TY - GEN
T1 - Exploiting the advantages of object-based DSM in a heterogeneous cluster environment
AU - Liu, Xuli
AU - Jiang, Hong
AU - Soh, Leen Kiat
PY - 2005
Y1 - 2005
N2 - In recent years, increasing effort has been made by the cluster and grid computing community to build object-based Distributed Shared Memory systems (DSM) in a cluster environment. In most of these systems, a shared object is simply used as a data-exchanging unit so as to alleviate the false-sharing problem, and the advantages of sharing objects remain to be fully exploited. Thus, this paper is motivated to investigate the potential advantages of object-based DSM. For example, the performance of a distributed application may be significantly improved by adoptively and judiciously setting the size of the shared-objects, i.e., granularity. This paper, in addition to investigating the advantages of sharing objects, particularly focuses on observing how the performance of a distributed application changes with varied granularity, obtaining the optimal granularity through curve fitting, studying the factors that affect the optimal granularity, and predicting this optimal granularity in a changing runtime environment.
AB - In recent years, increasing effort has been made by the cluster and grid computing community to build object-based Distributed Shared Memory systems (DSM) in a cluster environment. In most of these systems, a shared object is simply used as a data-exchanging unit so as to alleviate the false-sharing problem, and the advantages of sharing objects remain to be fully exploited. Thus, this paper is motivated to investigate the potential advantages of object-based DSM. For example, the performance of a distributed application may be significantly improved by adoptively and judiciously setting the size of the shared-objects, i.e., granularity. This paper, in addition to investigating the advantages of sharing objects, particularly focuses on observing how the performance of a distributed application changes with varied granularity, obtaining the optimal granularity through curve fitting, studying the factors that affect the optimal granularity, and predicting this optimal granularity in a changing runtime environment.
UR - http://www.scopus.com/inward/record.url?scp=33845330593&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33845330593&partnerID=8YFLogxK
U2 - 10.1109/CCGRID.2005.1558644
DO - 10.1109/CCGRID.2005.1558644
M3 - Conference contribution
AN - SCOPUS:33845330593
SN - 0780390741
SN - 9780780390744
T3 - 2005 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2005
SP - 800
EP - 807
BT - 2005 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2005
T2 - 2005 IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2005
Y2 - 9 May 2005 through 12 May 2005
ER -