TY - GEN
T1 - Planning the transformation of overlays
AU - Yoon, Young
AU - Robinson, Nathan
AU - Muthusamy, Vinod
AU - McIlraith, Sheila
AU - Jacobsen, Hans Arno
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/4/4
Y1 - 2016/4/4
N2 - Reconfiguring a topology is an important technique to sustain high efficiency and robustness of an overlay. However, the problem of transforming the overlay from an old topology to a newly refined one, at runtime, has received relatively little attention. The key challenge is to minimize the disruption that can be caused by topology transformation operations. Excessive disruption can be costly thus hamper the decision to migrate to a better topology. To address this issue, we solve a problem of finding an appropriate sequence of steps to transform a topology that incurs the least service disruption. We call this the incremental topology transformation (ITT) problem. ITT can be formulated well as an automated planning problem and can be solved with numerous off-the-shelf planning algorithms. However, we found that state-of-the-art domainindependent planning techniques can not scale to solve large ITT problem instances. This shortcoming motivated us to develop a suite of planners that use novel domain-specific heuristics to guide the search for a solution. Our empirical evaluation shows that our planners offer a viable solution to a diversity of ITT problems.
AB - Reconfiguring a topology is an important technique to sustain high efficiency and robustness of an overlay. However, the problem of transforming the overlay from an old topology to a newly refined one, at runtime, has received relatively little attention. The key challenge is to minimize the disruption that can be caused by topology transformation operations. Excessive disruption can be costly thus hamper the decision to migrate to a better topology. To address this issue, we solve a problem of finding an appropriate sequence of steps to transform a topology that incurs the least service disruption. We call this the incremental topology transformation (ITT) problem. ITT can be formulated well as an automated planning problem and can be solved with numerous off-the-shelf planning algorithms. However, we found that state-of-the-art domainindependent planning techniques can not scale to solve large ITT problem instances. This shortcoming motivated us to develop a suite of planners that use novel domain-specific heuristics to guide the search for a solution. Our empirical evaluation shows that our planners offer a viable solution to a diversity of ITT problems.
KW - Automated planning
KW - Overlays
UR - http://www.scopus.com/inward/record.url?scp=84975847302&partnerID=8YFLogxK
U2 - 10.1145/2851613.2851639
DO - 10.1145/2851613.2851639
M3 - Conference contribution
AN - SCOPUS:84975847302
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 500
EP - 507
BT - 2016 Symposium on Applied Computing, SAC 2016
PB - Association for Computing Machinery
T2 - 31st Annual ACM Symposium on Applied Computing, SAC 2016
Y2 - 4 April 2016 through 8 April 2016
ER -