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

Abstract

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.

Coordinated VM Resizing and Server Tuning: Throughput, Power Efficiency and Scalability

“…As many others in [2], [11], [12], [20], [24], [30], we use RUBiS [1] as the benchmark application in conducting the experiments. RUBiS is an open source multi-tier Internet benchmark application.…”

Automated and Agile Server Parameter Tuning with Learning and Control

“…Recently, autonomic computing in modern data centers has become a very active and important research area [2], [6], [8], [15], [18], [20], [21], [23], [24], [27], [29]. Those studies focused on capacity planning for virtual machines (VMs) co-location and distribution across a data center [8], [15], [26], VM provisioning for applications [3], [9], [10], [12], [24], [25], resource allocation in a VM [20], [21], [7], and server parameter tuning [2], [29].…”

“…As many others in [2], [21], [11], [10], We use RUBiS [1], [22] e-transationcal benchmark application for multi-tier Internet applications. RUBiS provides a web auction application modeled in a similar way of ebay.com and an emulation client used as the workload benchmark.…”

“…We implement the new approach in a testbed of virtualized HP ProLiant blade system. Like others [2], [10], [11], [21], we adopt RUBiS, an e-transactional benchmark application [1], [22] for performance evaluation and use three different workload characteristics: stationary, bursty, and step-change.…”