Magellan Final Report - Office of Science - U.S. Department of Energy

Chapter 7
User Support
Many of the aspects of cloud computing that make it so powerful also introduce new complexities and
challenges for both users and user support staff. Cloud computing provides users the flexibility to customize
their software stack, but it comes with the additional burden of managing the stack. Commercial cloud
providers have a limited user support model and typically additional support comes at an extra cost. This
chapter describes the user support model that was used for the Magellan project, including some of the
challenges that emerged during the course of the project. We discuss the key aspects of cloud computing
architecture that have bearing on user support. We discuss several examples of usage patterns of users and
how these were addressed. Finally, we summarize the overall assessment of the user support process for
mid-range computing users on cloud platforms.
7.1 Comparison of User Support Models
HPC centers provide a well-curated environment for robust, high-performance computing, which they make
accessible to non-expert users through a variety of activities. In these environments, substantial effort is put
into helping users to be productive and successful on the hosted platform. These efforts take a number of
forms, from building a tuned software environment that is optimized for HPC workloads, to user education,
and application porting and optimization. These efforts are important to the success of current and new
users in HPC facilities, as many computational scientists are not necessarily deeply knowledgeable in terms
of the details of modern computing hardware and software architecture.
HPC centers typically provide a single system software stack, paired with purpose built hardware, and a
set of policies for user access and prioritization. Users rely on a relatively fixed set of interfaces for interaction
with the resource manager, file system, and other facility services. Many HPC use cases are well covered
within this scope; for example, this environment is adapted for MPI applications that perform I/O to a
parallel file system. Other use cases such as high-throughput computing and data-intensive computing, may
not be so well supported at HPC centers. For example, computer scientists developing low level runtime
software for HPC applications have a particularly difficult time performing this work at production computing
centers. Also, deploying Hadoop on demand for computations, could be performed within the framework of
a traditional HPC system, albeit with significant effort and in a less optimized fashion.
Cloud systems provide Application Programming Interfaces (API) for low level resource provisioning.
These APIs enable users to provision new virtual machines, storage, and network resources. These resources
are configured by the user and can be built into complex networks including dozens, hundreds, or potentially
even thousands of VMs with distinct software configurations, security policies, and service architectures. The
flexibility of the capabilities provided by cloud APIs is substantial, allowing users to manage clusters built
out of virtual machines hosted inside of a cloud. This power comes at some cost in terms of support. The
cloud model offers a large amount of flexibility, making it difficult and expensive to provide support to cloud
users. The opportunities for errors or mistakes greatly increase once a user begins to modify virtual machine
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