Private:progress-sharangi - Revision history

Ssa121: /* [Feb 7] 3D FVV over DASH */

2013-02-08T03:29:51Z

[Feb 7] 3D FVV over DASH

Ssa121: /* [Feb 7] 3D FVV over DASH */

2013-02-08T03:29:34Z

[Feb 7] 3D FVV over DASH

Ssa121: /* Fall 2012 (RA) */

2013-02-08T03:28:47Z

Fall 2012 (RA)

Ssa121: /* [Nov20] Cloud rewrite project */

2012-11-20T23:47:25Z

[Nov20] Cloud rewrite project

Ssa121: /* [Nov20] Cloud rewrite project */

2012-11-20T23:47:08Z

[Nov20] Cloud rewrite project

Ssa121: /* [Oct29] Adaptable Video Caching */

2012-11-20T23:45:20Z

[Oct29] Adaptable Video Caching

Ssa121: /* [Oct12] Adaptable Video Caching */

2012-10-29T20:19:33Z

[Oct12] Adaptable Video Caching

Ssa121 at 23:15, 11 October 2012

2012-10-11T23:15:40Z

Ssa121: /* Fall 2012 (RA) */

2012-09-27T18:34:51Z

Fall 2012 (RA)

Ssa121: /* Fall 2012 (RA) */

2012-09-27T18:34:26Z

Fall 2012 (RA)

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 = Spring 2013 (RA) =
 === [Feb 7] 3D FVV over DASH ===
-* Worked with Ahmed on surveying open source DASH tools and 3D video player tools for implementing the feature. Details are updated int he wiki page [http://nsl.cs.sfu.ca/wiki/index.php/Private:FTV here].
+* Worked with Ahmed on surveying open source DASH tools and 3D video player tools for implementing the feature. Details are updated in the wiki page [http://nsl.cs.sfu.ca/wiki/index.php/Private:FTV here].
 = Fall 2012 (RA) =

@@ Line 1: / Line 1: @@
 = Spring 2013 (RA) =
 === [Feb 7] 3D FVV over DASH ===
-* Worked with Ahmed on surveying open source DASH tools and 3D video player tools for implementing the feature. Details are updated int he wiki page here.
+* Worked with Ahmed on surveying open source DASH tools and 3D video player tools for implementing the feature. Details are updated int he wiki page [http://nsl.cs.sfu.ca/wiki/index.php/Private:FTV here].
 = Fall 2012 (RA) =
 === [Nov20] Cloud rewrite project ===

← Older revision		Revision as of 03:28, 8 February 2013
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		+	= Spring 2013 (RA) =
		+	=== [Feb 7] 3D FVV over DASH ===
		+	* Worked with Ahmed on surveying open source DASH tools and 3D video player tools for implementing the feature. Details are updated int he wiki page here.
	= Fall 2012 (RA) =		= Fall 2012 (RA) =
	=== [Nov20] Cloud rewrite project ===		=== [Nov20] Cloud rewrite project ===

@@ Line 1: / Line 1: @@
 = Fall 2012 (RA) =
 === [Nov20] Cloud rewrite project ===
-* Worked on addressing the MMSys review comments for the project and submitted the updated report to ICDCS'13. Report also available in [https://cs-nsl-svn.cs.surrey.sfu.ca/nsl-projects/browser/Cloud/avcRewriter/documents/techReps/icdcs13/ svn].
+* Worked on addressing the MMSys review comments for the project and submitted the updated report to ICDCS'13. Report also available in svn [https://cs-nsl-svn.cs.surrey.sfu.ca/nsl-projects/browser/Cloud/avcRewriter/documents/techReps/icdcs13/ here].
 === [Oct29] Adaptable Video Caching ===

@@ Line 1: / Line 1: @@
 = Fall 2012 (RA) =
 === [Nov20] Cloud rewrite project ===
-* Worked on addressing the MMSys review comments for the project and submitted the updated report to ICDCS'13. Report avilable.
+* Worked on addressing the MMSys review comments for the project and submitted the updated report to ICDCS'13. Report also available in [https://cs-nsl-svn.cs.surrey.sfu.ca/nsl-projects/browser/Cloud/avcRewriter/documents/techReps/icdcs13/ svn].
 === [Oct29] Adaptable Video Caching ===

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 = Fall 2012 (RA) =
 === [Oct12] Adaptable Video Caching ===
 * I investigated caching policies for the general problem of adaptable and segmented media content caching at edge-proxies. The term adaptable refers to content that are adapted to user’s requirement at the edge location. Adaptation can be in terms of codec specific operations like H.264 rewriting, more generic transcoding or even video scaling/retargeting. Caching a segment of video makes more sense as entire video files are huge and more often only a part of it is viewed. There have been many work on the segmented video caching but it is more relevant now as DASH becomes the de-facto streaming protocol. Our particular caching scenario is different than previous caching problems because it needs to consider four characteristics of the system: miss-penalty, segment- size, segment-class(SVC or AVC) and temporal correlation between segments. The metrics of performance are byte-hit-rate, cost and user-perceived latency. As LRU and LFU are the most popular caching policies we should start by investigating how these two policies perform in our caching scenario, particularly how the aforementioned four parameters affect the performance of the two policies. Then we can further investigate to design our own algorithm that improves performance by giving consideration to all  the four requirements.
@@ Line 5: / Line 7: @@
 * I came across several papers referring to MediSyn from HP Labs but I don’t seem to find a download link for the same. I am avoiding use of general web access generators like ProWGen. The best will be if we can get some traces of our own by tracking Youtube usage on campus but I don’t know whether this is feasible. I am also trying to improve my simulator to accommodate this but validation through an external system/trace will be better.
 === [Sep27] Proposed extensions to cloud-rewrite project: ===
 * Model the relationship between the GoP video parameters and VM capability information to accurately estimate the processing times. Currently this is being done in an ad-hoc manner. Maybe we can apply machine learning/ neural networks framework for this. The training phase can be done off-line. We note that this is different that computing the GoP-to-VM assignment. However, we can apply machine learning framework for finding the assignment of VMs too.

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 = Fall 2012 (RA) =
-=== Research ===
+=== [Sep27] Proposed extensions to cloud-rewrite project:
-*Sep27 : Cloud-rewrite project, proposed extensions:
+* Model the relationship between the GoP video parameters and VM capability information to accurately estimate the processing times. Currently this is being done in an ad-hoc manner. Maybe we can apply machine learning/ neural networks framework for this. The training phase can be done off-line. We note that this is different that computing the GoP-to-VM assignment. However, we can apply machine learning framework for finding the assignment of VMs too.
-** Derive the full mathematical model for the caching cost. This model currently uses simulation to find the cache replacement probability values. We should try to derive them analytically. One challenge is that the file sizes of media chunks are going to be different which is much harder to model than if we assume them to be same. Need to look into cache-algorithm literature.
+* Derive the full mathematical model for the caching cost. This model currently uses simulation to find the cache replacement probability values. We should try to derive them analytically. One challenge is that the file sizes of media chunks are going to be different which is much harder to model than if we assume them to be same. Need to look into cache-algorithm literature.
-** We did not conduct any experiments to measure the latency performance of our solution. This is one area that we can look at. But it will require a more elaborate experimental setup. Better if we perform our experiments with VoD access traces than simulation.
+* We did not conduct any experiments to measure the latency performance of our solution. This is one area that we can look at. But it will require a more elaborate experimental setup. Better if we perform our experiments with VoD access traces than simulation.
 = Summer 2012 (RA) =