Paper Info
Title
Task-Allocation in a Large Scaled Hierarchical Many-Core Topology
Abstract
As more and more electronic devices get connected together, whole systems get more complicated in terms of development, maintenance and usage. New principles in design are needed to cope with this complexity. That is why we developed the Artificial Hormone System (AHS). The AHS enables an autonomous and decentralized task-allocation among a set of Processing Elements (PEs). The AHS works good in small scaled non-hierarchical scenarios. For larger scaled and hierarchical scenarios we developed the Hierarchical Artificial Hormone System (HAHS) and presented a first abstract concept of the Recursive Artificial Hormone System (RAHS). Both utilize the AHS in isolated smaller clusters of PEs to achieve the same functionality as the AHS with less communication. Thus, the whole task-set will be split to smaller and disjoint task-subsets which then will be distributed to the clusters. The HAHS uses a 2-Level hierarchy with a second regulation cycle between all clusters. The RAHS on the other hand is designed to manage a N-Level hierarchy. In this paper we will present the first implemented version of the RAHS, its evaluation and the comparison to the original AHS.
Year
DOI
Venue
2018
10.1109/ISORC.2018.00009
2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC)
Keywords
Field
DocType
Decentralized,Reliability,Artificial Hormone System,Recursive,Organic Computing,Hierarchic,Task-Allocation,Self-x,Autonomous
Resource management,Cluster (physics),System on a chip,Disjoint sets,Task analysis,Computer science,Electronics,Hierarchy,Recursion,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-5848-2
0
0.34
References 
Authors
10
2
Name
Order
Citations
PageRank
Andreas Lund1748.70
Uwe Brinkschulte241252.57