Multi-dimensional scheduling in cloud storage systems

Yao, Zhihao; Papapanagiotou, Ioannis; Callaway, Robert D.

doi:10.1109/icc.2015.7248353

Cited by 14 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, their scheme can offer higher volume I/O throughput performance under fewer storage nodes. Subsequently, Yao et al [34] proposed a Modified Vector Best Fit Decreasing algorithm (MVBFD) to address the volume allocation problem for cloud storage systems in 2015. They chose the proper storage node according to multiple resources, the volume requests and so on.…”

Section: Related Workmentioning

confidence: 99%

SLA-aware resource scheduling algorithm for cloud storage

Wang

Tao

Zhao

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

Cloud computing is a novel computing paradigm, which connects plenty of computing resources and storage resources via Internet. Cloud computing provides a number of high-quality services, such as cloud storage, outsourcing computing, and on-demand self-service, which have been widely accepted by the public. In cloud computing, by submitting their tasks to cloud, plenty of applications share huge computation and storage resources. However, how to schedule resource efficiently is a big challenge in cloud computing. In this paper, we propose a SLA-aware resource algorithm to enable cloud storage more efficiently. In our scheme, we take advantage of the back-end node space utilization and I/O throughput comprehensively simultaneously. We compare and contrast the existing scheduling storage policies by implementing those algorithms. The extensive tests show that our algorithm achieves a considerable improvement in terms of violation rate and the number of used hosts.

show abstract

Section: Related Workmentioning

confidence: 99%

SLA-aware resource scheduling algorithm for cloud storage

Wang

Tao

Zhao

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…Amazon Elastic Block Store (EBS) [1] and OpenStack Cinder [16] provide persistent block level storage volumes with virtual instances, usually via iSCSI [19]. For cinder-scheduler, Yao et al design and implement a new scheduling filter with the ability of IO throughput filtering and weighting to meet IOPS requirements [26], and propose a new block storage resource scheduling algorithm called MVBFD [27].…”

Section: Related Workmentioning

confidence: 99%

IOArbiter: Dynamic Provisioning of Backend Block Storage in the Cloud

Ra,

Lee

2019

Preprint

View full text Add to dashboard Cite

With the advent of virtualization technology, cloud computing realizes on-demand computing. The capability of dynamic resource provisioning is a fundamental driving factor for users to adopt the cloud technology. The aspect is important for cloud service providers to optimize the expense for running the infrastructure as well. Despite many technological advances in related areas, however, it is still the case that the infrastructure providers must decide hardware configuration before deploying a cloud infrastructure, especially from the storage's perspective. This static nature of the storage provisioning practice can cause many significant problems in meeting tenant requirements, which often come later into the picture. In this paper, we propose a system called IOArbiter that enables the dynamic creation of underlying storage implementation in the cloud. IOArbiter defers storage provisioning to the time at which a tenant actually requests a storage space. As a result, an underlying storage implementation, e.g., RAID-5/6 or Ceph storage pool with (6,3) erasure coding, will be materialized at the volume creation time. Using our prototype implementation with OpenStack Cinder, we show that IOArbiter can simultaneously satisfy a number of different tenant demands, which may not be possible with a static configuration. Additionally the built-in QoS mechanisms, including admission control and dynamic throttling, help IOArbiter system mitigate a noisy neighbor problem among tenants.

show abstract

“…Finally, there are management frameworks focusing on providing better SLOs to block storage consumers. Some, e.g., [26,27], proposed near optimal scheduling algorithms that considered multi-dimensional resources to enable performance-SLO management capabilities for cloud storage backend systems. PriorityMeister [28] focused on providing end-to-end tail latency QoS to meet performance SLOs.…”

Section: Related Workmentioning

confidence: 99%

Serifos: Workload Consolidation and Load Balancing for SSD Based Cloud Storage Systems

Yao¹,

Papapanagiotou²,

Griffith³

2015

Preprint

Self Cite

View full text Add to dashboard Cite

Achieving high performance in virtualized data centers requires both deploying high throughput storage clusters, i.e. based on Solid State Disks (SSDs), as well as optimally consolidating the workloads across storage nodes. Nowadays, the only practical solution for cloud storage providers to offer guaranteed performance is to grossly over-provision the storage nodes. The current workload scheduling mechanisms used in production do not have the intelligence to optimally allocate block storage volumes based on the performance of SSDs. In this paper, we introduce Serifos, an autonomous performance modeling and load balancing system designed for SSD-based cloud storage. Serifos takes into account the characteristics of the SSD storage units and constructs hardwaredependent workload consolidation models. Thus Serifos is able to predict the latency caused by workload interference and the average latency of concurrent workloads. Furthermore, Serifos leverages an I/O load balancing algorithm to dynamically balance the volumes across the cluster.Experimental results indicate that Serifos consolidation model is able to maintain the mean prediction error of around 10% for heterogeneous hardware. As a result of Serifos load balancing, we found that the variance and the maximum average latency are reduced by 82% and 52%, respectively. The supported Service Level Objectives (SLOs) on the testbed improve 43% on average latency, 32% on the maximum read and 63% on the maximum write latency.

show abstract

Multi-dimensional scheduling in cloud storage systems

Cited by 14 publications

References 13 publications

SLA-aware resource scheduling algorithm for cloud storage

SLA-aware resource scheduling algorithm for cloud storage

IOArbiter: Dynamic Provisioning of Backend Block Storage in the Cloud

Serifos: Workload Consolidation and Load Balancing for SSD Based Cloud Storage Systems

Contact Info

Product

Resources

About