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Tactic: Traffic Aware Cloud for Tier...

Sangpetch, Akkarit.

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Tactic: Traffic Aware Cloud for Tiered Infrastructure Consolidation.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Tactic: Traffic Aware Cloud for Tiered Infrastructure Consolidation./
Author:	Sangpetch, Akkarit.
Description:	151 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-08(E), Section: B.
Contained By:	Dissertation Abstracts International74-08B(E).
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3538969
ISBN:	9781303024214

Tactic: Traffic Aware Cloud for Tiered Infrastructure Consolidation.
Sangpetch, Akkarit.

Tactic: Traffic Aware Cloud for Tiered Infrastructure Consolidation. - 151 p.

Source: Dissertation Abstracts International, Volume: 74-08(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2013.

Large-scale enterprise applications are deployed as distributed applications. These applications consist of many inter-connected components with heterogeneous roles and complex dependencies. Each component typically consumes 5-15% of the server capacity. Deploying each component as a separate virtual machine (VM) allows us to consolidate the low-utilized VMs into the same infrastructure, enhancing the physical resource utilization. The application performance is often specified by the service level agreement (SLA) in terms of the 95th percentile response time.

ISBN: 9781303024214Subjects--Topical Terms:

1669061
Engineering, Computer.

Tactic: Traffic Aware Cloud for Tiered Infrastructure Consolidation.
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100 1 $a Sangpetch, Akkarit. $3 2094138
245 1 0 $a Tactic: Traffic Aware Cloud for Tiered Infrastructure Consolidation.
300 $a 151 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-08(E), Section: B.
500 $a Adviser: Hyong S. Kim.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2013.
520 $a Large-scale enterprise applications are deployed as distributed applications. These applications consist of many inter-connected components with heterogeneous roles and complex dependencies. Each component typically consumes 5-15% of the server capacity. Deploying each component as a separate virtual machine (VM) allows us to consolidate the low-utilized VMs into the same infrastructure, enhancing the physical resource utilization. The application performance is often specified by the service level agreement (SLA) in terms of the 95th percentile response time.
520 $a We propose Tactic, an automated application performance management system to find a VM placement that satisfies the SLA. Conceptually, Tactic finds a placement that provides sufficient resource capacity as well as reduce the resource contention due to requests arriving concurrently. We estimate the probability that the VMs receive the requests simultaneously in order to quantify the amount of resource contention and approximate the achievable response time of each VM. To estimate the overall application response time, we combine the response time of the individual VMs based on their interactions.
520 $a We automatically derive the interactions among the VMs based on their dependencies. The execution order of the dependent VMs is also derived to estimate the response time of each VM. We introduce the basic dependency primitives to represent both dependencies and the execution order among the VMs. We use the primitives to construct the dependency model which includes both the relationship of the VMs and the execution order needed for predicting the overall application response time. If the predicted response time does not meet the SLA, we need to find another placement candidate.
520 $a We propose a contention-aware placement algorithm that reduces the resource contention for the high-impact VMs. The impact is measured by the ratio of the change in the application response time to the change in the individual VM's response time. The algorithm identifies the VM with the highest impact using the dependency model. Compared to the random placement approach, our contention-aware approach can reduce the number of iterations required to find the placement that satisfies the SLA. With Tactic, we can automatically extract the dependencies from a complex application. We use the request traffic to infer the dependency model for the response time prediction. Tactic satisfies the 95th percentile response time.
520 $a We evaluate Tactic in realistic deployment scenarios using Drupal content management system and MongoDB distributed database. The results show that Tactic can accurately predict the 95 th percentile response time with less than 8% error for both applications. Tactic can also identify a placement with up 35% lower overall response time on Drupal, compared to a utilization-based approach.
590 $a School code: 0041.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Computer Science. $3 626642
650 4 $a Information Technology. $3 1030799
690 $a 0464
690 $a 0984
690 $a 0489
710 2 $a Carnegie Mellon University. $b Electrical and Computer Engineering. $3 2094139
773 0 $t Dissertation Abstracts International $g 74-08B(E).
790 $a 0041
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3538969