Investigating the Effect of Garbage Collection on Service Level Objectives of Clouds

2017 ◽  
Author(s):  
Panagiotis Patros ◽  
Kenneth B. Kent ◽  
Michael Dawson
Keyword(s):  
Garbage Collection ◽  
Service Level ◽  
Service Level Objectives

Why is garbage collection causing my service level objectives to fail?

2018 ◽  
Vol 7 (3/4) ◽  
pp. 282 ◽  
Author(s):  
Panagiotis Patros ◽  
Kenneth B. Kent ◽  
Michael Dawson
Keyword(s):  
Garbage Collection ◽  
Service Level ◽  
Service Level Objectives

Why is garbage collection causing my service level objectives to fail?

2018 ◽  
Vol 7 (3/4) ◽  
pp. 282
Author(s):  
Panagiotis Patros ◽  
Michael Dawson ◽  
Kenneth B. Kent
Keyword(s):  
Garbage Collection ◽  
Service Level ◽  
Service Level Objectives

Exploiting Write Heterogeneity of Morphable MLC/SLC SSDs in Datacenters with Service-Level Objectives

2017 ◽  
Vol 66 (8) ◽  
pp. 1457-1463 ◽  
Author(s):  
Che-Wei Chang ◽  
Geng-You Chen ◽  
Yi-Jung Chen ◽  
Chia-Wei Yeh ◽  
Pei Yin Eng ◽  
...  
Keyword(s):  
Service Level ◽  
Service Level Objectives

Characterizing Service Level Objectives for Cloud Services: Realities and Myths

2019 ◽  
Author(s):  
Jianru Ding ◽  
Ruiqi Cao ◽  
Indrajeet Saravanan ◽  
Nathaniel Morris ◽  
Christopher Stewart
Keyword(s):  
Service Level ◽  
Cloud Services ◽  
Service Level Objectives

Auction-based cloud service differentiation with service level objectives

2016 ◽  
Vol 94 ◽  
pp. 231-249 ◽  
Author(s):  
Jianbing Ding ◽  
Zhenjie Zhang ◽  
Richard T. B. Ma ◽  
Yin Yang
Keyword(s):  
Cloud Service ◽  
Service Level ◽  
Service Differentiation ◽  
Service Level Objectives

scholarly journals SSD Bandwidth Distributing I/O Scheduler Considering Garbage Collection

2018 ◽  
Vol 12 (1) ◽  
pp. 1-6
Author(s):  
Jung Kyu Park ◽  
Jaeho Kim
Keyword(s):  
Quality Of Service ◽  
Low Power ◽  
Garbage Collection ◽  
Service Level Agreement ◽  
Service Level ◽  
Storage Device ◽  
Hard Disks ◽  
Improve Performance ◽  
Simulation Based

There were scheduler studies for QoS(Quality of Service) or SLA(Service Level Agreement) of hard disks. The use of SSDs as storage has been increasing dramatically in recent systems due to their fast performance and low power usage. However, the studies to guarantee the SLA are based on the hard disk and do not consider SSD which is a ash storage device. In the SSD, GC(Garbae Collection) process copies data to an empty block and the corresponding block is removed by the GC. This causes SSD performance to degrade in a virtualized environment with many I/Os. We considered the Linux scheduler to take SSD characteristics into consideration and to improve I/O performance. In this paper, we propose a MTS-CFQ I/O scheduler that is implemented by modifying the existing Linux CFQ I/O scheduler. Our proposed method controls the time slice based on the I/O bandwidth for the current storage device. Real workload-driven simulation based experimental results have shown that MTS-CFQ can improve performance by up to 20% with an average of 5%, compared with the traditional CFQ I/O for the four workload considered.  

scholarly journals Optimal Decomposition of Service Level Objectives into Policy Assertions

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Yousef Rastegari ◽  
Fereidoon Shams
Keyword(s):  
Customer Satisfaction ◽  
Web Services ◽  
Decomposition Method ◽  
Performance Indicator ◽  
Service Level ◽  
Quality Aspects ◽  
Run Time ◽  
Service Level Objectives ◽  
Securities Settlement

WS-agreement specifies quality objectives that each partner is obligated to provide. To meet quality objectives, the corresponding partner should apply appropriate policy assertions to its web services and adjust their parameters accordingly. Transformation of WS-CDL to WSBPEL is addressed in some related works, but neither of them considers quality aspects of transformation nor run-time adaptation. Here, in conformance with web services standards, we propose an optimal decomposition method to make a set of WS-policy assertions. Assertions can be applied to WSBPEL elements and affect their run-time behaviors. The decomposition method achieves the best outcome for a performance indicator. It also guarantees the lowest adaptation overhead by reducing the number of service reselections. We considered securities settlement case study to prototype and evaluate the decomposition method. The results show an acceptable threshold between customer satisfaction—the targeted performance indicator in our case study—and adaptation overhead.

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