Posts Tagged Sector/Sphere
Open Source Cloud Computing Software at SC 09
Posted by admin in Blog, analytic infrastructure, cloud computing on November 11, 2009
SC 09 is in Portland this coming week from November 14 to 20. The Laboratory for Advanced Computing will have a booth and be showcasing a number of open source cloud computing technologies including:
Sector. Sector/Sphere is a high performance storage and compute cloud that scales to wide area networks. With Sector’s simplified parallel programming framework, you can easily apply a user defined function (UDF) to datasets that fill data centers. The current version of Sector is version 1.24 and includes support for streams and multiple master servers. Sector was the basis for an application that won the SC 08 Bandwidth Challenge. For more information, see sector.sourceforge.net.
As measured by the MalStone Benchmark, Sector was over 2x fast as Hadoop. Sector was one of six technologies selected by SC 09 as a disruptive technology.
This snapshot is from the LAC Cloud Monitor monitoring a Sector computation on the Open Cloud Testbed.
Cistrack. The Chicago Utilities for Biological Science or CUBioS is a set of integrated utilities for managing, processing, analyzing and sharing biological data. CUBioS integrates databases with cloud computing to provide an infrastructure that scales to high throughput sequencing platforms. CUBioS uses the Sector/Sphere cloud to process images produced by high throughput sequencing platforms. Cistrack is a CUBioS instance for cis-regulatory data. For more information, see www.cistrack.org.
Canopy. With clouds, it is now possible with a portal to create, monitor, and migrate Virtual Machines (VMs). With the open source Canopy application, it is now possible to create, monitor and migrate Virtual Networks containing multiple VMs connected with virtualized network infrastructure. Canopy provides a standardized library of functions to programatically control switch VLAN assignments to create VNs at line speed. Canopy is an open source project with an alpha releases planned for 2010.
UDT. UDT is a widely deployed (with millions of deployed instances) application level network transport protocol designed for large data transfers over wide area high performance networks. For more information, see udt.sourceforge.net.
UDX. UDX is a version of UDT that is designed for wide area high performance research and corporate networks within a single security domain (UDX does not contain the code UDT uses for transversing fire walls). In recent tests, UDX was able to achieve over 9.2 Gbps on a 10 Gbps wide area testbed. For more information, see udt.sourceforge.net.
LAC Cloud Monitor (LACCM). The LAC Cloud Monitor is a low overhead monitor for clouds that gathers system performance for thousands of servers along multiple dimensions. It integrates with the Argus Monitoring System and Nagios for logging and alerting. LACCM is used to monitor the OCC Open Cloud Testbed. LACCM is open source.
LAC Cloud Scheduler (LACCS)The LAC Cloud Scheduler (LACCS) is a system for scheduling clouds for exclusive use by researchers. It is simple to use, scalable, and easy to deploy. Using LACCS, multiple groups can share easily a local or wide area cloud. LACCS is used for scheduling the Open Cloud Testbed. LACCS is open source.
This is a segment that aired on WTTW’s Chicago Matters about cloud computing that described the Sector/Sphere and the Open Cloud Testbed. You need to select the episode on the right hand side of the page dated November 10, 2009 and titled “Chicago Matters Beyond Burnham (9:40)”
Cloud Computing Testbeds
Posted by admin in Blog, analytic infrastructure, cloud computing on July 29, 2009
Cloud computing is still an immature field: there are lots of interesting research problems, no standards, few benchmarks, and very limited interoperability between different applications and services.
Currently, there are relatively few testbeds available to the research community for research in cloud computing and few resources available to developers for testing interoperability. I expect this will change over time, but below are the testbeds that I am aware of and a little bit about each of them. If you know of any others, please let me know so that I can keep the list current (at least for a while until cloud computing testbeds become more common).
Before discussing the testbeds per se, I want to highlight one of the lessons that I have learned while working with one of the testbeds — the Open Cloud Testbed (OCT).
Disclaimer: I am one of the technical leads for the OCT and one of the Directors of the Open Cloud Consortium.
Currently the OCT consists of 120 identical nodes and 480 cores. All were purchased and assembled at the same time by the same team. One thing that caught me by suprise is that there are enough small differences between the nodes that the results of some experimental studies can vary by 5%, 10%, 20%, or more, depending upon which nodes are used within the testbed. This is because even one or two nodes with slightly inferior performance can impact the overall end-to-end performance of an application that uses some of today’s common cloud middleware.
Amazon Cloud. Although not usually thought of as a testbed, Amazon’s EC2, S3, SQS, EBS and related services are economical enough that they they can serve as the basis for an on-demand testbed for many experimental studies. In addition, Amazon provides grants so that their cloud services can be used for teaching and research.
Open Cloud Testbed (OCT). The Open Cloud Testbed is a testbed managed by the Open Cloud Consortium. The testbed currently consists of 4 racks of servers, located in 4 data centers at Johns Hopkins University (Baltimore), StarLight (Chicago), the University of Illinois (Chicago), and the University of California (San Diego). Each rack has 32 nodes and 128 cores. Two Cisco 3750E switches connect the 32 nodes, which then connects to the outside by a 10Gb/s uplink. In contrast to other cloud testbeds, the OCT utilizes wide area high performance networks, not the familiar commodity Internet. There are 10Gb/s networks that connect the various data centers. This network is provided by Cisco’s CWave national testbed infrastructure and through a partnership with the National Lambda Rail. Over the next few months the OCT will double in size to 8 racks and over 1000 cores. In the OCT, a variety of cloud systems and services are installed and available for research, including Hadoop, Sector/Sphere, CloudStore (KosmosFS), Eucalyptus, and Thrift. The OCT is a testbed designed to support systems-level, middleware and application level research in cloud computing, as well as the development of standards and interoperability frameworks. A technical report described the OCT is available from arxiv.org:0907.4810.
Open Cirrus(tm) Testbed. The Open Cirrus Testbed is a joint initiative sponsored by HP, Intel and Yahoo! in collaboration with the NSF, the University of Illinois at Urbana-Champaign (UIUC), Karlsruhe Institute of Technology, and the Infocomm Development Authority (IDA) of Singapore. Each of the six sites consists of at least 1000 cores and associated storage. The Open Cirrus Testbed is a federated system designed to support systems-level research in cloud computing. A technical report describing the testbed can be found here.
Eucalyptus Public Cloud. The Eucalyptus Public Cloud is a testbed for Eucalyptus applications. Eucalyptus shares the same APIs as Amazon’s web services. Currently, users are limited to no more than 4 virtual machines and experimental studies that require 6 hours or less.
Google-IBM-NSF CLuE Resource. Another cloud computing testbed is the IBM-Google-NSF Cluster Exploratory or CluE Resource. The IBM-Google NSF CLuE resource appears to be a testbed for cloud computing applications in the sense that Hadoop applications can be run on the testbed but that the testbed does not support systems research and experiments involving cloud middleware and cloud services per se, as is possible with the OCT and the Open Cirrus Testbed. (At least this was the case the last time I checked. It may be different now. If it is possible to do systems level research on the testbed, I would appreciate it if someone would let me know.) NSF has awarded nearly $5 million in grants to 14 universities through its Cluster Exploratory (CLuE) program to support research on this testbed.
Test Drive the Sector Public Cloud
Posted by admin in Blog, analytic infrastructure, cloud computing, data intensive computing on June 23, 2009
Sector is an open source cloud written in C++ for storing, sharing and processing large data sets. Sector is broadly similar to the Google File System and the Hadoop Distributed File System, except that it is designed to utilize wide area high performance networks.
Sphere is middleware that is designed to process data managed by Sector. Sphere implements a framework for distributed computing that allows any User Defined Function (UDF) to be applied to a Sector dataset.
One way to think about this is as a generalized MapReduce. With MapReduce, users work with pairs and define a Map function and a Reduce function, and the MapReduce application creates a workflow consisting of a Map, Shuffle, Sort and Reduce. With Sector, users can create a workflow consisting of any sequence of User Define Functions (UDFs) and apply these to any datasets managed by Sector. In particular, Sphere has predefined Shuffle and Sort UDFs that can be applied to datasets consisting of pairs so that MapReduce applications can be implemented once a user defines a Map and Reduce UDF.
Sector also implements security and we are currently using it to bring up a HIPAA-compliant private cloud.
Since Sector/Sphere is written in C++, it is straightforward to support C++ based data access tools and programming APIs.
If you have access to high speed research network (for example if you network can reach StarLight, the National Lambda Rail, ESNet, or Internet2), then you can try out the Sector Public Cloud.
You can reach the Sector Public Cloud from the Sector home page sector.sourceforge.net.
There is a technical report on the design of Sector on arXiv: arXiv:0809.1181v2.
There is some information on the performance of Sector/Sphere in my post on the MalStone Benchmark, a benchmark for clouds that support data intensive computing.
