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.
Enterprise-scale storage systems, which can contain hundreds of host computers and storage devices and up to tens of thousands of disks and logical volumes, are difficult to design. The volume of choices that need to be made is massive, and many choices have unforeseen interactions. Storage system design is tedious and complicated to do by hand, usually leading to solutions that are grossly overprovisioned, substantially under-performing or, in the worst case, both.To solve the configuration nightmare, we present MINERVA: a suite of tools for designing storage systems automatically. MINERVA uses declarative specifications of application requirements and device capabilities; constraint-based formulations of the various subproblems; and optimization techniques to explore the search space of possible solutions. This paper also explores and evaluates the design decisions that went into MINERVA, using specialized micro and macro-benchmarks. We show that MINERVA can successfully handle a workload with substantial complexity (a decision-support database benchmark). MINERVA created a 16-disk design in only a few minutes that achieved the same performance as a 30-disk system manually designed by human experts. Of equal importance, MINERVA was able to predict the r esulting system's performance before it was built. AbstractEnterprise-scale storage systems, which can contain hundreds of host computers and storage devices and up to tens of thousands of disks and logical volumes, are difficult to design. The volume of choices that need to be made is massive, and many choices have unforeseen interactions. Storage system design is tedious and complicated to do by hand, usually leading to solutions that are grossly over-provisioned, substantially under-performing or, in the worst case, both.To solve the configuration nightmare, we present MIN-ERVA: a suite of tools for designing storage systems automatically. MINERVA uses declarative specifications of application requirements and device capabilities; constraintbased formulations of the various sub-problems; and optimization techniques to explore the search space of possible solutions. This paper also explores and evaluates the design decisions that went into MINERVA, using specialized microand macro-benchmarks. We show that MINERVA can successfully handle a workload with substantial complexity (a decision-support database benchmark). MIN-ERVA created a 16-disk design in only a few minutes that achieved the same performance as a 30-disk system manually designed by human experts. Of equal importance, MINERVA was able to predict the resulting system's performance before it was built.
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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