Autonomic power and performance management for computing systems
Cluster Computing
A survey of autonomic computing—degrees, models, and applications
ACM Computing Surveys (CSUR)
Adapting to Run-Time Changes in Policies Driving Autonomic Management
ICAS '08 Proceedings of the Fourth International Conference on Autonomic and Autonomous Systems
SLA e-Negotiations, Enforcement and Management in an Autonomic Environment
MACE '08 Proceedings of the 3rd IEEE international workshop on Modelling Autonomic Communications Environments
Knowledge representation concepts for automated SLA management
Decision Support Systems
Future Generation Computer Systems
Bioinformatics
VCONF: a reinforcement learning approach to virtual machines auto-configuration
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Towards Self-Manageable Cloud Services
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 02
Sandpiper: Black-box and gray-box resource management for virtual machines
Computer Networks: The International Journal of Computer and Telecommunications Networking
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
A dynamic optimization model for power and performance management of virtualized clusters
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Efficient resource provisioning in compute clouds via VM multiplexing
Proceedings of the 7th international conference on Autonomic computing
Resource allocation algorithms for virtualized service hosting platforms
Journal of Parallel and Distributed Computing
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
The reservoir model and architecture for open federated cloud computing
IBM Journal of Research and Development
An approach for virtual appliance distribution for service deployment
Future Generation Computer Systems
Enacting SLAs in clouds using rules
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
Optimizing bioinformatics workflows for data analysis using cloud management techniques
Proceedings of the 6th workshop on Workflows in support of large-scale science
Cost-Efficient Utilization of Public SLA Templates in Autonomic Cloud Markets
UCC '11 Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing
Energy-aware service allocation
Future Generation Computer Systems
Cost-benefit analysis of an SLA mapping approach for defining standardized Cloud computing goods
Future Generation Computer Systems
Policy based resource allocation in IaaS cloud
Future Generation Computer Systems
Empirical prediction models for adaptive resource provisioning in the cloud
Future Generation Computer Systems
Self-Adaptive and Resource-Efficient SLA Enactment for Cloud Computing Infrastructures
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
Managing and Optimizing Bioinformatics Workflows for Data Analysis in Clouds
Journal of Grid Computing
SLA-driven dynamic cloud resource management
Future Generation Computer Systems
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To guarantee the vision of Cloud Computing QoS goals between the Cloud provider and the customer have to be dynamically met. This so-called Service Level Agreement (SLA) enactment should involve little human-based interaction in order to guarantee the scalability and efficient resource utilization of the system. To achieve this we start from Autonomic Computing, examine the autonomic control loop and adapt it to govern Cloud Computing infrastructures. We first hierarchically structure all possible adaptation actions into so-called escalation levels. We then focus on one of these levels by analyzing monitored data from virtual machines and making decisions on their resource configuration with the help of knowledge management (KM). The monitored data stems both from synthetically generated workload categorized in different workload volatility classes and from a real-world scenario: scientific workflow applications in bioinformatics. As KM techniques, we investigate two methods, Case-Based Reasoning and a rule-based approach. We design and implement both of them and evaluate them with the help of a simulation engine. Simulation reveals the feasibility of the CBR approach and major improvements by the rule-based approach considering SLA violations, resource utilization, the number of necessary reconfigurations and time performance for both, synthetically generated and real-world data.