Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Next generation distributed object systems will be re-quired to support millions if not billions of objects and topersist for long periods of time, such as a simulation of theentire World b b or modeling of protein folding. Ifthese complex object systems are to be scalable and run ef-fectively throughout the life-cycle, support is required toshare new knowledge, to store existing knowledge aboutcurrent object behaviors, to modify their behavior, as llas the capacity to accept some degree of risk by exploit-ing opportunities for software level speculative execution,where objects synchronizeonly when it becomes absolutelynecessary.This approach, hover, is not without its challenges.In a distributed object system, classes may be used in waysnot anticipatedby the original developer. Thus, the commoncase or critical path of a distributed object system becomescontext sensitive, which precludes discovery and optimiza-tion by traditional compile-time and run-time techniques.The second challenge is to effectively exploit software levelspeculative execution. While the performance beneﬁts ofthis approach can be tremendous, it can be very difﬁcult todetermine in large complex systems when and where spec-ulation can be exploited.To address these challenges for next generation soft-ware systems  propose the developmentof the PerfMinerengine for the performance mining of large-scale data-intensive distributed object applications. PerfMiner, a sys-tem for continuous performance optimization via mining,will enable a distributed object system to:1. discover its own critical path,2. detect new opportunities for speculative processing,and3. facilitate modifying an object’s behaviors (i.e., meth-ods) at run-time in response to newly acquired knowl-edge.PerfMiner will detect opportunities for both the criticalpath optimization and the beneﬁcial application of specu-lative execution using parallel data mining on a persistent,run-time generated database. PerfMiner can aid the auto-matic discovery of the critical path using object executiontraces, and can also discover other interesting patterns likethemost frequent paths for use by speculative execution,as ll as (conditionally) dependent/independent objects,data access patterns, communication patterns, code bottle-necks, abnormal execution states such as uncharacteristicdelays, and resource contention. Parallelism is exploited toenable a faster, more thorough mining of the data in thisreal-time sensitive environment.To demonstrate the beneﬁts of PerfMiner,  plan toconduct a performance study of a number of importantand varied distributed applications including real-time vi-sion algorithms, bioinformatics, molecular modeling, net-work simulations, distributed caching, and data mining it-self. The ultimate goal of PerfMiner is the robust perfor-mance of complex distributed object systems which enablesthem to adapt to and optimize for a dynamic critical path asll a dynamic run-time environment when executed on to-day’s high-por “grid” computing platforms.Our initial investigation focuses on analyzing differentdata mining algorithms across different ﬁle systems. Theseinclude the following ﬁle systems: Ext2fs (non-journaled), Ext3fs (journaled),Reiserfs (journaled), XFS (journaled), and Tmpfs – RAM ﬁle system. will also vary data mining algorithms applied to thesame databases, including: Association Rules, both horizontal and vertical, Clustering, and
['Christopher D. Carothers', 'Boleslaw K. Szymanski', 'Mohammed J. Zaki']
I am writing a research paper on a idea Performance mining of large-scale data-intensive applications
[{"from": "human", "value": "I am writing a research paper on a idea Performance mining of large-scale data-intensive applications"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Next generation distributed object systems will be re-quired to support millions if not billions of objects and topersist for long periods of time, such as a simulation of theentire World b b or modeling of protein folding. Ifthese complex object systems are to be scalable and run ef-fectively throughout the life-cycle, support is required toshare new knowledge, to store existing knowledge aboutcurrent object behaviors, to modify their behavior, as llas the capacity to accept some degree of risk by exploit-ing opportunities for software level speculative execution,where objects synchronizeonly when it becomes absolutelynecessary.This approach, hover, is not without its challenges.In a distributed object system, classes may be used in waysnot anticipatedby the original developer. Thus, the commoncase or critical path of a distributed object system becomescontext sensitive, which precludes discovery and optimiza-tion by traditional compile-time and run-time techniques.The second challenge is to effectively exploit software levelspeculative execution. While the performance bene\ufb01ts ofthis approach can be tremendous, it can be very dif\ufb01cult todetermine in large complex systems when and where spec-ulation can be exploited.To address these challenges for next generation soft-ware systems  propose the developmentof the PerfMinerengine for the performance mining of large-scale data-intensive distributed object applications. PerfMiner, a sys-tem for continuous performance optimization via mining,will enable a distributed object system to:1. discover its own critical path,2. detect new opportunities for speculative processing,and3. facilitate modifying an object\u2019s behaviors (i.e., meth-ods) at run-time in response to newly acquired knowl-edge.PerfMiner will detect opportunities for both the criticalpath optimization and the bene\ufb01cial application of specu-lative execution using parallel data mining on a persistent,run-time generated database. PerfMiner can aid the auto-matic discovery of the critical path using object executiontraces, and can also discover other interesting patterns likethemost frequent paths for use by speculative execution,as ll as (conditionally) dependent/independent objects,data access patterns, communication patterns, code bottle-necks, abnormal execution states such as uncharacteristicdelays, and resource contention. Parallelism is exploited toenable a faster, more thorough mining of the data in thisreal-time sensitive environment.To demonstrate the bene\ufb01ts of PerfMiner,  plan toconduct a performance study of a number of importantand varied distributed applications including real-time vi-sion algorithms, bioinformatics, molecular modeling, net-work simulations, distributed caching, and data mining it-self. The ultimate goal of PerfMiner is the robust perfor-mance of complex distributed object systems which enablesthem to adapt to and optimize for a dynamic critical path asll a dynamic run-time environment when executed on to-day\u2019s high-por \u201cgrid\u201d computing platforms.Our initial investigation focuses on analyzing differentdata mining algorithms across different \ufb01le systems. Theseinclude the following \ufb01le systems: Ext2fs (non-journaled), Ext3fs (journaled),Reiserfs (journaled), XFS (journaled), and Tmpfs \u2013 RAM \ufb01le system. will also vary data mining algorithms applied to thesame databases, including: Association Rules, both horizontal and vertical, Clustering, and"}]