Dynamic Scheduling with Service Curve for QoS Guarantee of Large-Scale Cloud Storage

Wei, Qingsong; Chen, Cheng; Xue, Minhui; Yuan, Xinkun; Wang, Chundong

doi:10.1109/tc.2017.2773511

Cited by 16 publications

(9 citation statements)

References 22 publications

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“…Because the tasks are optimally assigned to appropriate services, the service efficiency is improved to achieve a shorter computation time for workloads. Zhang et al [22] propose a service curve-based QoS algorithm to support three types of applications in a same storage system. Specifically, three scheduling queues with different priorities are adopted to schedule the I/O requests respectively in latency guarantee applications, IOPS guarantee applications and best-effort applications.…”

Section: B the Second Categorymentioning

confidence: 99%

Leveraging N-1 Queues to Improve the Energy Efficiency of Scalable Computing

Luo

Tang

2020

IEEE Access

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The clusters in a blockchain computing system can be constructed to be elastic, thus supporting scalable computing and improving energy efficiency. To form an elastic cluster, the service nodes are dynamically divided into the working nodes and the reserved nodes. Specifically, the working nodes are active to meet the computing requirements of workloads, while the reserved nodes are switched to a low-power state for energy saving. Traditionally, workloads are distributed to working nodes in the mode of N-N service queues. But in this mode, the Quality of Service (QoS) of different working nodes may be diverse, because the requirements are various for the accumulated requests in different working nodes. As a result, the overall system capability is not sufficiently utilized, and the overall system QoS is dragged down. In this paper, we propose an N-1 queueing and on-demand resource provisioning method to process workloads in the mode of N-1 service queues. Different from N-N service queues, N-1 service queues prohibit the accumulation of requests in working nodes. Thereby, once there are idle working nodes, waiting requests can immediately be delivered to them. As a result, all the working nodes are sufficiently utilized, and the overall QoS is improved. Accordingly, after using the N-1 service queues, fewer working nodes are enough to meet the same Service Level Agreement (SLA) on same workloads. In addition, by using a resource demand monitor module, our method dynamically readjusts the number of working nodes to match workload demand. Finally, the energy efficiency of an elastic cluster can be measurably improved, due to that fewer working nodes are powered on while the same SLA can be met. INDEX TERMS Blockchain computing systems, energy efficiency improvement, service queues, scalable computing.

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Section: B the Second Categorymentioning

confidence: 99%

Leveraging N-1 Queues to Improve the Energy Efficiency of Scalable Computing

Luo

Tang

2020

IEEE Access

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“…Software optimization methods and heuristic analysis are used to enhance the performance of SDN controller and proper architecture framework helps in reducing the request frequency. Cloud computing and network function virtualization and explosion of connected devices raised the traffic exponentially and instability in usage patterns (Lai et al, 2015;Zhang et al, 2018). For this agile architecture is mandatory to support dynamic reconfiguration of network infrastructure as well as services to support.…”

Section: State Of Artmentioning

confidence: 99%

Self-Optimized and Traffic Control With Link Prediction Heuristic for Hyper-Connected Network

Seth¹,

Bisht²,

Vijay³

2020

Contemporary Issues in Computing

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“…However, the SDFS can get the exact status of the underlying resource usage. Some other approaches cooperate with the storage servers to meet isolation goals, but they cannot deal with the implications of the file system. Thus, these approaches cannot address issues caused by the file system, such as the uncontrollability of delayed write‐back and the write amplification of journaling.…”

Section: Related Workmentioning

confidence: 99%

SDFS: A software‐defined file system for multitenant cloud storage

et al. 2018

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Performance isolation is highly desirable in cloud platforms where the virtual disks of virtual machines are simply large files on the shared and networked storage servers. However, existing isolation techniques cannot deal with the implications of the file system used by the networked storage servers, such that underlying resource usage is unpredictable (eg, the delayed write-back mechanism could postpone writes, and the journaling mechanism could amplify writes). The lack of visibility on underlying resource usage leads to the predicament of being unable to meet isolation goals. In this paper, we present a software-defined file system (SDFS) that exploits the underlying file system to allocate resources at per-image-file granularity and provide tenants with guaranteed throughput. The SDFS comprises two components: control plane and data plane. At the control plane, we provide a set of system calls to document tenant performance requirements into the metadata of image files. At the data plane, we construct a file-based scheduler to manage memory and disk resources according to the tenant performance requirements. The SDFS design does not require a modification to guest operating systems, hypervisors, or file server protocols. Through a prototype implementation, we demonstrate that the SDFS can meet isolation goals and increase resource utilization with negligible overhead.

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Dynamic Scheduling with Service Curve for QoS Guarantee of Large-Scale Cloud Storage

Cited by 16 publications

References 22 publications

Leveraging N-1 Queues to Improve the Energy Efficiency of Scalable Computing

Leveraging N-1 Queues to Improve the Energy Efficiency of Scalable Computing

Self-Optimized and Traffic Control With Link Prediction Heuristic for Hyper-Connected Network

SDFS: A software‐defined file system for multitenant cloud storage

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