Performance virtualization for large-scale storage systems

Chambliss, D. D.; Alvarez, Guillermo A.; Pandey, Prashant; Jadav, Divyesh; Jian, Xu; Menon, Dr. RG Vishnu; Lee, T.P.

doi:10.1109/reldis.2003.1238060

Cited by 68 publications

(57 citation statements)

References 8 publications

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“…In order to enable aggressive provisioning and efficient resource utilization, we need techniques to quickly mitigate SLO violations. Current mitigation techniques are either temporary fixes (e.g., throttling workloads [10,18,13,35]) or operate over very large timescales (e.g., data migration [1,3,4]). As shown in Figure 1 and discussed elsewhere [11], workloads may experience peaks a few times a day.…”

Section: Dynamismmentioning

confidence: 99%

Responding rapidly to service level violations using virtual appliances

Bairavasundaram

Soundararajan

Mathur

et al. 2012

SIGOPS Oper. Syst. Rev.

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One of the key goals in the data center today is providing storage services with service-level objectives (SLOs) for performance metrics such as latency and throughput. Meeting such SLOs is challenging due to the dynamism observed in these environments. In this position paper, we propose dynamic instantiation of virtual appliances, that is, virtual machines with storage functionality, as a mechanism to meet storage SLOs efficiently.In order for dynamic instantiation to be realistic for rapidlychanging environments, it should be automated. Therefore, an important goal of this paper is to show that such automation is feasible. We do so through a caching case study. Specifically, we build the automation framework for dynamically instantiating virtual caching appliances. This framework identifies sets of interfering workloads that can benefit from caching, determines the cache-size requirements of workloads, non-disruptively migrates the application to use the cache, and warms the cache to quickly return to acceptable service levels. We show through an experiment that this approach addresses SLO violations while using resources efficiently.

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Section: Dynamismmentioning

confidence: 99%

Responding rapidly to service level violations using virtual appliances

Bairavasundaram

Soundararajan

Mathur

et al. 2012

SIGOPS Oper. Syst. Rev.

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“…To address the similar problem in the domain of storage, various adaptations of these algorithms have been proposed. For example, YFQ [11], SFQ(D) [5] are based on Start-time Fair Queue (SFQ) [12]; SLEDS [13] and SARC [14] are adaptations of the leaky bucket algorithm; CVC [15] [4] adopts VirtualClock [7]. However, directly applying fair queuing-based algorithm to storage system can introduce unfairness within a short period of time (Section II-C).…”

Section: Overview and Related Workmentioning

confidence: 99%

An Adaptive Mechanism for Fair Sharing of Storage Resources

Jin

Buyya

2009

2009 21st International Symposium on Computer Architecture and High Performance Computing

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Abstract-To ensure Quality of Service (QoS) for data centers, it is critical to enforce a fair share of storage resources between competing users. Interposed schedulers are one of the most practical methods for performance isolation. Most fair queuing-based proportional sharing algorithms for existing interposed scheduler are variants of counterparts designed for network routers and may result in breaking the fairness of proportional sharing required by Service Level Agreements for storage systems. This paper presents a novel algorithm to address this problem. As an extension of the fair queuingbased algorithm, it can dynamically adapt to the performance variation of storage systems and guarantee a fair sharing of resources as well as satisfying the minimal performance requirements for different clients. The design and performance evaluation are presented.

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“…Just like in our prototype, SLEDS [8], Façade [12], SFQ [10], and Argon [19] place a scheduling tier above the existing disk scheduler which controls the I/Os issued to the underlying disk. Argon [19] uses a quanta-based scheduler, while SLEDS [8] uses a leaky-bucket filter to throttle I/Os from clients exceeding their given fraction. Similarly, SFQ dynamically adjusts the deadline of I/Os to provide fair sharing of bandwidth.…”

Section: Related Workmentioning

confidence: 99%

Towards End-to-End Quality of Service: Controlling I/O Interference in Shared Storage Servers

Soundararajan

Amza

2008

Middleware 2008

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Abstract. Due to the imperative need to reduce the costs of management, power and cooling in large data centers, operators multiplex several concurrent applications on each physical server of a server farm connected to a shared network attached storage. Determining and enforcing per-application resource quotas on the fly in this context poses a complex resource allocation and control problem spanning many levels including the CPU, memory and storage resources within each physical server and/or across the server farm. This problem is further complicated by the need to provide end-to-end Quality of Service (QoS) guarantees to hosted applications.In this paper, we introduce a novel approach towards controlling application interference for resources in shared server farms. Specifically, we design and implement a minimally intrusive method for passing application-level QoS requirements through the software stack. We leverage high-level per-application requirements for controlling I/O interference between multiple database applications, by QoS-aware dynamic resource partitioning at the storage server. Our experimental evaluation, using the MySQL database engine and OLTP benchmarks, shows the effectiveness of our technique in enforcing high-level application Service Level Objectives (SLOs) in shared server farms.

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Performance virtualization for large-scale storage systems

Cited by 68 publications

References 8 publications

Responding rapidly to service level violations using virtual appliances

Responding rapidly to service level violations using virtual appliances

An Adaptive Mechanism for Fair Sharing of Storage Resources

Towards End-to-End Quality of Service: Controlling I/O Interference in Shared Storage Servers

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