Web applications are mostly designed with multiple tiers for flexibility and software reusability. It is difficult to model the behavior of multi-tier Web applications due to the fact that the workload is dynamic and unpredictable and the resource demand in each tier is different. Those features also cause the task of resource allocation for multi-tier Web applications very challenging. In order to meet service level agreements (SLAs) with minimal resource costs, Web service providers should dynamically allocate appropriate resources to each tier. This is particularly important to minimize the monetary cost in the pay-as-you-go cloud computing environments. Recently, a number of rule and model based approaches have been proposed for resource provisioning in cloud computing. In this survey, we identify challenges of the resource allocation problem and conduct a comparative review on those rule and model based approaches for resource allocation in multi-tier Web sites. Given the analysis on their advantages and limitations, we outline research directions to further improve the effectiveness of resource management in multi-tier Web applications.

A Survey of Resource Management in Multi-Tier Web Applications

Cloud elasticity allows dynamic resource provisioning in concert with actual application demands. Feedback control approaches have been applied with success to resource allocation in physical servers. However, cloud dynamics make the design of an accurate and stable resource controller challenging, especially when application-level performance is considered as the measured output. Application-level performance is highly dependent on the characteristics of workload and sensitive to cloud dynamics. To address these challenges, we extend a self-tuning fuzzy control (STFC) approach, originally developed for response time assurance in web servers to resource allocation in virtualized environments. We introduce mechanisms for adaptive output amplification and flexible rule selection in the STFC approach for better adaptability and stability. Based on the STFC, we further design a two-layer QoS provisioning framework, DynaQoS, that supports adaptive multi-objective resource allocation and service differentiation. We implement a prototype of DynaQoS on a Xen-based cloud testbed. Experimental results on representative server workloads show that STFC outperforms popular controllers such as Kalman filter, ARMA and, Adaptive PI in the control of CPU, memory, and disk bandwidth resources under both static and dynamic workloads. Further results with multiple control objectives and service classes demonstrate the effectiveness of DynaQoS in performance-power control and service differentiation.

QoS Guarantees and Service Differentiation for Dynamic Cloud Applications

A virtualized data center faces important but challenging issue of performance isolation among heterogeneous customer applications. Performance interference resulting from the contention of shared resources among co-located virtual servers has significant impact on the dependability of application QoS. We propose and develop NINEPIN, a non-invasive and energy efficient performance isolation mechanism that mitigates performance interference among heterogeneous applications hosted in virtualized servers. It is capable of increasing data center utility. Its novel hierarchical control framework aligns performance isolation goals with the incentive to regulate the system towards optimal operating conditions. The framework combines machine learning based self-adaptive modeling of performance interference and energy consumption, utility optimization based performance targeting and a robust model predictive control based target tracking. We implement NINEPIN on a virtualized HP ProLiant blade server hosting SPEC CPU2006 and RUBiS benchmark applications. Experimental results demonstrate that NINEPIN outperforms a representative performance isolation approach, Q-Clouds, improving the overall system utility and reducing energy consumption.

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NINEPIN: Non-invasive and energy efficient performance isolation in virtualized servers

In a data center, various components of Web applications co-located on virtualized servers exhibit complex time-varying interactions and interference. It has a significant impact on the user perceived performance and power consumption of the underlying system. We propose and develop APPLEware, an autonomic middleware for joint performance and power control of co-located Web applications. It features a distributed control structure that provides performance assurance and energy efficiency for large complex systems. It applies machine learning based self-adaptive modeling to capture the complex and time-varying relationship between the application performance and allocation of resources to various application components, in the presence of highly dynamic and bursty workloads and inter-application performance interference. The distributed controllers perform coordinated resource allocation to meet the service level agreements of applications in an agile and energy-efficient manner. Experimental results based on a testbed implementation with benchmark applications demonstrate APPLEware's effectiveness and energy efficiency.

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Autonomic performance and power control for co-located Web applications on virtualized servers

Modern cloud data centers rely on server consolidation the allocation of several virtual machines on the same physical host to minimize their costs. Choosing the right consolidation level how many and which virtual machines are assigned to a physical server is a challenging problem, because contemporary multitier cloud applications must meet service level agreements in face of highly dynamic, nonstationary, and bursty workloads. In this paper, we deal with the problem of achieving the best consolidation level that can be attained without violating application service level agreements. We tackle this problem by devising fuzzy controller for consolidation and QoS FC2Q, a resource management framework exploiting feedback fuzzy logic control, that is able to dynamically adapt the physical CPU capacity allocated to the tiers of an application in order to precisely match the needs induced by the intensity of its current workload. We implement FC2Q on a real testbed and use this implementation to demonstrate its ability of meeting the aforementioned goals by means of a thorough experimental evaluation, carried out with real-world cloud applications and workloads. Furthermore, we compare the performance achieved by FC2Q against those attained by existing state-of-the-art alternative solutions, and we show that FC2Q works better than them in all the considered experimental scenarios. Copyright © 2014 John Wiley & Sons, Ltd.

FC2Q: exploiting fuzzy control in server consolidation for cloud applications with SLA constraints

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PERFUME: Power and performance guarantee with fuzzy MIMO control in virtualized servers