Multivariable System Identification for Process Control
Multivariable System Identification for Process Control
Condor-G: A Computation Management Agent for Multi-Institutional Grids
Cluster Computing
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
An Agent-based Resource Allocation Model for Grid Computing
SCC '05 Proceedings of the 2005 IEEE International Conference on Services Computing - Volume 01
Virtual workspaces: Achieving quality of service and quality of life in the Grid
Scientific Programming - Dynamic Grids and Worldwide Computing
ARMS: An agent-based resource management system for grid computing
Scientific Programming
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
On the Use of Fuzzy Modeling in Virtualized Data Center Management
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Combining batch execution and leasing using virtual machines
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
pMapper: power and migration cost aware application placement in virtualized systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Cost-aware scheduling for heterogeneous enterprise machines (CASH'EM)
CLUSTER '07 Proceedings of the 2007 IEEE International Conference on Cluster Computing
VCONF: a reinforcement learning approach to virtual machines auto-configuration
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Automated control in cloud computing: challenges and opportunities
ACDC '09 Proceedings of the 1st workshop on Automated control for datacenters and clouds
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Self-Tuning Virtual Machines for Predictable eScience
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Virtual Infrastructure Management in Private and Hybrid Clouds
IEEE Internet Computing
A Heuristic for Mapping Virtual Machines and Links in Emulation Testbeds
ICPP '09 Proceedings of the 2009 International Conference on Parallel Processing
Shares and utilities based power consolidation in virtualized server environments
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Understanding Performance Interference of I/O Workload in Virtualized Cloud Environments
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Reducing Costs of Spot Instances via Checkpointing in the Amazon Elastic Compute Cloud
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Enforcing SLAs in Scientific Clouds
CLUSTER '10 Proceedings of the 2010 IEEE International Conference on Cluster Computing
Multiplexing low and high QoS workloads in virtual environments
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
Rule-Based Mapping of Virtual Machines in Clouds
PDP '11 Proceedings of the 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing
MASCOTS '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
AppRAISE: application-level performance management in virtualized server environments
IEEE Transactions on Network and Service Management
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Virtualization technology makes data centers more dynamic and easier to administrate. Today, cloud providers offer customers access to complex applications running on virtualized hardware. Nevertheless, big virtualized data centers become stochastic environments and the simplification on the user side leads to many challenges for the provider. He has to find cost-efficient configurations and has to deal with dynamic environments to ensure service level objectives (SLOs). We introduce a software solution that reduces the degree of human intervention to manage clouds. It is designed as a multi-agent system (MAS) and placed on top of the Infrastructure as a Service (IaaS) layer. Worker agents allocate resources, configure applications, check the feasibility of requests, and generate cost estimates. They are equipped with application specific knowledge allowing it to estimate the type and number of necessary resources. During runtime, a worker agent monitors the job and adapts its resources to ensure the specified quality of service--even in noisy clouds where the job instances are influenced by other jobs. They interact with a scheduler agent, which takes care of limited resources and does a cost-aware scheduling by assigning jobs to times with low costs. The whole architecture is self-optimizing and able to use public or private clouds. Building a private cloud needs to face the challenge to find a mapping of virtual machines (VMs) to hosts. We present a rule-based mapping algorithm for VMs. It offers an interface where policies can be defined and combined in a generic way. The algorithm performs the initial mapping at request time as well as a remapping during runtime. It deals with policy and infrastructure changes. An energy-aware scheduler and the availability of cheap resources provided by a spot market are analyzed. We evaluated our approach by building up an SaaS stack, which assigns resources in consideration of an energy function and that ensures SLOs of two different applications, a brokerage system and a high-performance computing software. Experiments were done on a real cloud system and by simulations.