AdaptGuard: guarding adaptive systems from instability
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
GreenCloud: a new architecture for green data center
ICAC-INDST '09 Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session
Semantic-less coordination of power management and application performance
ACM SIGOPS Operating Systems Review
MEC-IDC: joint load balancing and power control for distributed Internet Data Centers
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems
Autonomic mix-aware provisioning for non-stationary data center workloads
Proceedings of the 7th international conference on Autonomic computing
Stochastic approximation control of power and tardiness in a three-tier web-hosting cluster
Proceedings of the 7th international conference on Autonomic computing
INFOCOM'10 Proceedings of the 29th conference on Information communications
Adaptive power management for real-time event streams
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Dynamic and adaptive allocation of applications on MPSoC platforms
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Leveraging Heterogeneity for Energy Minimization in Data Centers
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Power-aware optimization for heterogeneous multi-tier clusters
Journal of Parallel and Distributed Computing
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The increased complexity of performance-sensitive soft- ware systems leads to increased use of automated adapta- tion policies in lieu of manual performance tuning. Com- position of adaptive components into larger adaptive sys- tems, however, presents challenges that arise from potential incompatibilities among the respective adaptation policies. Consequently, unstable or poorly-tuned feedback loops may result that cause performance deterioration. This paper (i) presents a mechanism, called adaptation graph analy- sis, for identifying potential incompatibilities between com- posed adaptation policies and (ii) illustrates a general de- sign methodology for co-adaptation that resolves such in- compatibilities. Our results are demonstrated by a case study on energy minimization in multi-tier Web server farms subject to soft real-time constraints. Two independently effi- cient energy saving policies (an On/Off policy that switches machines off when not needed and a dynamic voltage scal- ing policy) are shown to conflict leading to increased en- ergy consumption when combined. Our adaptation graph analysis predicts the problem, and our co-adaptation design methodology finds a solution that improves performance. Experimental results from a 17-server farm running the in- dustry standard TPC-W e-commerce benchmark show that co-adaptation renders a cut-down in energy consumption by more than 50%, when workload is not high, while main- taining latency within acceptable bounds. The paper serves as a proof of concept of the proposed conflict-identification and resolution methodology and an invitation to further in- vestigate a science for composing adaptive systems.