Pentium 4 Performance-Monitoring Features
IEEE Micro
Organic computing: on the feasibility of controlled emergence
Proceedings of the 2nd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
Task activity vectors: a new metric for temperature-aware scheduling
Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008
Examinating Task Distribution by an Artificial Hormone System Based Middleware
ISORC '08 Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing
The Degree of Global-State Awareness in Self-Organizing Systems
IWSOS '09 Proceedings of the 4th IFIP TC 6 International Workshop on Self-Organizing Systems
Autonomic workload management for multi-core processor systems
ARCS'10 Proceedings of the 23rd international conference on Architecture of Computing Systems
A survey and taxonomy of on-chip monitoring of multicore systems-on-chip
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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The growing complexity of future heterogeneous and parallel computing systems is addressed by Organic Computing principles, employing so-called Self-X features for autonomous adaptation and optimization. Here, one major problem is the fact that individual system components only have knowledge about their own states and is therefore lacking the global picture; as a result, each component is unable to determine whether given constraints or requirements are met, whether an optimization cycle should be triggered or not. Even worse, a local instance cannot evaluate the outcome of such optimization cycles and therefore is unable to rate whether the measures taken resulted in a global improvement or not. In order to solve this problem, we present a novel rule-based approach for online system-state evaluation and classification. The rules used for system evaluation are derived during runtime from the information provided by a dedicated, distributed monitoring infrastructure. An important feature of this approach is its capability to self-adapt, i.e., the monitoring infrastructure can adapt the rules to react to given requirements and/or changed system behavior. The proposed method is light-weight to be efficiently employed in self-organizing parallel manycore systems.