Learning from Clouds Past: A Look Back at Magellan
In 2009, the US Department of Energy (DOE) launched a bold experiment, a $32 million program to assess the benefit of cloud computing to the scientific community. A distributed testbed infrastructure, named Magellan, was established at the Argonne Leadership Computing Facility (ALCF) and the National Energy Research Scientific Computing Center (NERSC) to provide a tool for computational science in a cloud environment. Magellan, with funding from the American Recovery and Reinvestment Act, was to help major research organizations answer the classic cloud question: is it better to rent or buy?
“What we’re exploring is the question of whether the DOE or other government agencies should be buying their own clusters … or whether those kinds of purchases should be done in a more consolidated way,” said NERSC Director Kathy Yelick in a previous article.
Despite high-hopes and community support, in late 2011, we learned that the Magellan project was being discontinued, leaving many wondering what happened. Now we have some answers in the form of a 169-page report, sponsored by the Department of Energy’s Office of Advanced Scientific Computing Research (ASCR), which funded the study to assess what Magellan tells us about the the role of cloud computing for scientific applications.
Since industry was already benefiting from the cloud model, from the economies of scale generated by a shared pool of network-accessible resources, the Magellan team members initially set out to determine if cloud would hold the same potential for science. As stated in the executive summary:
The goal of Magellan, a project funded through the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR), was to investigate the potential role of cloud computing in addressing the computing needs for the DOE Office of Science (SC), particularly related to serving the needs of mid-range computing and future data-intensive computing workloads. A set of research questions was formed to probe various aspects of cloud computing from performance, usability, and cost.
Specifically, Magellan was tasked with addressing the following questions:
- Are the open source cloud software stacks ready for DOE HPC science?
- Can DOE cyber security requirements be met within a cloud?
- Are the new cloud programming models useful for scientific computing?
- Can DOE HPC applications run efficiently in the cloud? What applications are suitable for clouds?
- How usable are cloud environments for scientific applications?
- When is it cost effective to run DOE HPC science in a cloud?
It should be noted that Magellan was not a typical commercial cloud, rather this “science cloud” was purpose-built for the special requirements of scientific computing. Magellan was based on the IBM iDataplex chassis using Intel processor cores for a theoretical peak performance of over 100 teraflop/s. Other components include:
- High bandwidth, low-latency node interconnects (InfiniBand).
- High-bin processors tuned for performance.
- Preinstalled scientific applications, compilers, debuggers, math libraries and other tools.
- High-bandwidth parallel file system.
- High-capacity data archive.
During Magellan’s two-year run, the staff at NERSC and Argonne National Laboratory examined how different aspects of cloud computing infrastructure and technologies could be harnessed by various scientific applications. They evaluated cloud models such as Infrastructure as a Service (IaaS) and Platform as a Service (Paas), virtual software stacks, MapReduce and open-source implementation (Hadoop), as well as resource provider and user perspectives.
Using a wide-range of applications as benchmarks, the researchers compared the Magellan cloud with various other architectures, including a Cray XT4 supercomputer, a Dell cluster system, and Amazon’s EC2 commercial cloud offering. Despite the testbed moniker, a lot of important production science took place, contributing to advances in particle physics, climate research, quantum chemistry, plasma physics and astrophysics.
Science workloads, by their nature, tend to be cloud-challenged, although to varying degrees. The report outlines the three major classifications of computational models, beginning with large-scale tightly-coupled science codes, which require the power of traditional supercomputers and take a big penalty working in a virtualized cloud environment. Then, there are the mid-range tightly-coupled applications, which run at a smaller scale and tend to be good candidates for cloud, although there is some performance loss. The final category, high-throughput workloads, usually involve asynchronous, independent computations, and in the past relied on desktop and small clusters for processing. But due to an explosion in sensor data, cloud is a good fit, especially when you factor in the fact that these high-throughput and data-intensive workloads do not fit into current scheduling and allocation policies.
The two-year Magellan project led to these key findings:
- Scientific applications have special requirements that require cloud solutions that are tailored to these needs.
- Scientific applications with minimal communication and I/O are best suited for clouds.
- Clouds require significant programming and system administration support.
- Significant gaps and challenges exist in current open-source virtualized cloud software stacks for production science use.
- Clouds expose a different risk model requiring different security practices and policies.
- MapReduce shows promise in addressing scientific needs, but current implementations have gaps and challenges.
- Public clouds can be more expensive than in-house large systems. Many of the cost benefits from clouds result from the increased consolidation and higher average utilization.
- DOE supercomputing centers already achieve energy efficiency levels comparable to commercial cloud centers.
- Cloud is a business model and can be applied at DOE supercomputing centers.
From this list, it is apparent that cloud was unable to measure up to a centralized supercomputer system in many ways, but the delivery model does have its place. According to the report, “users with applications that have more dynamic or interactive needs could benefit from on-demand, self-service environments and rapid elasticity through the use of virtualization technology, and the MapReduce programming model to manage loosely coupled application runs.”
In other words, cloud excels when it comes to flexibility and responsiveness. In fact, the report found that “for users who need the added flexibility offered by the cloud computing model, additional costs may be more than offset by the increased flexibility. Furthermore, in some cases the potential for more immediate access to compute resources could directly translate into cost savings.”
However, when it comes to the potential cost savings of using a public cloud versus the costs of hardware acquisition, the report makes the point that DOE procurement costs are often significantly discounted, which offsets some of the potential savings:
Existing DOE centers already achieve many of the benefits of cloud computing since these centers consolidate computing across multiple program offices, deploy at large scales, and continuously refine and improve operational efficiency. Cost analysis shows that DOE centers are cost competitive, typically 3-7x less expensive, when compared to commercial cloud providers. Because the commercial sector constantly innovates, DOE labs and centers should continue to benchmark their computing cost against public clouds to ensure they are providing a competitive service.
“Cloud computing is ultimately a business model,” state the authors. “But cloud models often provide additional capabilities and flexibility that are helpful to certain workloads. DOE labs and centers should consider adopting and integrating these features of cloud computing into their operations in order to support more diverse workloads and further enable scientific discovery, without sacrificing the productivity and effectiveness of computing platforms that have been optimized for science over decades of development and refinement.”
The authors further suggest that when an integrated approach is not sufficient, a private cloud solution should be considered based on its ability to provide many of the benefits of commercial clouds while avoiding some of the pitfalls, such as security, data management, and performance penalties.
To recap: cloud services are a good complement to centralized computing resources, but not a replacement. This should not come as a surprise to our community. This is HPC, high-performance computing, and whenever you add additional layers, i.e., virtualization, the application takes a performance hit. However, as the report makes clear, there are good use cases for cloud services, such as “scientific groups needing support for on-demand access to resources, sudden surges in resource needs, customized environments, periodic predictable resource needs (e.g., monthly processing of genome data, nightly processing of telescope data), or unpredictable events such as computing for disaster recovery.” The report goes on to note that “cloud services essentially provide a differentiated service model that can cater to these diverse needs, allowing users to get a virtual private cluster with a certain guaranteed level of service.”
Magellan was billed as an exploratory project, set to go for two years. In fact, the project was named Magellan in honor of the Portuguese explorer Fernão de Magalhães, the first person to lead an expedition across the Pacific. The original “clouds of Magellan” refers to two small galaxies in the southern sky. The current-day Magellan, as the first major scientific cloud testbed, also navigated uncharted waters and documented the journey for the benefit of future generations.