Niclas Hedhman wrote:
On Tuesday 05 August 2008 01:48:13 Doug Cutting wrote:
-1. You get my +1 vote when the proposal text is part of the [VOTE] thread.
;-)
See below.
The wiki page has not been changed since the vote was called.
Doug
-----------------------------
= Tashi Proposal =
A proposal to the Apache Software Foundation Incubator PMC by
David O'Hallaron^*+^, Michael Kozuch^*^, Michael Ryan^*^, Steven
Schlosser^*^, Jim Cipar^+^, Greg Ganger^+^, Garth Gibson^+^, Julio
Lopez^+^, Michael Strouken^+^, Wittawat Tantisiriroj^+^, Doug
Cutting^#^, Jay Kistler^#^, Thomas Kwan^#^
^*^Intel Research Pittsburgh, ^+^Carnegie Mellon University, ^#^Yahoo!
July 10, 2008
== 1. Abstract ==
Tashi is a cluster management system for cloud computing on Big Data.
== 2. Proposal ==
The Tashi project aims to build a software infrastructure for cloud
computing on massive internet-scale datasets (what we call ''Big
Data''). The idea is to build a cluster management system that enables
the Big Data that are stored in a cluster/data center to be accessed,
shared, manipulated, and computed on by remote users in a convenient,
efficient, and safe manner. The system aims to provide the following
basic capabilities:
(a) ''On-demand provisioning of storage and compute resources.'' Users
request a number of compute nodes, which can be either virtual or
physical machines, and a set of disk images to boot up on the nodes. In
response they receive their own persistent logical cluster of compute
and storage nodes, which they can then manage and use.
(b) ''Extensible end-to-end system management.'' Tashi will define open
non-proprietary interfaces for management tasks such as observation,
inference, planning, and actuation. This will keep the system
vendor-neutral and allow different research and development groups to
plug in different implementations of different management modules.
(c) ''Cooperative storage and compute management.'' The system will
define new non-proprietary interfaces and methods that will allow
compute and storage management to work together in concert.
(d) ''Flexible storage models.'' The system will support a range of
different storage models, such as network-attached storage, per-node
storage, and hybrids, to allow developers, researchers, and large scale
cluster/data center operators to experiment with different kinds of file
systems.
(e) ''Flexible machine models.'' The system will support different
machine models. In particular, it will be VMM-agnostic, able to run
different virtual machine monitors such as KVM and Xen. Also, in order
to address the cluster squatting problem (when clusters are balkanized
by users who reserve and hold nodes for their exclusive use) the system
will support a novel bi-model booting capability, in which virtual
machine and physical machine instances can boot from the same disk image.
== 3. Rationale and Approach ==
Digital media, pervasive sensing, web authoring, mobile computing,
scientific and medical instruments, physical simulations, and virtual
worlds are all delivering vast new datasets relating to every aspect of
our lives. A growing fraction of this Big Data is going unused or being
underexploited due to the overwhelming scale of the data involved.
Effective sharing, understanding, and use of this new wealth of raw
information poses one of the great challenges for the new century.
In order to compute on this emerging Big Data, many research and
development groups are purchasing their own racks of compute and storage
servers. The goal of the Tashi project is to develop a layer of utility
software that turns these raw racks of servers into easily managed cloud
computers that will allow remote users to share and explore their Big Data.
To our knowledge there are no open source projects addressing cluster
management for Big Data applications. We need a project such as Tashi
for a number of reasons: (1) No cloud computing cluster management
systems have tackled the problem of having both compute and storage
management working together in concert, which we believe will be
necessary to support Big Data. (2) We need non-proprietary interfaces
for cloud computing, and open source is the way to develop these. For
example, Google's new App Engine and Amazon's web services require
people to build to proprietary API's, so that their applications are no
longer vendor neutral, but are tied to a particular service provider.
(3) We need an extensible system that can serve as a platform to
stimulate research in cluster management for cloud computing.
The Tashi system is targeted at two (not always distinct) communities:
(1) As a production system for organizations who want to offer medium to
large scale clusters to their users. For example, many companies and
university departments are purchasing such clusters, and a system like
Tashi would help them provide their users with access to the cycles and
storage in the clusters.
(2) As an extensible research platform for distributed systems researchers.
The approach for the project is to build on existing cluster management
work pioneered by projects such as Usher (UCSD), Cluster on Demand
(Duke), and EC2/S3 (Amazon), and then develop the new capabilities that
will be required to support Big Data cloud computing.
== 4. Need for a Community Effort ==
A number of events at Yahoo, Carnegie Mellon, and Intel Research
Pittsburgh motivated the development of Tashi and convinced us to work
together in the context of an open-source community:
(a) In 2006 the Parallel Data Lab (PDL) at Carnegie Mellon built a
cluster of 400 nodes from industry donations, with a goal of creating a
"Data Center Observatory" that would allow systems researchers to study
and monitor applications running on the cluster. This dream has been
slow to materialize because of the cost and complexity of supporting and
managing multiple applications and systems groups.
(b) In Fall 2007, Yahoo began offering access to their M45 research
cluster to researchers at Carnegie Mellon, and in order to support M45
as well as their own internal production clusters, began to develop some
cloud computing infrastructure on their own.
(c) In Fall 2007, Intel Research Pittsburgh purchased a moderate-sized
100-node cluster and made it available to applications groups at
Carnegie Mellon working on various Big Data applications such as
computational photography, machine translation, automatic speech
recognition, and event detection in spatio-temporal video streams.
Provisioning and scheduling the cluster in the face of so many different
application demands has proven to be difficult.
The difficulties of managing and provisioning these different clusters
convinced us that the problem was too big for any one of us to solve
completely on our own, and that we needed to band together create a
open-source community effort focused on developing a single software system.
Another important reason to develop an open-source community around
Tashi is that we need non-proprietary vendor-neutral APIs for the
emerging area of cloud computing, and open source is the best way to
achieve that.
== 5. Known Risks ==
''Commitment to future development.'' The risk of the developers
abandoning the project is small, mainly because they all own and manage
moderate to large scale clusters, and desperately need something like
Tashi to provision and manage those clusters. We also need a system like
Tashi to serve as an extensible platform for our research.
''Experience with open source.'' Yahoo has had a significant and
positive experience with the Apache Software Foundation (ASF) and
Hadoop. While Intel and Carnegie Mellon have developed some non-ASF
style open source projects in the past (e.g., Internet Suspend/Resume,
OpenDHT, and Open``Diamond), they have no experience with ASF-style open
source communities. However, they hope to benefit from Yahoo's
considerable experience in this area.
''Diversity of developer community.'' The initial code base for Tashi
was developed by a single research programmer, Michael Ryan, at Intel
Research Pittsburgh. An important reason for putting Tashi in the
incubator is to expand the set of developers to include programmers from
Carnegie Mellon and Yahoo, initially, and later, hopefully, from other
groups such as Usher at UCSD, Eucalyptus from UCSB, Cluster-on-Demand
from Duke University, and the RAD Lab at University of California, Berkeley.
''Relationship to other Apache projects.'' There are no Apache projects
such as Tashi that focus on systems support for cloud computing.
However, the Tashi project is closely related to Hadoop/HDFS. The
VM-based provisioning of Tashi will subsume the now
deprecated sub-clustering functionality of Hadoop-on-demand. The Tashi
prototype uses HDFS to host the cluster boot images. Also, we expect
that many Tashi logical clusters will run Hadoop jobs.
''Reasons that Tashi is an ASF project.'' There are three main reasons
for developing Tashi through Apache rather than, say, Source``Forge. (1)
Our Yahoo partner has had a very positive experience with the Hadoop
project. (2) We recognize the need to build a strong developer
community, and Apache is centered around building such communities. (3)
The ASF also offers substantial legal oversight that makes it attractive
for cross-organizational collaborative efforts such as Tashi. With
Source``Forge, for example, you have few guarantee about the title of
the code. Thus, people can easily post code they don't own, and/or
change the license terms of other open source code that they include in
their projects. So users of code from Source``Forge must be wary. On
the other hand, Apache vets all contributions, keeping signed documents
from every committer on file, etc.
== 6. Related Work ==
A small sampling of some closely related work:
[1] M. Mc``Nett, D. Gupta, A. Bahdat, G. Voelker, "Usher: An Extensible
Framework for Managing Clusters of Virtual Machines", Proceedings of the
21st Large Installation System Administration Conference (LISA 07), 2007.
[2] D. Irwin, J. Chase, L. Grit, A. Yumerefendi, D. Becker, "Sharing
Networked Resources with Brokered Leases", Usenix, 2006.
[3] J. Chase, D. Irwin, L. Grit, J. Moore, S. Sprenkle, "Dynamic Virtual
Clusters in a Grid Site Manager", HPDC, 2003.
[4] S. Garfinkel, "An Evaluation of Amazon's Grid Computing Services:
EC2, S3, and SQS", Tech Report TR-08-07, School for Engineering and
Applied Sciences, Harvard University, 2007.
[5] Red``Hat oVirt System, http://ovirt.org, 2008
[6] Eucalyptus, Rich Wolski, http://eucalyptus.cs.ucsb.edu
== 7. Source ==
We have working code, a pre-alpha proof-of-concept prototype that was
developed by Michael Ryan at Intel Research Pittsburgh. The prototype is
currently running on the 100-node cluster there. We will enter the
incubator with clean code, developed entirely by Michael Ryan, that is
unencumbered by any licensing issues.
== 8. Required Resources ==
(a) Proposed Mailing lists:
* tashi-private (with moderated subscriptions)
* tashi-dev
* tashi-commits
* tashi-user
(b) Subversion directory
* http://svn.apache.org/repos/asf/incubator/tashi
(c) Issue tracking:
* Tashi will use JIRA for bug tracking.
== 9. Initial Committers ==
Initially, there will be one committer each from Carnegie Mellon and
Intel Research:
* Michael Stroucken ([EMAIL PROTECTED])
* Michael Ryan ([EMAIL PROTECTED])
== 10. Sponsors ==
* ''Champion:'' Doug Cutting ([EMAIL PROTECTED])
* ''Nominated mentors:'' Matthieu Riou <[EMAIL PROTECTED]>
* ''Sponsoring entity:'' Apache Incubator PMC
= Tashi Proposal =
A proposal to the Apache Software Foundation Incubator PMC by
David O'Hallaron^*+^, Michael Kozuch^*^, Michael Ryan^*^, Steven
Schlosser^*^, Jim Cipar^+^, Greg Ganger^+^, Garth Gibson^+^, Julio
Lopez^+^, Michael Strouken^+^, Wittawat Tantisiriroj^+^, Doug
Cutting^#^, Jay Kistler^#^, Thomas Kwan^#^
^*^Intel Research Pittsburgh, ^+^Carnegie Mellon University, ^#^Yahoo!
July 10, 2008
== 1. Abstract ==
Tashi is a cluster management system for cloud computing on Big Data.
== 2. Proposal ==
The Tashi project aims to build a software infrastructure for cloud
computing on massive internet-scale datasets (what we call ''Big
Data''). The idea is to build a cluster management system that enables
the Big Data that are stored in a cluster/data center to be accessed,
shared, manipulated, and computed on by remote users in a convenient,
efficient, and safe manner. The system aims to provide the following
basic capabilities:
(a) ''On-demand provisioning of storage and compute resources.'' Users
request a number of compute nodes, which can be either virtual or
physical machines, and a set of disk images to boot up on the nodes. In
response they receive their own persistent logical cluster of compute
and storage nodes, which they can then manage and use.
(b) ''Extensible end-to-end system management.'' Tashi will define open
non-proprietary interfaces for management tasks such as observation,
inference, planning, and actuation. This will keep the system
vendor-neutral and allow different research and development groups to
plug in different implementations of different management modules.
(c) ''Cooperative storage and compute management.'' The system will
define new non-proprietary interfaces and methods that will allow
compute and storage management to work together in concert.
(d) ''Flexible storage models.'' The system will support a range of
different storage models, such as network-attached storage, per-node
storage, and hybrids, to allow developers, researchers, and large scale
cluster/data center operators to experiment with different kinds of file
systems.
(e) ''Flexible machine models.'' The system will support different
machine models. In particular, it will be VMM-agnostic, able to run
different virtual machine monitors such as KVM and Xen. Also, in order
to address the cluster squatting problem (when clusters are balkanized
by users who reserve and hold nodes for their exclusive use) the system
will support a novel bi-model booting capability, in which virtual
machine and physical machine instances can boot from the same disk image.
== 3. Rationale and Approach ==
Digital media, pervasive sensing, web authoring, mobile computing,
scientific and medical instruments, physical simulations, and virtual
worlds are all delivering vast new datasets relating to every aspect of
our lives. A growing fraction of this Big Data is going unused or being
underexploited due to the overwhelming scale of the data involved.
Effective sharing, understanding, and use of this new wealth of raw
information poses one of the great challenges for the new century.
In order to compute on this emerging Big Data, many research and
development groups are purchasing their own racks of compute and storage
servers. The goal of the Tashi project is to develop a layer of utility
software that turns these raw racks of servers into easily managed cloud
computers that will allow remote users to share and explore their Big Data.
To our knowledge there are no open source projects addressing cluster
management for Big Data applications. We need a project such as Tashi
for a number of reasons: (1) No cloud computing cluster management
systems have tackled the problem of having both compute and storage
management working together in concert, which we believe will be
necessary to support Big Data. (2) We need non-proprietary interfaces
for cloud computing, and open source is the way to develop these. For
example, Google's new App Engine and Amazon's web services require
people to build to proprietary API's, so that their applications are no
longer vendor neutral, but are tied to a particular service provider.
(3) We need an extensible system that can serve as a platform to
stimulate research in cluster management for cloud computing.
The Tashi system is targeted at two (not always distinct) communities:
(1) As a production system for organizations who want to offer medium to
large scale clusters to their users. For example, many companies and
university departments are purchasing such clusters, and a system like
Tashi would help them provide their users with access to the cycles and
storage in the clusters.
(2) As an extensible research platform for distributed systems researchers.
The approach for the project is to build on existing cluster management
work pioneered by projects such as Usher (UCSD), Cluster on Demand
(Duke), and EC2/S3 (Amazon), and then develop the new capabilities that
will be required to support Big Data cloud computing.
== 4. Need for a Community Effort ==
A number of events at Yahoo, Carnegie Mellon, and Intel Research
Pittsburgh motivated the development of Tashi and convinced us to work
together in the context of an open-source community:
(a) In 2006 the Parallel Data Lab (PDL) at Carnegie Mellon built a
cluster of 400 nodes from industry donations, with a goal of creating a
"Data Center Observatory" that would allow systems researchers to study
and monitor applications running on the cluster. This dream has been
slow to materialize because of the cost and complexity of supporting and
managing multiple applications and systems groups.
(b) In Fall 2007, Yahoo began offering access to their M45 research
cluster to researchers at Carnegie Mellon, and in order to support M45
as well as their own internal production clusters, began to develop some
cloud computing infrastructure on their own.
(c) In Fall 2007, Intel Research Pittsburgh purchased a moderate-sized
100-node cluster and made it available to applications groups at
Carnegie Mellon working on various Big Data applications such as
computational photography, machine translation, automatic speech
recognition, and event detection in spatio-temporal video streams.
Provisioning and scheduling the cluster in the face of so many different
application demands has proven to be difficult.
The difficulties of managing and provisioning these different clusters
convinced us that the problem was too big for any one of us to solve
completely on our own, and that we needed to band together create a
open-source community effort focused on developing a single software system.
Another important reason to develop an open-source community around
Tashi is that we need non-proprietary vendor-neutral APIs for the
emerging area of cloud computing, and open source is the best way to
achieve that.
== 5. Known Risks ==
''Commitment to future development.'' The risk of the developers
abandoning the project is small, mainly because they all own and manage
moderate to large scale clusters, and desperately need something like
Tashi to provision and manage those clusters. We also need a system like
Tashi to serve as an extensible platform for our research.
''Experience with open source.'' Yahoo has had a significant and
positive experience with the Apache Software Foundation (ASF) and
Hadoop. While Intel and Carnegie Mellon have developed some non-ASF
style open source projects in the past (e.g., Internet Suspend/Resume,
OpenDHT, and Open``Diamond), they have no experience with ASF-style open
source communities. However, they hope to benefit from Yahoo's
considerable experience in this area.
''Diversity of developer community.'' The initial code base for Tashi
was developed by a single research programmer, Michael Ryan, at Intel
Research Pittsburgh. An important reason for putting Tashi in the
incubator is to expand the set of developers to include programmers from
Carnegie Mellon and Yahoo, initially, and later, hopefully, from other
groups such as Usher at UCSD, Eucalyptus from UCSB, Cluster-on-Demand
from Duke University, and the RAD Lab at University of California, Berkeley.
''Relationship to other Apache projects.'' There are no Apache projects
such as Tashi that focus on systems support for cloud computing.
However, the Tashi project is closely related to Hadoop/HDFS. The
VM-based provisioning of Tashi will subsume the now
deprecated sub-clustering functionality of Hadoop-on-demand. The Tashi
prototype uses HDFS to host the cluster boot images. Also, we expect
that many Tashi logical clusters will run Hadoop jobs.
''Reasons that Tashi is an ASF project.'' There are three main reasons
for developing Tashi through Apache rather than, say, Source``Forge. (1)
Our Yahoo partner has had a very positive experience with the Hadoop
project. (2) We recognize the need to build a strong developer
community, and Apache is centered around building such communities. (3)
The ASF also offers substantial legal oversight that makes it attractive
for cross-organizational collaborative efforts such as Tashi. With
Source``Forge, for example, you have few guarantee about the title of
the code. Thus, people can easily post code they don't own, and/or
change the license terms of other open source code that they include in
their projects. So users of code from Source``Forge must be wary. On
the other hand, Apache vets all contributions, keeping signed documents
from every committer on file, etc.
== 6. Related Work ==
A small sampling of some closely related work:
[1] M. Mc``Nett, D. Gupta, A. Bahdat, G. Voelker, "Usher: An Extensible
Framework for Managing Clusters of Virtual Machines", Proceedings of the
21st Large Installation System Administration Conference (LISA 07), 2007.
[2] D. Irwin, J. Chase, L. Grit, A. Yumerefendi, D. Becker, "Sharing
Networked Resources with Brokered Leases", Usenix, 2006.
[3] J. Chase, D. Irwin, L. Grit, J. Moore, S. Sprenkle, "Dynamic Virtual
Clusters in a Grid Site Manager", HPDC, 2003.
[4] S. Garfinkel, "An Evaluation of Amazon's Grid Computing Services:
EC2, S3, and SQS", Tech Report TR-08-07, School for Engineering and
Applied Sciences, Harvard University, 2007.
[5] Red``Hat oVirt System, http://ovirt.org, 2008
[6] Eucalyptus, Rich Wolski, http://eucalyptus.cs.ucsb.edu
== 7. Source ==
We have working code, a pre-alpha proof-of-concept prototype that was
developed by Michael Ryan at Intel Research Pittsburgh. The prototype is
currently running on the 100-node cluster there. We will enter the
incubator with clean code, developed entirely by Michael Ryan, that is
unencumbered by any licensing issues.
== 8. Required Resources ==
(a) Proposed Mailing lists:
* tashi-private (with moderated subscriptions)
* tashi-dev
* tashi-commits
* tashi-user
(b) Subversion directory
* http://svn.apache.org/repos/asf/incubator/tashi
(c) Issue tracking:
* Tashi will use JIRA for bug tracking.
== 9. Initial Committers ==
Initially, there will be one committer each from Carnegie Mellon and
Intel Research:
* Michael Stroucken ([EMAIL PROTECTED])
* Michael Ryan ([EMAIL PROTECTED])
== 10. Sponsors ==
* ''Champion:'' Doug Cutting ([EMAIL PROTECTED])
* ''Nominated mentors:'' Matthieu Riou <[EMAIL PROTECTED]>
* ''Sponsoring entity:'' Apache Incubator PMC
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