Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center

Wang, Xiaoying; Lan, Dongjun; Wang, Gang; Fang, Xing; Meng, Yanmei; Chen, Ying; Wang, Qingbo

doi:10.1109/icac.2007.6

Cited by 54 publications

(29 citation statements)

References 20 publications

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“…Significant research has been conducted on utility-based autonomic resource management in recent years [1], [16], [19], [21], [23]. Welsh and Culler proposed the use of utility functions expressed in high-level service-level attributes to dynamically allocate resources in realistic autonomic computing system [21].…”

Section: Related Workmentioning

confidence: 99%

“…Menascé and Bennani considered dynamic priority scheduling and allocation of CPU shares to virtual servers [16]. Wang et al proposed a virtual-appliance-based autonomic resource provisioning framework for large virtualized data centers [23]. Weng et al designed a management framework for a virtualized cluster system, and presented an automatic performance tuning strategy to balance the workload [25].…”

Section: Related Workmentioning

confidence: 99%

“…Autonomic resource provisioning in data center servers has been an active and important research area [1], [5], [6], [16], [17], [18], [19], [21], [23]. Many of previous efforts applied the utility computing paradigm to achieve multiple objectives of a data center.…”

Section: Introductionmentioning

confidence: 99%

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aMOSS: Automated Multi-objective Server Provisioning with Stress-Strain Curving

Lama

Zhou

2011

2011 International Conference on Parallel Processing

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Abstract-A modern data center built upon virtualized server clusters for hosting Internet applications has multiple correlated and conflicting objectives. Utility-based approaches are often used for optimizing multiple objectives. However, it is difficult to define a local utility function to suitably represent one objective and to apply different weights on multiple local utility functions. Furthermore, choosing weights statically may not be effective in the face of highly dynamic workloads. In this paper, we propose an automated multi-objective server provisioning with stress-strain curving approach (aMOSS). First, we formulate a multi-objective optimization problem that is to minimize the number of physical machines used, the average response time and the total number of virtual servers allocated for multi-tier applications. Second, we propose a novel stress-strain curving method to automatically select the most efficient solution from a Pareto-optimal set that is obtained as the result of a nondominated sorting based optimization technique. Third, we enhance the method to reduce server switching cost and improve the utilization of physical machines. Simulation results demonstrate that compared to utility-based approaches, aMOSS automatically achieves the most efficient tradeoff between performance and resource allocation efficiency. We implement aMOSS in a testbed of virtualized blade servers and demonstrate that it outperforms a representative dynamic server provisioning approach in achieving the average response time guarantee and in resource allocation efficiency for a multi-tier Internet service. aMOSS provides a unique perspective to tackle the challenging autonomic server provisioning problem.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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aMOSS: Automated Multi-objective Server Provisioning with Stress-Strain Curving

Lama

Zhou

2011

2011 International Conference on Parallel Processing

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“…The proposed solver scans for the ideal number of physical servers for each AE, with a shaft seek calculation. In [13] a comparable design has been considered for machine based autonomic provisioning. The engineering characterizes some Virtual Application Environments (VAEs): a VAE traverses more than at least one virtual servers, and each virtual server is characterized inside a physical machine.…”

Section: Significance Of Resource Allocationmentioning

confidence: 99%

Efficient Allocation of Resources in Cloud Using Hybrid Optimization Based Algorithm

Yadav¹

2017

Ra Journals

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“…The optimization problem that we consider presents a non-linear optimization objective while previous approaches [15,16] to similar problems address only linear optimization objectives. In [17], the authors evaluate a similar problem to that addressed in our work (restricted to transactional applications), and use a simulated annealing optimization algorithm. Their strategy aims to maximize the overall system utility while we focus on first maximizing the performance of the least performing application in the system, which increases fairness and prevents starvation, as was shown in [18].…”

Section: Related Workmentioning

confidence: 99%

Enabling Resource Sharing between Transactional and Batch Workloads Using Dynamic Application Placement

Carrera

Steinder²,

Whalley³

et al. 2008

Middleware 2008

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Abstract. We present a technique that enables existing middleware to fairly manage mixed workloads: batch jobs and transactional applications. The technique leverages a generic application placement controller, which dynamically allocates compute resources to application instances. The controller works towards a fairness goal while also trying to maximize individual workload performance. We use relative performance functions to drive the application placement controller. Such functions are derived from workload-specific performance models-in the case of transactional workloads, we use queuing theory to build the performance model. For batch workloads, we evaluate a candidate placement by calculating long-term estimates of the completion times that are achievable with that placement according to a scheduling policy. In this paper, we propose a lowest relative performance first scheduling policy as a way to also achieve fair resource allocation among batch jobs. Our technique permits collocation of the workload types on the same physical hardware, and leverages control mechanisms such as suspension and migration to perform online system reconfiguration. In our experiments we demonstrate that our technique maximizes mixed workload performance while providing service differentiation based on highlevel performance goals.

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Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center

Cited by 54 publications

References 20 publications

aMOSS: Automated Multi-objective Server Provisioning with Stress-Strain Curving

aMOSS: Automated Multi-objective Server Provisioning with Stress-Strain Curving

Efficient Allocation of Resources in Cloud Using Hybrid Optimization Based Algorithm

Enabling Resource Sharing between Transactional and Batch Workloads Using Dynamic Application Placement

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