On the impact of virtualization on the I/O performance of analytic workloads

Ha, Son-Hai; Venzano, Daniele; Brown, Patrick; Michiardi, Pietro

doi:10.1109/cloudtech.2016.7847722

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Such scenarios are advised by authors in Reference 19. For example, operations of random read/write with

ρ = 1.5}

resembles the usual workloads in traditional databases, conventional batch‐processing over file systems, and big data applications 21‐23 …”

Section: Resultsmentioning

confidence: 99%

“…The attained disk performance of two separate applications, each running in a separate container, that perform different I/O‐intensive workload patterns such as sequentially/randomly reading/writing with 16 threads from/into large sized files. The selected pattern represents usual I/O access patterns in traditional relational database systems and file servers 21‐23 . The measurement results over different parameters (e.g., as I/O request response times) showed that the performance degradation and variation among collocated workloads can be prevalent and significant even by employing the LXC, which imposes lower overheads to the system compared with VMMs.…”

Section: Motivational Studymentioning

confidence: 99%

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Enhancing disk input output performance in consolidated virtualized cloud platforms using a randomized approximation scheme

HoseinyFarahabady

Taheri

Zomaya

et al. 2021

Concurrency and Computation

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In a virtualized computer system with shared resources, consolidated virtual services (VSs) fiercely compete with each other to obtain the required capacity of resources, and this causes significant system's performance degradation. The performance of input output (I/O)‐bound applications running inside their own VS is mainly determined by the total time required to schedule every read/write request, plus the actual time needed by the device driver to complete the request. To achieve a right performance isolation of shared resources (e.g., the last level cache, memory bandwidth, and the disk buffer), it is essential to limit the performance degradation level among collocated applications, as simultaneously several I/O operations are requested by VSs, perhaps with different priorities. This article proposes a resource allocation controller that uses a fully polynomial‐time randomized approximation scheme to enable performance isolation of concurrent I/O requests in a shared system with multiple consolidated VSs. This controller uses a Monte Carlo sampling approach to measure and estimate the unknown attributes of operational requests originating from each VS. This is formalized as an optimization problem with the aim to minimize the degree of total quality of service (QoS) violation incidents in the entire platform. We associated a reward function to every working machine that represents the fulfillment degree of quality of service metric among all running VSs. The conducted comprehensive set of experiments showed that the proposed algorithm can reduce the QoS violation incidents by 32%, compared with the result which is obtained by employing the default resource allocation policy embedded in the existing Linux container layer.

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“…Such scenarios are advised by authors in Reference 19. For example, operations of random read/write with

ρ = 1.5}

resembles the usual workloads in traditional databases, conventional batch‐processing over file systems, and big data applications 21‐23 …”

Section: Resultsmentioning

confidence: 99%

Section: Motivational Studymentioning

confidence: 99%

Enhancing disk input output performance in consolidated virtualized cloud platforms using a randomized approximation scheme

HoseinyFarahabady

Taheri

Zomaya

et al. 2021

Concurrency and Computation

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“…directsync cache mode of QEMU-KVM uses both O_DSYNC and O_DIRECT semantics. The guest's I/O requests bypass the host page cache and are serviced with the FUA flag [1,10,19]. Microsoft SQL Server uses Write-Ahead Logging (WAL) to ensure ACID properties of transactions [37].…”

Section: Introductionmentioning

confidence: 99%

Fair I/O scheduler for alleviating read/write interference by forced unit access in flash memory

Kim

Won

2022

Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems

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For the past few years, the enterprise Solid State Drives that employ NVM Express are widely used due to their high performance. It is common for multiple tenants and processes to share a single SSD. Providing fair SSD performance for multiple applications has become an important issue. We observe that the write request with FUA flag delays the processing of read request due to SSD internal read/write interference. To alleviate the performance degradation of the read requests, we propose TABS, per-Type fAir Bandwidth I/O Scheduler for NVMe SSDs. TABS determines the fair bandwidth proportional to the maximum bandwidth and I/O issue rate for each type of request. (i) Two-phase Dynamic Scheduling sets fairness goals dynamically according to the I/O patterns of workload. Then, it throttles the FUA write requests to meet the pre-measured fairness goal. (ii) Software-based feedback makes more accurate scheduling possible. By using these techniques, TABS can guarantee fairness between the read and FUA flagged write requests. Finally, compared with the fairness goals, TABS achieves 76% fairness on average and up to 99.5% fairness, while the noop scheduler, the default Linux scheduler, shows 18% fairness. CCS CONCEPTS• Information systems → Flash memory.

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