload we generate has a uniform arrival rate and an exponentially distributed video size. We use four dynamic scaling policies which are widely used with existing mainstream Linux operating systems. Our observation is that while the gain of DVFS is clear in the first scenario (in which a predominantly IO-bound application is used), its use in the second scenario is rather counterproductive.
{"title":"Analysis of the Power Consumption of a Multimedia Server under Different DVFS Policies","authors":"W. Dargie","doi":"10.1109/CLOUD.2012.31","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.31","url":null,"abstract":"Dynamic voltage and frequency scaling (DVFS) has been a useful power management strategy in embedded systems, mobile devices, and wireless sensor networks. Recently, it has also been proposed for servers and data centers in conjunction with service consolidation and optimal resource-pool sizing. In this paper, we experimentally investigate the scope and usefulness of DVFS in a server environment. We set up a multimedia server which will be used in two different scenarios. In the first scenario, the server will host requests to download video files of known and available formats. In the second scenario, videos of unavailable formats can be accepted; in which case the server employs a trans coder to convert between AVI, MPEG and SLV formats before the videos are downloaded. The workload we generate has a uniform arrival rate and an exponentially distributed video size. We use four dynamic scaling policies which are widely used with existing mainstream Linux operating systems. Our observation is that while the gain of DVFS is clear in the first scenario (in which a predominantly IO-bound application is used), its use in the second scenario is rather counterproductive.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"15 12 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":"127641526","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}
Most Internet applications employ some kind of load balancing policies in a cluster setting to achieve reliable service provision as well as to deal with a resource bottleneck. However, these policies may not ensure the utilization of textit{all} of the hardware resources in a server equally efficiently. This paper experimentally investigates the relationship between the power consumption and resource utilization of a multimedia server cluster when different load balancing policies are used to distribute a workload. Our observations are the following: (1) A bottleneck on a single hardware resource can lead to a significant amount of underutilization of the entire system. (2) A ten times increment in the network bandwidth of the entire cluster can double the throughput of individual servers. The associated increment in power consumption of the individual servers is 1.2% only. (3) For TCP-based applications, session information is more useful than other types of status information to utilize power more efficiently. (4) The use of dynamic frequency scaling does not affect the overall throughput of IO-bound applications but reduces the power consumption of the servers; but this reduction is only 12% of the overall power consumption. More power can be saved by avoiding a resource bottleneck or through service consolidation.
大多数Internet应用程序在集群设置中采用某种负载平衡策略，以实现可靠的服务提供以及处理资源瓶颈。但是，这些策略可能无法确保服务器中textit{所有}硬件资源的使用效率相同。本文通过实验研究了采用不同负载均衡策略分配工作负载时多媒体服务器集群的功耗与资源利用率之间的关系。我们的观察结果如下:(1)单个硬件资源的瓶颈可能导致整个系统的大量未充分利用。(2)将整个集群的网络带宽增加十倍，可以使单个服务器的吞吐量增加一倍。单个服务器的相关功耗增量为1.2% only. (3) For TCP-based applications, session information is more useful than other types of status information to utilize power more efficiently. (4) The use of dynamic frequency scaling does not affect the overall throughput of IO-bound applications but reduces the power consumption of the servers; but this reduction is only 12% of the overall power consumption. More power can be saved by avoiding a resource bottleneck or through service consolidation.
{"title":"Analysis of the Power and Hardware Resource Consumption of Servers under Different Load Balancing Policies","authors":"W. Dargie, A. Schill","doi":"10.1109/CLOUD.2012.30","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.30","url":null,"abstract":"Most Internet applications employ some kind of load balancing policies in a cluster setting to achieve reliable service provision as well as to deal with a resource bottleneck. However, these policies may not ensure the utilization of textit{all} of the hardware resources in a server equally efficiently. This paper experimentally investigates the relationship between the power consumption and resource utilization of a multimedia server cluster when different load balancing policies are used to distribute a workload. Our observations are the following: (1) A bottleneck on a single hardware resource can lead to a significant amount of underutilization of the entire system. (2) A ten times increment in the network bandwidth of the entire cluster can double the throughput of individual servers. The associated increment in power consumption of the individual servers is 1.2% only. (3) For TCP-based applications, session information is more useful than other types of status information to utilize power more efficiently. (4) The use of dynamic frequency scaling does not affect the overall throughput of IO-bound applications but reduces the power consumption of the servers; but this reduction is only 12% of the overall power consumption. More power can be saved by avoiding a resource bottleneck or through service consolidation.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"481 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":"131918284","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}
A minimum cost maximum flow algorithm is proposed for resources(e.g. virtual machines) placement in clouds confronted to dynamic workloads and flows variations. The algorithm is compared to an exact method generalizing the classical Bin-Packing formulation using a linear integer program. A directed graph is used to model the allocation problem for cloud resources organized in a finite number of resource types; a common practice in cloud services. Providers can use the minimum cost maximum flow algorithm to opportunistically select the most appropriate physical resources to serve applications or to ensure elastic platform provisioning. The modified Bin-Packing algorithm is used to benchmark the minimum cost maximum flow solution. The latter combined with a prediction mechanism to handle dynamic variations achieves near optimal performance.
{"title":"Minimum Cost Maximum Flow Algorithm for Dynamic Resource Allocation in Clouds","authors":"Makhlouf Hadji, D. Zeghlache","doi":"10.1109/CLOUD.2012.36","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.36","url":null,"abstract":"A minimum cost maximum flow algorithm is proposed for resources(e.g. virtual machines) placement in clouds confronted to dynamic workloads and flows variations. The algorithm is compared to an exact method generalizing the classical Bin-Packing formulation using a linear integer program. A directed graph is used to model the allocation problem for cloud resources organized in a finite number of resource types; a common practice in cloud services. Providers can use the minimum cost maximum flow algorithm to opportunistically select the most appropriate physical resources to serve applications or to ensure elastic platform provisioning. The modified Bin-Packing algorithm is used to benchmark the minimum cost maximum flow solution. The latter combined with a prediction mechanism to handle dynamic variations achieves near optimal performance.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"41 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":"134052328","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 computing has become an important driver for delivering infrastructure as a service (IaaS) to users with on-demand requests for customized environments and sophisticated software stacks. Within the FutureGrid (FG) project, we offer different IaaS frameworks as well as high performance computing infrastructures by allowing users to explore them as part of the FG testbed. To ease the use of these infrastructures, as part of performance experiments, we have designed an image management framework, which allows us to create user defined software stacks based on abstract image management and uniform image registration. Consequently, users can create their own customized environments very easily. The complex processes of the underlying infrastructures are managed by our sophisticated software tools and services. Besides being able to manage images for IaaS frameworks, we also allow the registration and deployment of images onto bare-metal by the user. This level of functionality is typically not offered in a HPC (high performance computing) infrastructure. However, our approach provides users with the ability to create their own environments changing the paradigm of administrator-controlled dynamic provisioning to user-controlled dynamic provisioning, which we also call raining. Thus, users obtain access to a testbed with the ability to manage state-of-the-art software stacks that would otherwise not be supported in typical compute centers. Security is also considered by vetting images before they are registered in a infrastructure. In this paper, we present the design of our image management framework and evaluate two of its major components. This includes the image creation and image registration. Our design and implementation can support the current FG user community interested in such capabilities.
{"title":"Abstract Image Management and Universal Image Registration for Cloud and HPC Infrastructures","authors":"Javier Diaz, G. Laszewski, Fugang Wang, G. Fox","doi":"10.1109/CLOUD.2012.94","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.94","url":null,"abstract":"Cloud computing has become an important driver for delivering infrastructure as a service (IaaS) to users with on-demand requests for customized environments and sophisticated software stacks. Within the FutureGrid (FG) project, we offer different IaaS frameworks as well as high performance computing infrastructures by allowing users to explore them as part of the FG testbed. To ease the use of these infrastructures, as part of performance experiments, we have designed an image management framework, which allows us to create user defined software stacks based on abstract image management and uniform image registration. Consequently, users can create their own customized environments very easily. The complex processes of the underlying infrastructures are managed by our sophisticated software tools and services. Besides being able to manage images for IaaS frameworks, we also allow the registration and deployment of images onto bare-metal by the user. This level of functionality is typically not offered in a HPC (high performance computing) infrastructure. However, our approach provides users with the ability to create their own environments changing the paradigm of administrator-controlled dynamic provisioning to user-controlled dynamic provisioning, which we also call raining. Thus, users obtain access to a testbed with the ability to manage state-of-the-art software stacks that would otherwise not be supported in typical compute centers. Security is also considered by vetting images before they are registered in a infrastructure. In this paper, we present the design of our image management framework and evaluate two of its major components. This includes the image creation and image registration. Our design and implementation can support the current FG user community interested in such capabilities.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"587 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":"134337850","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}
Fábio Oliveira, T. Eilam, M. Kalantar, Florian Rosenberg
Virtualization promises to reduce data centers' total cost of ownership by enabling the creation of a small set of standardized building blocks to be shared and used many times indifferent software stacks. However, without proper methodology and tools, an organization can easily end up with a large number of one-off virtual images, adversely affecting the cost. We propose an approach, tool, and algorithms for constructing high-quality, semantically-rich image building blocks that are easy to share, compose, and reuse. In our approach, domain experts codify knowledge of a particular software product (or a combination thereof) in a platform- and cloud-agnostic software bundle. Image builders easily construct virtual images by composing a set of standardized bundles. Semantic-based validation guarantees a valid and complete image design. Moreover, we propose algorithms to automate image design by searching for an optimal set of building blocks taking into account multiple metrics such as cost, size, and expected build duration.
{"title":"Semantically-Rich Composition of Virtual Images","authors":"Fábio Oliveira, T. Eilam, M. Kalantar, Florian Rosenberg","doi":"10.1109/CLOUD.2012.40","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.40","url":null,"abstract":"Virtualization promises to reduce data centers' total cost of ownership by enabling the creation of a small set of standardized building blocks to be shared and used many times indifferent software stacks. However, without proper methodology and tools, an organization can easily end up with a large number of one-off virtual images, adversely affecting the cost. We propose an approach, tool, and algorithms for constructing high-quality, semantically-rich image building blocks that are easy to share, compose, and reuse. In our approach, domain experts codify knowledge of a particular software product (or a combination thereof) in a platform- and cloud-agnostic software bundle. Image builders easily construct virtual images by composing a set of standardized bundles. Semantic-based validation guarantees a valid and complete image design. Moreover, we propose algorithms to automate image design by searching for an optimal set of building blocks taking into account multiple metrics such as cost, size, and expected build duration.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"10 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131614870","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 expanding Cloud computing services offer great opportunities for consumers to find the best service and best pricing, which however raises new challenges on how to select the best service out of the huge pool. It is time-consuming for consumers to collect the necessary information and analyze all service providers to make the decision. This is also a highly demanding task from a computational perspective, because the same computations may be conducted repeatedly by multiple consumers who have similar requirements. Therefore, in this paper, we propose a novel brokerage-based architecture in the Cloud, where the Cloud brokers is responsible for the service selection. In particular, we design a unique indexing technique for managing the information of a large number of Cloud service providers. We then develop efficient service selection algorithms that rank potential service providers and aggregate them if necessary. We prove the efficiency and effectiveness of our approach through an experimental study with the real and synthetic Cloud data.
{"title":"A Brokerage-Based Approach for Cloud Service Selection","authors":"Smitha Sundareswaran, A. Squicciarini, D. Lin","doi":"10.1109/CLOUD.2012.119","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.119","url":null,"abstract":"The expanding Cloud computing services offer great opportunities for consumers to find the best service and best pricing, which however raises new challenges on how to select the best service out of the huge pool. It is time-consuming for consumers to collect the necessary information and analyze all service providers to make the decision. This is also a highly demanding task from a computational perspective, because the same computations may be conducted repeatedly by multiple consumers who have similar requirements. Therefore, in this paper, we propose a novel brokerage-based architecture in the Cloud, where the Cloud brokers is responsible for the service selection. In particular, we design a unique indexing technique for managing the information of a large number of Cloud service providers. We then develop efficient service selection algorithms that rank potential service providers and aggregate them if necessary. We prove the efficiency and effectiveness of our approach through an experimental study with the real and synthetic Cloud data.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"4 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":"130733928","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}
As a rising application paradigm, cloud computing enables the resources to be virtualized and shared among applications. In a typical cloud computing scenario, customers, Service Providers (SP), and Platform Providers (PP) are independent participants, and they have their own objectives with different revenues and costs. From PPs' viewpoints, much research work reduced the costs by optimizing VM placement and deciding when and how to perform the VM migrations. However, some work ignored the fact that the balanced use of the multi-dimensional resources can affect overall resource utilization significantly. Furthermore, some work focuses on the selection of the VMs and the target servers without considering how to perform the reconfigurations. In this paper, with a comprehensive consideration of PPs' interests, we propose a framework to improve their profits by maximizing the resource utilization and reducing the reconfiguration costs. Firstly, we use the vector arithmetic to model the objective of balancing the multi-dimensional resources use and propose a VM deployment optimization method to maximize the resource utilization. Then a two-level runtime reconfiguration strategy, including local adjustment and VM parallel migration, is presented to reduce the VM migration and shorten the total migration time. Finally, we conduct some preliminary experiments, and the results show that our framework is effective in maximizing the resource utilization and reducing the costs of the runtime reconfiguration.
{"title":"A Profit-Aware Virtual Machine Deployment Optimization Framework for Cloud Platform Providers","authors":"Wei Chen, Xiaoqiang Qiao, Jun Wei, Tao Huang","doi":"10.1109/CLOUD.2012.60","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.60","url":null,"abstract":"As a rising application paradigm, cloud computing enables the resources to be virtualized and shared among applications. In a typical cloud computing scenario, customers, Service Providers (SP), and Platform Providers (PP) are independent participants, and they have their own objectives with different revenues and costs. From PPs' viewpoints, much research work reduced the costs by optimizing VM placement and deciding when and how to perform the VM migrations. However, some work ignored the fact that the balanced use of the multi-dimensional resources can affect overall resource utilization significantly. Furthermore, some work focuses on the selection of the VMs and the target servers without considering how to perform the reconfigurations. In this paper, with a comprehensive consideration of PPs' interests, we propose a framework to improve their profits by maximizing the resource utilization and reducing the reconfiguration costs. Firstly, we use the vector arithmetic to model the objective of balancing the multi-dimensional resources use and propose a VM deployment optimization method to maximize the resource utilization. Then a two-level runtime reconfiguration strategy, including local adjustment and VM parallel migration, is presented to reduce the VM migration and shorten the total migration time. Finally, we conduct some preliminary experiments, and the results show that our framework is effective in maximizing the resource utilization and reducing the costs of the runtime reconfiguration.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"1 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":"132432567","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 this paper we describe an approach to graphically externalize the cloud potential of a company, considering its architectural description. For this purpose it is shown how current architectural description can be extended, in terms of knowledge and graphical representation. The goal is to focus on the most important features and aspects to consider during the evaluation of shifting into a cloud environment. Even if each company has different strategies and approaches to its business activities, there are some domains related to the shift in a cloud environment that should be considered in any case. This paper shows how these main areas can be taken into account in order to extend the architectural representation of a company and express its cloud readiness.
{"title":"Enterprise Architectures for Cloud Computing","authors":"L. Aureli, Arianna Pierfranceschi, H. Wache","doi":"10.1109/CLOUD.2012.149","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.149","url":null,"abstract":"In this paper we describe an approach to graphically externalize the cloud potential of a company, considering its architectural description. For this purpose it is shown how current architectural description can be extended, in terms of knowledge and graphical representation. The goal is to focus on the most important features and aspects to consider during the evaluation of shifting into a cloud environment. Even if each company has different strategies and approaches to its business activities, there are some domains related to the shift in a cloud environment that should be considered in any case. This paper shows how these main areas can be taken into account in order to extend the architectural representation of a company and express its cloud readiness.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"1 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":"128819704","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}
DBSCAN is a well-known algorithm for density-based clustering because it can identify the groups of arbitrary shapes and deal with noisy datasets. However, with the increasing amount of data, DBSCAN algorithm running on a single machine has to face the scalability problem. In this paper, we propose a Map/Reduce-based DBSCAN algorithm called DBSCAN-MR to solve the scalability problem. In DBSCAN-MR, the input dataset is partitioned into smaller parts and then parallel processed on the Hadoop platform. However, choosing different partition mechanisms will affect the execution efficiency and load balance of each node. Therefore, we propose a method, partition with reduce boundary points (PRBP), to select partition boundaries based on the distribution of data points. Our experimental results show that DBSCAN-MR with the design of PRBP has higher efficiency and scalability than competitors.
DBSCAN是一种著名的基于密度的聚类算法，因为它可以识别任意形状的组并处理有噪声的数据集。然而，随着数据量的不断增加，在单机上运行的DBSCAN算法不得不面临可伸缩性问题。在本文中，我们提出了一种基于Map/ reduce的DBSCAN算法，称为DBSCAN- mr来解决可扩展性问题。在DBSCAN-MR中，输入数据集被分割成更小的部分，然后在Hadoop平台上并行处理。但是，选择不同的分区机制会影响每个节点的执行效率和负载均衡。因此，我们提出了一种基于数据点分布选择分区边界的方法，即PRBP (partition with reduce boundary points)。实验结果表明，采用PRBP设计的DBSCAN-MR具有更高的效率和可扩展性。
{"title":"Efficient Map/Reduce-Based DBSCAN Algorithm with Optimized Data Partition","authors":"Bi-Ru Dai, I-Chang Lin","doi":"10.1109/CLOUD.2012.42","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.42","url":null,"abstract":"DBSCAN is a well-known algorithm for density-based clustering because it can identify the groups of arbitrary shapes and deal with noisy datasets. However, with the increasing amount of data, DBSCAN algorithm running on a single machine has to face the scalability problem. In this paper, we propose a Map/Reduce-based DBSCAN algorithm called DBSCAN-MR to solve the scalability problem. In DBSCAN-MR, the input dataset is partitioned into smaller parts and then parallel processed on the Hadoop platform. However, choosing different partition mechanisms will affect the execution efficiency and load balance of each node. Therefore, we propose a method, partition with reduce boundary points (PRBP), to select partition boundaries based on the distribution of data points. Our experimental results show that DBSCAN-MR with the design of PRBP has higher efficiency and scalability than competitors.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"26 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":"134609184","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}
D. Rodríguez-Silva, Lilian Adkinson-Orellana, F. González-Castaño, I. Armiño-Franco, David González-Martínez
Traditional video surveillance systems require infrastructures including expensive servers with capabilities to process images and store video recordings. These surveillance systems produce and need to store a huge amount of data and to execute on them specific image analysis in real-time in order to detect safety events. We propose a video surveillance system based on Cloud Computing that collects multimedia streams generated by surveillance cameras, optimizes their transmissions according to network condition and stores them in a cloud storage system in an efficient and secure way.
{"title":"Video Surveillance Based on Cloud Storage","authors":"D. Rodríguez-Silva, Lilian Adkinson-Orellana, F. González-Castaño, I. Armiño-Franco, David González-Martínez","doi":"10.1109/CLOUD.2012.44","DOIUrl":"https://doi.org/10.1109/CLOUD.2012.44","url":null,"abstract":"Traditional video surveillance systems require infrastructures including expensive servers with capabilities to process images and store video recordings. These surveillance systems produce and need to store a huge amount of data and to execute on them specific image analysis in real-time in order to detect safety events. We propose a video surveillance system based on Cloud Computing that collects multimedia streams generated by surveillance cameras, optimizes their transmissions according to network condition and stores them in a cloud storage system in an efficient and secure way.","PeriodicalId":214084,"journal":{"name":"2012 IEEE Fifth International Conference on Cloud Computing","volume":"52 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":"133798847","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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