Journal ArticleDOI
1000 islands: an integrated approach to resource management for virtualized data centers
Xiaoyun Zhu,Donald E. Young,Brian J. Watson,Zhikui Wang,Jerry Rolia,Sharad Singhal,Bret A. McKee,Chris D. Hyser,Daniel Gmach,Robert D. Gardner,Tom Christian,Ludmila Cherkasova +11 more
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TLDR
This paper describes an automated capacity and workload management system that integrates multiple resource controllers at three different scopes and time scales and confirms that such an integrated solution ensures efficient and effective use of data center resources while reducing service level violations for high priority applications.Abstract:
Recent advances in hardware and software virtualization offer unprecedented management capabilities for the mapping of virtual resources to physical resources. It is highly desirable to further create a "service hosting abstraction" that allows application owners to focus on service level objectives (SLOs) for their applications. This calls for a resource management solution that achieves the SLOs for many applications in response to changing data center conditions and hides the complexity from both application owners and data center operators. In this paper, we describe an automated capacity and workload management system that integrates multiple resource controllers at three different scopes and time scales. Simulation and experimental results confirm that such an integrated solution ensures efficient and effective use of data center resources while reducing service level violations for high priority applications.read more
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Proceedings ArticleDOI
Paragon: QoS-aware scheduling for heterogeneous datacenters
TL;DR: Paragon is an online and scalable DC scheduler that is heterogeneity and interference-aware, derived from robust analytical methods and uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload, by identifying similarities to previously scheduled applications.
Proceedings ArticleDOI
Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments
Jing Xu,José A. B. Fortes +1 more
TL;DR: A two-level control system to manage the mappings of workloads to VMs and VMs to physical resources and an improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives.
Journal ArticleDOI
Resource Management in Clouds: Survey and Research Challenges
Brendan Jennings,Rolf Stadler +1 more
TL;DR: This paper outlines a conceptual framework for cloud resource management and uses it to structure the state-of-the-art review, and identifies five challenges for future investigation that relate to providing predictable performance for cloud-hosted applications.
Journal ArticleDOI
Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms
TL;DR: The used problem formulations and optimization algorithms are surveyed, highlighting their strengths and limitations, and pointing out areas that need further research.
Journal ArticleDOI
Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments
TL;DR: The main novelty of the approach is to address-in a unifying framework-service centers resource management by exploiting as actuation mechanisms allocation of virtual machines to servers, load balancing, capacity allocation, server power state tuning, and dynamic voltage/frequency scaling.
References
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Journal ArticleDOI
httperf—a tool for measuring web server performance
David Mosberger,Tai Jin +1 more
TL;DR: In this article, the authors describe a tool for measuring web server performance called httperf, which provides a flexible facility for generating various HTTP workloads and for measuring server performance.
Proceedings ArticleDOI
No "power" struggles: coordinated multi-level power management for the data center
TL;DR: This paper proposes and validate a power management solution that coordinates different individual approaches and performs a detailed quantitative sensitivity analysis to draw conclusions about the impact of different architectures, implementations, workloads, and system design choices.