Name
Abstract | ||
|---|---|---|
The growing popularity of hosted storage services and shared storage infrastructure in data centers is driving the recent interest in resource management and QoS in storage systems. The bursty nature of storage workloads raises significant performance and provisioning challenges, leading to increased resource requirements, management costs, and energy consumption. We present a novel workload shaping framework to handle bursty workloads, where the arrival stream is dynamically decomposed to isolate its bursts, and then rescheduled to exploit available slack. We show how decomposition reduces the server capacity requirements and power consumption significantly, while affecting QoS guarantees minimally. We present an optimal decomposition algorithm RTT and a recombination algorithm Miser, and show the benefits of the approach by evaluating the performance of several storage workloads using both simulation and Linux implementation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TPDS.2010.129 | IEEE Trans. Parallel Distrib. Syst. |
Keywords | Field | DocType |
workload decomposition,storage resource management,server capacity requirements,scheduling,workload bursts decomposition,power consumption,linux implementation,bursty nature,quality of service,efficient storage resource management,storage management,decomposing workload bursts,graduated qos,bursty workloads,increased resource requirement,resource management,linux,qos,management cost,storage service,energy consumption,scheduling.,shared storage infrastructure,recombination algorithm miser,storage system,workload shaping framework,optimal decomposition algorithm rtt,storage workloads,algorithm design and analysis,requirements management,resource manager,energy storage,data center,energy management,algorithm design,power system | Resource management,Energy management,Converged storage,Scheduling (computing),Computer science,Computer data storage,Quality of service,Computer network,Provisioning,Real-time computing,Energy consumption,Distributed computing | Journal |
Volume | Issue | ISSN |
22 | 5 | 1045-9219 |
Citations | PageRank | References |
4 | 0.43 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lanyue Lu | 1 | 101 | 5.67 |
| Peter J. Varman | 2 | 700 | 83.23 |
| Kshitij Doshi | 3 | 89 | 10.76 |