A proposal on Smart controller Architecture in Hierarchical and Flat Clustering.

1. Smart Controller
Over Resource Nodes
Ghazal Tashakor

2. Agenda
 Deployment Model
 Controller
1. Monitoring Nodes
2. Cluster Analysis
3. Switching in a multi-cluster graph
 References

3. Deployment Model
Scheduling Jobs
Controller
Resource Discovery
Aggregation nodes
Leaf nodes
Content-based
Clustering/
XML Clustering
Job1
Job2
Job3
Free Free Free Free Free Free
Free Free Free
Free
Busy
Busy
TransmittingTimetotheBrokers.
Transmittingphase
AggregationTime
CommunicationTime
Communication phase
Collaborative/Fuzzy
Clustering
Aggregation phase
1. Monitoring nodes (Predictors)
2. Cluster Analysis
3. Switching in multi-cluster graphs
Load Balancing
Resource Allocation
Event Processing
Job
Management
Brokers

4. Controller
Cloud and Edge Resource nodes in collaboration and
communication to work together needs a controller to
do the smart cluster analysis and take the final
decision for their workload elasticity.
Also it needs monitoring strategy before analysis and
Switching ability after that.

5. 1.Monitoring Nodes in:
Multi-cluster combination model
Multi-clustering techniques:
1.Hierarchical clustering for Cluster-head nodes.
(Vertically)
2.Flat Clustering for non-head cluster nodes
(Horizontally)

6. 2. Cluster Analysis phases
1. Prediction Phase: Cluster-head nodes
2.Communication phase: Receiving packets from non-
head cluster nodes (Connectivity and mobility issues)
3. Aggregation Phase : Capacity and dependencies
measurement for selected nodes/resources ( For load
balancing and event processing issues)
4.Transmitting Phase: Transmit the aggregated
information to the Brokers.

7. 3. Switching in a multi-cluster
graph
Identification clustering based on membership
profiles could be a good method in standard fuzzy
clustering algorithm to detect the user migration by
checking the node migration between clusters.
This is possible by studying the migratory behavior of
user/nodes over time and achieve the statistical
approaches for predicting the path.

8. References
i. Switching regression models and fuzzy clustering, RJ Hathaway, JC Bezdek - Fuzzy
Systems, IEEE Transactions …, 1993
ii. Relative entropy collaborative fuzzy clustering method, M Zarinbal, MHF Zarandi, IB
Turksen - Pattern Recognition, 2015 – Elsevier
iii. Collaborative clustering with the use of Fuzzy C-Means and its quantification, W Pedrycz,
P Rai - Fuzzy Sets and Systems, 2008 – Elsevier
iv. Detecting the migration of mobile service customers using fuzzy clustering I Bose, X Chen
- Information & Management, 2015 – Elsevier
v. Node Similarity-based Graph Clustering and Visualization M Erdélyi, J Abonyi - 7th
International Symposium of Hungarian …, 2006
vi. Node Similarity-based Graph Clustering and Visualization M Erdélyi, J Abonyi - 7th
International Symposium of Hungarian …, 2006 - researchgate.net
vii. XML clustering: a review of structural approaches M Piernik, D Brzezinski, T Morzy… - The
Knowledge …, 2015 - Cambridge Univ Press
viii. Combining multiple clustering systems C Boulis, M Ostendorf - Knowledge Discovery in
Databases: PKDD 2004, 2004 – Springer
ix. Toward Semantic XML Clustering. A Tagarelli, S Greco - Sdm, 2006
x. A cluster validity index for fuzzy clustering KL Wu, MS Yang - Pattern Recognition Letters,
2005 – Elsevier
xi. A Hierarchical Model for Minimum Entropy Data Partitioning W Wu, AB Lee, D Mumford
– 2003
xii. https://en.wikipedia.org/wiki/Hamming_distance