Virtualized data centers enable sharing of resources among hosted applications. However, it is difficult to satisfy servicelevel objectives (SLOs) of applications on shared infrastructure, as application workloads and resource consumption patterns change over time. In this paper, we present AutoControl, a resource control system that automatically adapts to dynamic workload changes to achieve application SLOs. AutoControl is a combination of an online model estimator and a novel multi-input, multi-output (MIMO) resource controller. The model estimator captures the complex relationship between application performance and resource allocations, while the MIMO controller allocates the right amount of multiple virtualized resources to achieve application SLOs. Our experimental evaluation with RUBiS and TPC-W benchmarks along with production-trace-driven workloads indicates that AutoControl can detect and mitigate CPU and disk I/O bottlenecks that occur over time and across multiple nodes by allocating each resource accordingly. We also show that AutoControl can be used to provide service differentiation according to the application priorities during resource contention.

Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a twotier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.

Feedback mechanisms can help today's increasingly complex computer systems adapt to changes in workloads or operating conditions. Control theory offers a principled way for designing feedback loops to deal with unpredictable changes, uncertainties, and disturbances in systems. We provide an overview of the joint research at HP Labs and University of Michigan in the past few years, where control theory was applied to automated resource and service level management in data centers. We highlight the key benefits of a control-theoretic approach for systems research, and present specific examples from our experience of designing adaptive resource control systems where this approach worked well. In addition, we outline the main limitations of this approach, and discuss the lessons learned from our experience.

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Abstract

Today's enterprise data centers are shifting towards a utility computing model where many business critical applications share a common pool of infrastructure resources that offer capacity on demand. Management of such a pool requires having a control system that can dynamically allocate resources to applications in real time. Although this is possible by use of virtualization technologies, capacity overhead or actuation delay may occur due to frequent re-scheduling in the virtualization layer. This paper evaluates the overhead of a dynamic allocation scheme in both system capacity and application-level performance relative to static allocation. We conducted experiments with virtual containers built using Xen and OpenVZ technologies for hosting both computational and transactional workloads. We present the results of the experiments as well as plausible explanations for them. We also describe implications and guidelines for feedback controller design in a dynamic allocation system based on our observations.

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