Virtual Organization Clusters are systems comprised of virtual machines that provide dedicated computing clusters for each individual Virtual Organization. The design of these clusters allows individual virtual machines to be independent of the underlying physical hardware, potentially allowing virtual clusters to span multiple grid sites. A major challenge in using Virtual Organization Clusters as a grid computing abstraction arises from the need to schedule and provision physical resources to run the virtual machines.This paper describes a virtual cluster scheduler implementation based on the Condor High Throughput Computing system. By means of real-time monitoring of the Condor job queue, virtual machines that belong to individual Virtual Organizations are provisioned and booted. Jobs belonging to each Virtual Organization are then run on the organization-specific virtual machines, which form a cluster dedicated to the specific organization. Once the queued jobs have executed, the virtual machines are terminated, thereby allowing the physical resources to be re-claimed. Tests of this system were conducted using synthetic workloads, demonstrating that dynamic provisioning of virtual machines preserves system throughput for all but the shortest-running of grid jobs, without undue increase in scheduling latency.
{"title":"Dynamic Provisioning of Virtual Organization Clusters","authors":"M. Murphy, Brandon Kagey, M. Fenn, S. Goasguen","doi":"10.1109/CCGRID.2009.37","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.37","url":null,"abstract":"Virtual Organization Clusters are systems comprised of virtual machines that provide dedicated computing clusters for each individual Virtual Organization. The design of these clusters allows individual virtual machines to be independent of the underlying physical hardware, potentially allowing virtual clusters to span multiple grid sites. A major challenge in using Virtual Organization Clusters as a grid computing abstraction arises from the need to schedule and provision physical resources to run the virtual machines.This paper describes a virtual cluster scheduler implementation based on the Condor High Throughput Computing system. By means of real-time monitoring of the Condor job queue, virtual machines that belong to individual Virtual Organizations are provisioned and booted. Jobs belonging to each Virtual Organization are then run on the organization-specific virtual machines, which form a cluster dedicated to the specific organization. Once the queued jobs have executed, the virtual machines are terminated, thereby allowing the physical resources to be re-claimed. Tests of this system were conducted using synthetic workloads, demonstrating that dynamic provisioning of virtual machines preserves system throughput for all but the shortest-running of grid jobs, without undue increase in scheduling latency.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116433315","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}
Rui Ding, Jinzhu Gao, Bin Chen, J. Siepmann, Yi Liu
With the development of science, research on data analysis is becoming increasingly important. However, sometimes it is difficult to draw a conclusion from a complex data set. Data Visualization has been widely used for people to understand more about their datasets by representing the data in a way that visually highlights the relationships. As the size of data grows exponentially, keeping multiple local copies of the data becomes unrealistic for a collaborative research project. In this paper, we design and develop a cybertool, CT-IANP, which supports collaborative research in the area of atmospheric nucleation. The paper shows how Java 3D, web-based tools, and other techniques are used to achieve the goal.
{"title":"Web-Based Visualization of Atmospheric Nucleation Processes Using Java3D","authors":"Rui Ding, Jinzhu Gao, Bin Chen, J. Siepmann, Yi Liu","doi":"10.1109/CCGRID.2009.56","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.56","url":null,"abstract":"With the development of science, research on data analysis is becoming increasingly important. However, sometimes it is difficult to draw a conclusion from a complex data set. Data Visualization has been widely used for people to understand more about their datasets by representing the data in a way that visually highlights the relationships. As the size of data grows exponentially, keeping multiple local copies of the data becomes unrealistic for a collaborative research project. In this paper, we design and develop a cybertool, CT-IANP, which supports collaborative research in the area of atmospheric nucleation. The paper shows how Java 3D, web-based tools, and other techniques are used to achieve the goal.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131661957","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}
There exists numerous Grid middleware to develop and execute programs on the computational Grid, but they still require intensive work from their users. BitDew is made to facilitate the usage of large scale Grid with dynamic, heterogeneous, volatile and highly distributed computing resources for applications that require a huge amount of data processing. Data-intensive applications form an important class of applications for the e-Science community which require secure and coordinated access to large datasets, wide-area transfers and broad distribution ofTeraBytes of data while keeping track of multiple data replicas. In genetic biology, gene sequences comparison and analysis are the most basic routines. With the considerable increase of sequences to analyze, we need more and more computing power as well as efficient solution to manage data. In this work, we investigate the advantages of using a new Desktop Grid middleware BitDew, designed for large scale data management.Our contribution is two-fold: firstly, we introduce a data-driven Master/Slave programming model and we present an implementation of BLAST over BitDew following this model, secondly, we present extensive experimental and simulation results which demonstrate the effectiveness and scalability of our approach. We evaluate the benefit of multi-protocol data distribution to achieve remarkable speedups, we report on the ability to cope with highly volatile environment with relative performance degradation, we show the benefit of data replication in Grid with heterogeneous resource performance and we evaluate the combination of data fault tolerance and data replication when computing on volatileresources.
{"title":"BLAST Application with Data-Aware Desktop Grid Middleware","authors":"Haiwu He, G. Fedak, B. Tang, F. Cappello","doi":"10.1109/CCGRID.2009.91","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.91","url":null,"abstract":"There exists numerous Grid middleware to develop and execute programs on the computational Grid, but they still require intensive work from their users. BitDew is made to facilitate the usage of large scale Grid with dynamic, heterogeneous, volatile and highly distributed computing resources for applications that require a huge amount of data processing. Data-intensive applications form an important class of applications for the e-Science community which require secure and coordinated access to large datasets, wide-area transfers and broad distribution ofTeraBytes of data while keeping track of multiple data replicas. In genetic biology, gene sequences comparison and analysis are the most basic routines. With the considerable increase of sequences to analyze, we need more and more computing power as well as efficient solution to manage data. In this work, we investigate the advantages of using a new Desktop Grid middleware BitDew, designed for large scale data management.Our contribution is two-fold: firstly, we introduce a data-driven Master/Slave programming model and we present an implementation of BLAST over BitDew following this model, secondly, we present extensive experimental and simulation results which demonstrate the effectiveness and scalability of our approach. We evaluate the benefit of multi-protocol data distribution to achieve remarkable speedups, we report on the ability to cope with highly volatile environment with relative performance degradation, we show the benefit of data replication in Grid with heterogeneous resource performance and we evaluate the combination of data fault tolerance and data replication when computing on volatileresources.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"17 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661717","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}
Grid computing is now a viable computational paradigm for executing large scale workflow applications. However, many aspects of performance optimization remain challenging. In this paper, we focus on the workflow scheduling mechanism. While there is much work on static scheduling approaches for workflow applications in parallel environments, little work has been done on a real-world multi-cluster Grid environment. Since a typical Grid environment is dynamic, we propose a new cluster-based scheduling mechanism that dynamically executes a top-down static scheduling algorithm using the real-time feedback from the execution monitor. We also propose a novel two phase migration mechanism that mitigates the effect of a possible bad reschedule decision. Our experimental results show that this approach achieves the best performance among all the scheduling approaches we implemented on both reserved resources and those with external loads.
{"title":"Hybrid Re-scheduling Mechanisms for Workflow Applications on Multi-cluster Grid","authors":"Yang Zhang, C. Koelbel, K. Cooper","doi":"10.1109/CCGRID.2009.60","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.60","url":null,"abstract":"Grid computing is now a viable computational paradigm for executing large scale workflow applications. However, many aspects of performance optimization remain challenging. In this paper, we focus on the workflow scheduling mechanism. While there is much work on static scheduling approaches for workflow applications in parallel environments, little work has been done on a real-world multi-cluster Grid environment. Since a typical Grid environment is dynamic, we propose a new cluster-based scheduling mechanism that dynamically executes a top-down static scheduling algorithm using the real-time feedback from the execution monitor. We also propose a novel two phase migration mechanism that mitigates the effect of a possible bad reschedule decision. Our experimental results show that this approach achieves the best performance among all the scheduling approaches we implemented on both reserved resources and those with external loads.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132733186","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}
Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has responsibility for establishing how best to map tasks within a workflow to the available resources. As workflows are typically run over shared resources, and thus face unpredictable and changing resource capabilties, there may be benefit to be derived from adapting the task-to-resource mapping while a workflow is executing. This paper describes the use of utility functions to express the relative merits of alternative mappings; in essence, a utility function can be used to give a score to a candidate mapping, and the exploration of alternative mappings can be cast as an optimization problem. In this approach, changing the utility function allows adaptations to be carried out with a view to meeting different objectives. The contributions of this paper include: (i) a description of how adaptive workflow execution can be expressed as an optimization problem where the objective of the adaptation is to maximize some property expressed as a utility function; (ii) a description of how the approach has been applied to support adaptive workflow execution in grids; and (iii) an experimental evaluation of the resulting approach for alternative utility measures based on response time and profit.
{"title":"Utility Driven Adaptive Work?ow Execution","authors":"Kevin Lee, N. Paton, R. Sakellariou, A. Fernandes","doi":"10.1109/CCGRID.2009.15","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.15","url":null,"abstract":"Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has responsibility for establishing how best to map tasks within a workflow to the available resources. As workflows are typically run over shared resources, and thus face unpredictable and changing resource capabilties, there may be benefit to be derived from adapting the task-to-resource mapping while a workflow is executing. This paper describes the use of utility functions to express the relative merits of alternative mappings; in essence, a utility function can be used to give a score to a candidate mapping, and the exploration of alternative mappings can be cast as an optimization problem. In this approach, changing the utility function allows adaptations to be carried out with a view to meeting different objectives. The contributions of this paper include: (i) a description of how adaptive workflow execution can be expressed as an optimization problem where the objective of the adaptation is to maximize some property expressed as a utility function; (ii) a description of how the approach has been applied to support adaptive workflow execution in grids; and (iii) an experimental evaluation of the resulting approach for alternative utility measures based on response time and profit.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133582934","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}
Highly distributed systems such as Grids are used today to the execution of large-scale parallel applications. The behavior analysis of these applications is not trivial. The complexity appears because of the event correlation among processes, external influences like time-sharing mechanisms and saturation of network links, and also the amount of data that registers the application behavior. Almost all visualization tools to analysis of parallel applications offer a space-time representation of the application behavior. This paper presents a novel technique that combines traces from grid applications with a treemap visualization of the data. With this combination, we dynamically create an annotated hierarchical structure that represents the application behavior for the selected time interval. The experiments in the grid show that we can readily use our technique to the analysis of large-scale parallel applications with thousands of processes.
{"title":"Towards Visualization Scalability through Time Intervals and Hierarchical Organization of Monitoring Data","authors":"L. Schnorr, Guillaume Huard, P. Navaux","doi":"10.1109/CCGRID.2009.19","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.19","url":null,"abstract":"Highly distributed systems such as Grids are used today to the execution of large-scale parallel applications. The behavior analysis of these applications is not trivial. The complexity appears because of the event correlation among processes, external influences like time-sharing mechanisms and saturation of network links, and also the amount of data that registers the application behavior. Almost all visualization tools to analysis of parallel applications offer a space-time representation of the application behavior. This paper presents a novel technique that combines traces from grid applications with a treemap visualization of the data. With this combination, we dynamically create an annotated hierarchical structure that represents the application behavior for the selected time interval. The experiments in the grid show that we can readily use our technique to the analysis of large-scale parallel applications with thousands of processes.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114653717","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}
Searching for particular resources in a large-scale decentralized unstructured network can be very difficult since there is no centralized management to provide the specific location of resources. Moreover, the dynamic behavior of networks and the diversity of user behavior cause the search more complex and may not guarantee success. To address the problems, we propose a new adaptive resource indexing technique that aims to increase both efficiency and quality of the search by reducing both messages and time required for each query. Our approach consists of two complementary techniques. One is an index selection technique that selectively keeps the indices at each peer to increase the chance of successful queries with minimum space requirement. Another is an index distribution technique that automatically adjusts index distribution rate based on the search performance to optimize both the search performance and overhead. We simulate the technique in various network conditions and the results show that our technique is effective in decreasing hop counts and messages needed for resolving queries with only small overhead. It decreases the average hop count by up to 44% with 75%-less messages when used with flooding based queries even facing high churn. Furthermore, the query success rate with a limited timeout condition also increases, approaching nearly to 100%.
{"title":"Adaptive Resource Indexing Technique for Unstructured Peer-to-Peer Networks","authors":"S. Lerthirunwong, N. Maruyama, S. Matsuoka","doi":"10.1109/CCGRID.2009.41","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.41","url":null,"abstract":"Searching for particular resources in a large-scale decentralized unstructured network can be very difficult since there is no centralized management to provide the specific location of resources. Moreover, the dynamic behavior of networks and the diversity of user behavior cause the search more complex and may not guarantee success. To address the problems, we propose a new adaptive resource indexing technique that aims to increase both efficiency and quality of the search by reducing both messages and time required for each query. Our approach consists of two complementary techniques. One is an index selection technique that selectively keeps the indices at each peer to increase the chance of successful queries with minimum space requirement. Another is an index distribution technique that automatically adjusts index distribution rate based on the search performance to optimize both the search performance and overhead. We simulate the technique in various network conditions and the results show that our technique is effective in decreasing hop counts and messages needed for resolving queries with only small overhead. It decreases the average hop count by up to 44% with 75%-less messages when used with flooding based queries even facing high churn. Furthermore, the query success rate with a limited timeout condition also increases, approaching nearly to 100%.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124058347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the design and implementation of the Adaptable Virtual Storage System (AVSS) and introduces the capacity virtualization and performance virtualization for storage systems. AVSS has the following characteristics: 1) adoption of extended YFQ algorithm to control the allocation of bandwidth resources, realization of the performance isolation and guarantees of virtual disks; 2) adoption of hierarchy structure and dynamic mapping mechanism to manage heterogeneous storage resources flexibly and effectively, which lays a foundation for allocating storage resources on demand; 3) application of data-access frequency statistics and dynamic behavior analysis to supervise storage layout reorganization. The experimental results proved the correctness of our design. AVSS can isolate different applications and avoid performance interference. It can adjust storage layout according to the behavior of applications and improve the utilization of storage resources while improving the performance of the storage system.
{"title":"AVSS: An Adaptable Virtual Storage System","authors":"Jian Ke, Xudong Zhu, Wenwu Na, Lu Xu","doi":"10.1109/CCGRID.2009.42","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.42","url":null,"abstract":"This paper presents the design and implementation of the Adaptable Virtual Storage System (AVSS) and introduces the capacity virtualization and performance virtualization for storage systems. AVSS has the following characteristics: 1) adoption of extended YFQ algorithm to control the allocation of bandwidth resources, realization of the performance isolation and guarantees of virtual disks; 2) adoption of hierarchy structure and dynamic mapping mechanism to manage heterogeneous storage resources flexibly and effectively, which lays a foundation for allocating storage resources on demand; 3) application of data-access frequency statistics and dynamic behavior analysis to supervise storage layout reorganization. The experimental results proved the correctness of our design. AVSS can isolate different applications and avoid performance interference. It can adjust storage layout according to the behavior of applications and improve the utilization of storage resources while improving the performance of the storage system.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121473838","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}
Volunteer Computing (VC) projects harness the power of computers owned by volunteers across the Internet to perform hundreds of thousands of independent jobs. In VC projects, the path leading from the generation of jobs to the validation of the job results is characterized by delays hidden in the job lifespan, i.e., distribution delay,in-progress delay, and validation delay. These delays are difficult to estimate because of the dynamic behavior and heterogeneity of VC resources. A wrong estimation of these delays can cause the loss of project throughput and job latency in VC projects. In this paper, we evaluate the accuracy of several probabilistic methods to model the upper time bounds of these delays. We show how our selected models predict up-and-down trends in traces from existing VC projects. The use of our models provides valuable insights on selecting project deadlines and taking scheduling decisions. By accurately predicting job lifespan delays, our models lead to more efficient resource use, higher project throughput, and lower job latency in VC projects.
{"title":"Modeling Job Lifespan Delays in Volunteer Computing Projects","authors":"Trilce Estrada, M. Taufer, Kevin Reed","doi":"10.1109/CCGRID.2009.69","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.69","url":null,"abstract":"Volunteer Computing (VC) projects harness the power of computers owned by volunteers across the Internet to perform hundreds of thousands of independent jobs. In VC projects, the path leading from the generation of jobs to the validation of the job results is characterized by delays hidden in the job lifespan, i.e., distribution delay,in-progress delay, and validation delay. These delays are difficult to estimate because of the dynamic behavior and heterogeneity of VC resources. A wrong estimation of these delays can cause the loss of project throughput and job latency in VC projects. In this paper, we evaluate the accuracy of several probabilistic methods to model the upper time bounds of these delays. We show how our selected models predict up-and-down trends in traces from existing VC projects. The use of our models provides valuable insights on selecting project deadlines and taking scheduling decisions. By accurately predicting job lifespan delays, our models lead to more efficient resource use, higher project throughput, and lower job latency in VC projects.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122117658","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}
Current implementations of real-time collaborative applications rely on a dedicated infrastructure to carry out all synchronizing and communication functions, and require all end nodes to communicate directly with and through the central server. In this paper, we investigate an architecture, in which the most resource intensive functionality of continuous communication among collaborators to disseminate changes is decentralized, utilizing the end users as relays. We observe that communication characteristics of real-time collaboration makes use of existing multicast mechanisms unsuitable. As collaborative editing sessions are typically long, we are able to gather and then use additional parameters ofnodes (their instabilities and frequency of sending updates) and communication links (latencies and average costs). We identify several criteria to determinethe quality of a multicast tree: cost, latency and instability. We analyze the complexity of these problems and propose algorithms to optimize the communicationtopology. We also consider the multiobjective problem in which we search for a tree that results in a good trade-off between these measures. Validation ofalgorithms on numerous graphs shows that it is important to consider the multiobjective problem, as optimal solutions for one performance measure can be far from optimal values of the others.
{"title":"Multicast Trees for Collaborative Applications","authors":"K. Rządca, Jackson Tan Teck Yong, Anwitaman Datta","doi":"10.1109/CCGRID.2009.38","DOIUrl":"https://doi.org/10.1109/CCGRID.2009.38","url":null,"abstract":"Current implementations of real-time collaborative applications rely on a dedicated infrastructure to carry out all synchronizing and communication functions, and require all end nodes to communicate directly with and through the central server. In this paper, we investigate an architecture, in which the most resource intensive functionality of continuous communication among collaborators to disseminate changes is decentralized, utilizing the end users as relays. We observe that communication characteristics of real-time collaboration makes use of existing multicast mechanisms unsuitable. As collaborative editing sessions are typically long, we are able to gather and then use additional parameters ofnodes (their instabilities and frequency of sending updates) and communication links (latencies and average costs). We identify several criteria to determinethe quality of a multicast tree: cost, latency and instability. We analyze the complexity of these problems and propose algorithms to optimize the communicationtopology. We also consider the multiobjective problem in which we search for a tree that results in a good trade-off between these measures. Validation ofalgorithms on numerous graphs shows that it is important to consider the multiobjective problem, as optimal solutions for one performance measure can be far from optimal values of the others.","PeriodicalId":118263,"journal":{"name":"2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124701283","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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