Centaur: Host-Side SSD Caching for Storage Performance Control

Koller, Ricardo; Mashtizadeh, Ali José; Rangaswami, Raju

doi:10.1109/icac.2015.44

Cited by 42 publications

(44 citation statements)

References 20 publications

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“…The need for partitioning host side caches has been studied in [9], [7], [5]. To further illustrate the need, we conduct a simple experiment where two VMs residing in the same host share a caching device that is managed with a global Least Recently Used (LRU) eviction policy, i.e., blocks are stored and evicted from a single cache in Least Recently Used order, with no attention paid to the VM making the disk request.…”

Section: A the Case For Smart Partitioningmentioning

confidence: 99%

“…To mitigate this problem, automated software layer solutions have been proposed that try to divide the cache space of a single disk dynamically among different VMs based on the nature and the priority of the workloads running inside the VMs such that the overall I/O performance is maximized [7], [8], [9]. Our work extends these systems to take advantage of deepening storage hierarchy options, allowing several different caching devices to be partitioned among a set of virtual machines.…”

Section: Introductionmentioning

confidence: 99%

“…However, this method is labor intensive, and prevents the cache from being dynamically managed as VM workloads change over time. Recently there has been work done for dynamic cache partitioning [9], [7], [5] however, they focus on managing a single shared cache layer and do not take advantage of the deepening storage hierarchy.…”

Section: Introductionmentioning

confidence: 99%

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Multi-cache: Dynamic, Efficient Partitioning for Multi-tier Caches in Consolidated VM Environments

Rajasekaran

Duan

Zhang

et al. 2016

2016 IEEE International Conference on Cloud Engineering (IC2E)

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Every physical machine in today's typical datacenter is backed by storage devices with hundreds of Gigabytes to Terabytes in size. Data center vendors usually use hard disk drives for their back-end storage as it is cheap and reliable. However, the increase in the I/O accesses to the back-end storage from one or many of the VMs hosted on a physical machine can reduce its overall accesses time significantly due to contention. This may not be suitable for interactive applications requiring low latency that might be co-located with other I/O intensive applications.In this paper we present Multi-Cache, a multi-layer cache management system that uses a combination of cache devices of varied speed and cost such as solid state drives, non-volatile memories, etc to mitigate this problem. Multi-Cache partitions each device dynamically at runtime according to the workload of each VM and its priority. We use a heuristic optimization technique that ensures maximum utilization of the caches resulting in a high hit rate.We use a weighted partitioning policy that improves latency by up to 72% for individual workloads, and a overall hit rate increase of up to 31% for host running several workloads together in comparison to standard LRU caching algorithms.

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Section: A the Case For Smart Partitioningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-cache: Dynamic, Efficient Partitioning for Multi-tier Caches in Consolidated VM Environments

Rajasekaran

Duan

Zhang

et al. 2016

2016 IEEE International Conference on Cloud Engineering (IC2E)

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“…1). Inclusion of SSDs in the I/O caching layer of systems improves the response time of the requests supplied by the cache, and hence, a wide range of enterprise and academic I/O cache architectures are proposed with the purpose of maximizing the hit ratio of the caching layer [10], [11], [12], [2], [13], [3], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28]. In these I/O caching schemes, mainly based on datapath or push mode cache architectures, the entire accesses are directed to the caching layer [29] and as such, the highest number of requests is responded via the caching layer to achieve the highest performance in terms of hit ratio.…”

Section: Introductionmentioning

confidence: 99%

LBICA: A Load Balancer for I/O Cache Architectures

Ahmadian

Salkhordeh

Asadi

2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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In recent years, enterprise Solid-State Drives (SSDs) are used in the caching layer of high-performance servers to close the growing performance gap between processing units and storage subsystem. SSD-based I/O caching is typically not effective in workloads with burst accesses in which the caching layer itself becomes the performance bottleneck because of the large number of accesses. Existing I/O cache architectures mainly focus on maximizing the cache hit ratio while they neglect the average queue time of accesses. Previous studies suggested bypassing the cache when burst accesses are identified. These schemes, however, are not applicable to a general cache configuration and also result in significant performance degradation on burst accesses.In this paper, we propose a novel I/O cache load balancing scheme (LBICA) with adaptive write policy management to prevent the I/O cache from becoming performance bottleneck in burst accesses. Our proposal, unlike previous schemes, which disable the I/O cache or bypass the requests into the disk subsystem in burst accesses, selectively reduces the number of waiting accesses in the SSD queue and balances the load between the I/O cache and the disk subsystem while providing the maximum performance. The proposed scheme characterizes the workload based on the type of in-queue requests and assigns an effective cache write policy. We aim to bypass the accesses which 1) are served faster by the disk subsystem or 2) cannot be merged with other accesses in the I/O cache queue. Doing so, the selected requests are responded by the disk layer, preventing from overloading the I/O cache. Our evaluations on a physical system shows that LBICA reduces the load on the I/O cache by 48% and improves the performance of burst workloads by 30% compared to the latest state-of-the-art load balancing scheme.

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“…In today's virtualized data centers, DRAM and host-side caching of file system data are used for speeding up VM performance [BLM + 12, KMR + 13, KMR15]. Big cloud data centers make use of virtualization techniques to deploy thousands of virtual machines in hundreds of hosts.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Main Memory Usage in Modern Computing Systems to Improve Overall System Performance

Campello¹

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Operating Systems use fast, CPU-addressable main memory to maintain an application's temporary data as anonymous data and to cache copies of persistent data stored in slower block-based storage devices. However, the use of this faster memory comes at a high cost. Therefore, several techniques have been implemented to use main memory more efficiently in the literature. In this thesis we introduce three distinct approaches to improve overall system performance by optimizing main memory usage.First, DRAM and host-side caching of file system data are used for speeding up virtual machine performance in today's virtualized data centers. The clustering of VM images that share identical pages, coupled with data deduplication, has the potential to optimize main memory usage, since it provides more opportunity for sharing resources across processes and across different VMs. In our first approach, we study the use of content and semantic similarity metrics and a new algorithm to cluster VM images and place them in hosts where through deduplication we improve main memory usage.Second, while careful VM placement can improve memory usage by eliminating duplicate data, caches in current systems employ complex machinery to manage the cached data. Writing data to a page not present in the file system page cache causes the operating system to synchronously fetch the page into memory, blocking the writing process. In this thesis, we address this limitation with a new approach to managing page writes involving v buffering the written data elsewhere in memory and unblocking the writing process immediately. This buffering allows the system to service file writes faster and with less memory resources.In our last approach, we investigate the use of emerging byte-addressable persistent memory technology to extend main memory as a less costly alternative to exclusively using expensive DRAM. We motivate and build a tiered memory system wherein persistent memory and DRAM co-exist and provide improved application performance at lower cost and power consumption with the goal of placing the right data in the right memory tier at the right time. The proposed approach seamlessly performs page migration across memory tiers as access patterns change and/or to handle tier memory pressure.

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Centaur: Host-Side SSD Caching for Storage Performance Control

Cited by 42 publications

References 20 publications

Multi-cache: Dynamic, Efficient Partitioning for Multi-tier Caches in Consolidated VM Environments

Multi-cache: Dynamic, Efficient Partitioning for Multi-tier Caches in Consolidated VM Environments

LBICA: A Load Balancer for I/O Cache Architectures

Optimizing Main Memory Usage in Modern Computing Systems to Improve Overall System Performance

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