306 research outputs found
Spatial Locality Aware Disk Scheduling in Virtualized Environment
International audienceExploiting spatial locality, a key technique for improving disk I/O utilization and performance, faces additional challenges in the virtualized cloud because of the transparency feature of virtualization. This paper contributes a novel disk I/O scheduling framework, named Pregather, to improve disk I/O efficiency through exposure and exploitation of the special spatial locality in the virtualized environment, thereby improving the performance of disk-intensive applications without harming the transparency feature of virtualization. The key idea behind Pregather is to implement an intelligent model to predict the access regularity of spatial locality for each VM. Moreover, Pregather embraces an adaptive time slice allocation scheme to further reduce the resource contention and ensure fairness among VMs. We implement the Pregather disk scheduling framework and perform extensive experiments that involve multiple simultaneous applications of both synthetic benchmarks and MapReduce applications on Xen-based platforms. Our experiments demonstrate the accuracy of our prediction model and indicate that Pregather results in the high disk spatial locality and a significant improvement in disk throughput and application performance
A survey and classification of storage deduplication systems
The automatic elimination of duplicate data in a storage system commonly known as deduplication is increasingly accepted as an effective technique to reduce storage costs. Thus, it has been applied to different storage types, including archives and backups, primary storage, within solid state disks, and even to random access memory. Although the general approach to deduplication is shared by all storage types, each poses specific challenges and leads to different trade-offs and solutions. This diversity is often misunderstood, thus underestimating the relevance of new research and development.
The first contribution of this paper is a classification of deduplication systems according to six criteria that correspond to key design decisions: granularity, locality, timing, indexing, technique, and scope.
This classification identifies and describes the different approaches used for each of them. As a second contribution, we describe which combinations of these design decisions have been proposed and found more useful for challenges in each storage type. Finally, outstanding research challenges and unexplored design points are identified and discussed.This work is funded by the European Regional Development Fund (EDRF) through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the Fundacao para a Ciencia e a Tecnologia (FCT; Portuguese Foundation for Science and Technology) within project RED FCOMP-01-0124-FEDER-010156 and the FCT by PhD scholarship SFRH-BD-71372-2010
ON OPTIMIZATIONS OF VIRTUAL MACHINE LIVE STORAGE MIGRATION FOR THE CLOUD
Virtual Machine (VM) live storage migration is widely performed in the data cen- ters of the Cloud, for the purposes of load balance, reliability, availability, hardware maintenance and system upgrade. It entails moving all the state information of the VM being migrated, including memory state, network state and storage state, from one physical server to another within the same data center or across different data centers. To minimize its performance impact, this migration process is required to be transparent to applications running within the migrating VM, meaning that ap- plications will keep running inside the VM as if there were no migration operations at all.
In this dissertation, a thorough literature review is conducted to provide a big picture of the VM live storage migration process, its problems and existing solutions. After an in-depth examination, we observe that a severe IO interference between the VM IO threads and migration IO threads exists and causes both types of the IO threads to suffer from performance degradation. This interference stems from the fact that both types of IO threads share the same critical IO path by reading from and writing to the same shared storage system. Owing to IO resource contention and requests interference between the two different types of IO requests, not only will the IO request queue lengthens in the storage system, but the time-consuming disk seek operations will also become more frequent. Based on this fundamental observation, this dissertation research presents three related but orthogonal solutions that tackle the IO interference problem in order to improve the VM live storage migration performance.
First, we introduce the Workload-Aware IO Outsourcing scheme, called WAIO, to improve the VM live storage migration efficiency. Second, we address this problem by proposing a novel scheme, called SnapMig, to improve the VM live storage migration efficiency and eliminate its performance impact on user applications at the source server by effectively leveraging the existing VM snapshots in the backup servers. Third, we propose the IOFollow scheme to improve both the VM performance and migration performance simultaneously. Finally, we outline the direction for the future research work.
Advisor: Hong Jian
Optimizing Virtual Machine I/O Performance in Cloud Environments
Maintaining closeness between data sources and data consumers is crucial for workload I/O performance. In cloud environments, this kind of closeness can be violated by system administrative events and storage architecture barriers. VM migration events are frequent in cloud environments. VM migration changes VM runtime inter-connection or cache contexts, significantly degrading VM I/O performance. Virtualization is the backbone of cloud platforms. I/O virtualization adds additional hops to workload data access path, prolonging I/O latencies. I/O virtualization overheads cap the throughput of high-speed storage devices and imposes high CPU utilizations and energy consumptions to cloud infrastructures. To maintain the closeness between data sources and workloads during VM migration, we propose Clique, an affinity-aware migration scheduling policy, to minimize the aggregate wide area communication traffic during storage migration in virtual cluster contexts. In host-side caching contexts, we propose Successor to recognize warm pages and prefetch them into caches of destination hosts before migration completion. To bypass the I/O virtualization barriers, we propose VIP, an adaptive I/O prefetching framework, which utilizes a virtual I/O front-end buffer for prefetching so as to avoid the on-demand involvement of I/O virtualization stacks and accelerate the I/O response. Analysis on the traffic trace of a virtual cluster containing 68 VMs demonstrates that Clique can reduce inter-cloud traffic by up to 40%. Tests of MPI Reduce_scatter benchmark show that Clique can keep VM performance during migration up to 75% of the non-migration scenario, which is more than 3 times of the Random VM choosing policy. In host-side caching environments, Successor performs better than existing cache warm-up solutions and achieves zero VM-perceived cache warm-up time with low resource costs. At system level, we conducted comprehensive quantitative analysis on I/O virtualization overheads. Our trace replay based simulation demonstrates the effectiveness of VIP for data prefetching with ignorable additional cache resource costs
SimFS: A Simulation Data Virtualizing File System Interface
Nowadays simulations can produce petabytes of data to be stored in parallel
filesystems or large-scale databases. This data is accessed over the course of
decades often by thousands of analysts and scientists. However, storing these
volumes of data for long periods of time is not cost effective and, in some
cases, practically impossible. We propose to transparently virtualize the
simulation data, relaxing the storage requirements by not storing the full
output and re-simulating the missing data on demand. We develop SimFS, a file
system interface that exposes a virtualized view of the simulation output to
the analysis applications and manages the re-simulations. SimFS monitors the
access patterns of the analysis applications in order to (1) decide the data to
keep stored for faster accesses and (2) to employ prefetching strategies to
reduce the access time of missing data. Virtualizing simulation data allows us
to trade storage for computation: this paradigm becomes similar to traditional
on-disk analysis (all data is stored) or in situ (no data is stored) according
with the storage resources that are assigned to SimFS. Overall, by exploiting
the growing computing power and relaxing the storage capacity requirements,
SimFS offers a viable path towards exa-scale simulations
Victima: Drastically Increasing Address Translation Reach by Leveraging Underutilized Cache Resources
Address translation is a performance bottleneck in data-intensive workloads
due to large datasets and irregular access patterns that lead to frequent
high-latency page table walks (PTWs). PTWs can be reduced by using (i) large
hardware TLBs or (ii) large software-managed TLBs. Unfortunately, both
solutions have significant drawbacks: increased access latency, power and area
(for hardware TLBs), and costly memory accesses, the need for large contiguous
memory blocks, and complex OS modifications (for software-managed TLBs). We
present Victima, a new software-transparent mechanism that drastically
increases the translation reach of the processor by leveraging the
underutilized resources of the cache hierarchy. The key idea of Victima is to
repurpose L2 cache blocks to store clusters of TLB entries, thereby providing
an additional low-latency and high-capacity component that backs up the
last-level TLB and thus reduces PTWs. Victima has two main components. First, a
PTW cost predictor (PTW-CP) identifies costly-to-translate addresses based on
the frequency and cost of the PTWs they lead to. Second, a TLB-aware cache
replacement policy prioritizes keeping TLB entries in the cache hierarchy by
considering (i) the translation pressure (e.g., last-level TLB miss rate) and
(ii) the reuse characteristics of the TLB entries. Our evaluation results show
that in native (virtualized) execution environments Victima improves average
end-to-end application performance by 7.4% (28.7%) over the baseline four-level
radix-tree-based page table design and by 6.2% (20.1%) over a state-of-the-art
software-managed TLB, across 11 diverse data-intensive workloads. Victima (i)
is effective in both native and virtualized environments, (ii) is completely
transparent to application and system software, and (iii) incurs very small
area and power overheads on a modern high-end CPU.Comment: To appear in 56th IEEE/ACM International Symposium on
Microarchitecture (MICRO), 202
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