Hello, We are about to get about 200 GB of illumina reads(43 bp) from 20 samples, two groups of 10 animals. We are hoping to use Galaxy on the Cloud to compare gene expression between the two groups. First of all, do you think this is possible with the current state of Galaxy Cloud development? Secondly, we are currently practicing with small drosophila datasets (4 sets of 2 GB each), and over the course of a few days of doing relatively little besides grooming and filtering the data, we had already been charged $60 by Amazon, which we thought was a bit inefficient. What is the best way to proceed working from one day to the next? Should one terminate the cluster at Cloud Console and then stop(pause) the cluster at the AWS console, and then restart the instance the next day? Does one have to reattach all of the EBS volumes before restarting the cluster? We were just terminating the instance and then bringing it back up and all the data was still there, ie it worked fine, but when we looked after a couple days there were 45 EBS volumes there - much of it was surely redundant as our data wasn¹t very large. Perhaps we need to take a snapshot and reboot the instance from this? Thank you for any hints regarding this matter, this is all very new to me. Let me know if you need clarification or more information. David Martin dmarti@lsuhsc.edu
I'd be interested in why AWS is so expensive for these datasets: Is it mostly a) the data transfer between nodes? b) the data storage on EBS? c) the CPU time ? why next-gen analysis is expensive on the cloud? Can anyone who is actively using AWS look up the distribution of the total cost on the individual types? I guess that there is a lot of room for improvement for the different costs, depending on the type of algorithm that you're using. thanks in advance Max On Tue, Nov 23, 2010 at 8:17 PM, David Martin <dmarti@lsuhsc.edu> wrote:
Hello,
We are about to get about 200 GB of illumina reads(43 bp) from 20 samples, two groups of 10 animals. We are hoping to use Galaxy on the Cloud to compare gene expression between the two groups. First of all, do you think this is possible with the current state of Galaxy Cloud development? Secondly, we are currently practicing with small drosophila datasets (4 sets of 2 GB each), and over the course of a few days of doing relatively little besides grooming and filtering the data, we had already been charged $60 by Amazon, which we thought was a bit inefficient. What is the best way to proceed working from one day to the next? Should one terminate the cluster at Cloud Console and then stop(pause) the cluster at the AWS console, and then restart the instance the next day? Does one have to reattach all of the EBS volumes before restarting the cluster? We were just terminating the instance and then bringing it back up and all the data was still there, ie it worked fine, but when we looked after a couple days there were 45 EBS volumes there - much of it was surely redundant as our data wasn’t very large. Perhaps we need to take a snapshot and reboot the instance from this? Thank you for any hints regarding this matter, this is all very new to me. Let me know if you need clarification or more information.
David Martin dmarti@lsuhsc.edu
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It's just that computing, and cloud computing with that, is expensive. Depending on the usage, either the EBS volumes or the CPU time (i.e., instances) is what will represent majority of the cost. Most likely, it will be the instances, unless you use very few instances for a short period and a lot of storage. There are a couple of papers I can recall analyzing the cost of science in the cloud, if you want to take a look: - Deelman E, Singh G, Livny M, Berriman B, Good J: The cost of doing science on the cloud: the Montage example - Wilkening J, Wilke A, Desai N, Meyer F: Using Clouds for Metagenomics: A Case Study Enis On Tue, Nov 23, 2010 at 3:43 PM, Maximilian Haussler <maximilianh@gmail.com>wrote:
I'd be interested in why AWS is so expensive for these datasets:
Is it mostly a) the data transfer between nodes? b) the data storage on EBS? c) the CPU time ? why next-gen analysis is expensive on the cloud?
Can anyone who is actively using AWS look up the distribution of the total cost on the individual types?
I guess that there is a lot of room for improvement for the different costs, depending on the type of algorithm that you're using.
thanks in advance Max
On Tue, Nov 23, 2010 at 8:17 PM, David Martin <dmarti@lsuhsc.edu> wrote:
Hello,
We are about to get about 200 GB of illumina reads(43 bp) from 20 samples, two groups of 10 animals. We are hoping to use Galaxy on the Cloud to compare gene expression between the two groups. First of all, do you think this is possible with the current state of Galaxy Cloud development? Secondly, we are currently practicing with small drosophila datasets (4 sets of 2 GB each), and over the course of a few days of doing relatively little besides grooming and filtering the data, we had already been charged $60 by Amazon, which we thought was a bit inefficient. What is the best way to proceed working from one day to the next? Should one terminate the cluster at Cloud Console and then stop(pause) the cluster at the AWS console, and then restart the instance the next day? Does one have to reattach all of the EBS volumes before restarting the cluster? We were just terminating the instance and then bringing it back up and all the data was still there, ie it worked fine, but when we looked after a couple days there were 45 EBS volumes there - much of it was surely redundant as our data wasn’t very large. Perhaps we need to take a snapshot and reboot the instance from this? Thank you for any hints regarding this matter, this is all very new to me. Let me know if you need clarification or more information.
David Martin dmarti@lsuhsc.edu
_______________________________________________ galaxy-user mailing list galaxy-user@lists.bx.psu.edu http://lists.bx.psu.edu/listinfo/galaxy-user
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OK OK, cloud computing is expensive. But I also know from my own experience that you can cut I/O by a factor of 10-20 and CPU by a factor of ten as well: - use bowtie for mapping (but index is quite big): saves a lot of CPU - compress input fastq files (reduces size to 1/5) and read only compressed files - extreme solution: strip all quality values from fastq (reduces size to 1/4) - remove all file-concatenation steps - pipe into samtools to convert to bam immediately after mapping, always save in bam format - strip all unmapped reads directly with samtools -F4 but I wonder how much that would save in the end...? cheers Max -- Maximilian Haussler Tel: +447574246789 http://www.manchester.ac.uk/research/maximilian.haussler/ On Tue, Nov 23, 2010 at 9:02 PM, Enis Afgan <eafgan@emory.edu> wrote:
It's just that computing, and cloud computing with that, is expensive. Depending on the usage, either the EBS volumes or the CPU time (i.e., instances) is what will represent majority of the cost. Most likely, it will be the instances, unless you use very few instances for a short period and a lot of storage.
There are a couple of papers I can recall analyzing the cost of science in the cloud, if you want to take a look: - Deelman E, Singh G, Livny M, Berriman B, Good J: The cost of doing science on the cloud: the Montage example - Wilkening J, Wilke A, Desai N, Meyer F: Using Clouds for Metagenomics: A Case Study
Enis
On Tue, Nov 23, 2010 at 3:43 PM, Maximilian Haussler < maximilianh@gmail.com> wrote:
I'd be interested in why AWS is so expensive for these datasets:
Is it mostly a) the data transfer between nodes? b) the data storage on EBS? c) the CPU time ? why next-gen analysis is expensive on the cloud?
Can anyone who is actively using AWS look up the distribution of the total cost on the individual types?
I guess that there is a lot of room for improvement for the different costs, depending on the type of algorithm that you're using.
thanks in advance Max
On Tue, Nov 23, 2010 at 8:17 PM, David Martin <dmarti@lsuhsc.edu> wrote:
Hello,
We are about to get about 200 GB of illumina reads(43 bp) from 20 samples, two groups of 10 animals. We are hoping to use Galaxy on the Cloud to compare gene expression between the two groups. First of all, do you think this is possible with the current state of Galaxy Cloud development? Secondly, we are currently practicing with small drosophila datasets (4 sets of 2 GB each), and over the course of a few days of doing relatively little besides grooming and filtering the data, we had already been charged $60 by Amazon, which we thought was a bit inefficient. What is the best way to proceed working from one day to the next? Should one terminate the cluster at Cloud Console and then stop(pause) the cluster at the AWS console, and then restart the instance the next day? Does one have to reattach all of the EBS volumes before restarting the cluster? We were just terminating the instance and then bringing it back up and all the data was still there, ie it worked fine, but when we looked after a couple days there were 45 EBS volumes there - much of it was surely redundant as our data wasn’t very large. Perhaps we need to take a snapshot and reboot the instance from this? Thank you for any hints regarding this matter, this is all very new to me. Let me know if you need clarification or more information.
David Martin dmarti@lsuhsc.edu
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It costs about $500 per month to run a single AMI instance with several CPUs --Hiram
David: For a pilot I would just use our public instance at http://usegalaxy.org to polish up the exact workflow and settings that would give you satisfactory results on a subset of data. This way it would be much easier to figure out where you can "cut-corners" for performance. You will then have a "best-parctise" workflow that you'll be able to rerun on the cloud. Use the new ftp-based upload to get datasets into Galaxy. Thanks! anton On Nov 23, 2010, at 3:17 PM, David Martin wrote:
Hello,
We are about to get about 200 GB of illumina reads(43 bp) from 20 samples, two groups of 10 animals. We are hoping to use Galaxy on the Cloud to compare gene expression between the two groups. First of all, do you think this is possible with the current state of Galaxy Cloud development? Secondly, we are currently practicing with small drosophila datasets (4 sets of 2 GB each), and over the course of a few days of doing relatively little besides grooming and filtering the data, we had already been charged $60 by Amazon, which we thought was a bit inefficient. What is the best way to proceed working from one day to the next? Should one terminate the cluster at Cloud Console and then stop(pause) the cluster at the AWS console, and then restart the instance the next day? Does one have to reattach all of the EBS volumes before restarting the cluster? We were just terminating the instance and then bringing it back up and all the data was still there, ie it worked fine, but when we looked after a couple days there were 45 EBS volumes there - much of it was surely redundant as our data wasn’t very large. Perhaps we need to take a snapshot and reboot the instance from this? Thank you for any hints regarding this matter, this is all very new to me. Let me know if you need clarification or more information.
David Martin dmarti@lsuhsc.edu _______________________________________________ galaxy-user mailing list galaxy-user@lists.bx.psu.edu http://lists.bx.psu.edu/listinfo/galaxy-user
Anton Nekrutenko http://nekrut.bx.psu.edu http://usegalaxy.org
Your approach for terminating a cluster and starting it back up when it's needed should continue to be fine for your purposes. That's the best and pretty much the only way to minimize the cost. The reason there are 45 EBS volumes created is because each time you start an instance, a root EBS volume from snapshot 'snap-f3a64f99' is created to serve as the root file system. When you terminate that particular instance, that EBS volume is no longer needed and can be deleted (in the next AMI we build, we will enable deletion of that volume automatically upon instance termination). In other words, feel free to delete all EBS volumes that were created from a snapshot; they can be and are recreated when needed. The only volume that should not be deleted is your data volume. The ID of this volume can be found in your cluster's bucket (cm-<HASH>) in your S3 account in file named persistent_data.txt As a reference, don't attach/detach EBS volumes manually to running Galaxy Cloud instances because the application will lose track of them and not be able to recover. In addition, always click 'Terminate cluster' on the Galaxy Cloud main UI and wait for it to shutdown all of he services; then *terminate* the master instance from AWS console (don't *stop* the instance). As far as uploading 200GB of data to a cloud instance and processing it there. In principle, it should work. However, there is a 1TB limit on EBS volumes imposed by Amazon. As a result, and considering the multiple transformation steps your data will have to go through within Galaxy, I am concerned that you will reach that 1TB limit. We will be working on expanding beyond that limit by composing a filesystem from multiple EBS volumes but that's not available yet. Hope this helps; let us know if you have any more questions, Enis On Tue, Nov 23, 2010 at 3:17 PM, David Martin <dmarti@lsuhsc.edu> wrote:
Hello,
We are about to get about 200 GB of illumina reads(43 bp) from 20 samples, two groups of 10 animals. We are hoping to use Galaxy on the Cloud to compare gene expression between the two groups. First of all, do you think this is possible with the current state of Galaxy Cloud development? Secondly, we are currently practicing with small drosophila datasets (4 sets of 2 GB each), and over the course of a few days of doing relatively little besides grooming and filtering the data, we had already been charged $60 by Amazon, which we thought was a bit inefficient. What is the best way to proceed working from one day to the next? Should one terminate the cluster at Cloud Console and then stop(pause) the cluster at the AWS console, and then restart the instance the next day? Does one have to reattach all of the EBS volumes before restarting the cluster? We were just terminating the instance and then bringing it back up and all the data was still there, ie it worked fine, but when we looked after a couple days there were 45 EBS volumes there - much of it was surely redundant as our data wasn’t very large. Perhaps we need to take a snapshot and reboot the instance from this? Thank you for any hints regarding this matter, this is all very new to me. Let me know if you need clarification or more information.
David Martin dmarti@lsuhsc.edu
_______________________________________________ galaxy-user mailing list galaxy-user@lists.bx.psu.edu http://lists.bx.psu.edu/listinfo/galaxy-user
participants (5)
-
Anton Nekrutenko -
David Martin -
Enis Afgan -
Hiram Clawson -
Maximilian Haussler