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6.1 Introduction

To be clear, our concern throughout this chapter is with commercial services which rent computational resources over the Internet at short notice and charge in small increments (by the minute or the hour). In 2016, the four largest such services6.1 were (in alphabetical order) Amazon Web Services (`AWS'), (Microsoft) Azure, Google Cloud Platform (`GCP'), and (IBM) SoftLayer; in verson 8.7.1, the condor_annex tool supports only AWS. AWS can start booting a new virtual machine as quickly as a few seconds after the request; barring hardware failure, you will be able to continue renting that VM until you stop paying the hourly charge. The other cloud services are broadly similar.

If you already have access to the Grid, you may wonder why you would want to begin cloud computing. The cloud services offer two major advantages over the Grid: first, cloud resources are typically available more quickly and in greater quantity than from the Grid; and second, because cloud resources are virtual machines, they are considerably more customizable than Grid resources. The major disadvantages are, of course, cost and complexity (although we hope that condor_annex reduces the latter).

We illustrate these advantages with what we anticipate will be the most common uses for condor_annex.

6.1.1 Use Case: Deadlines

With the ability to acquire computational resources in seconds or minutes and retain them for days or weeks, it becomes possible to rapidly adjust the size - and cost - of an HTCondor pool. Giving this ability to the end-user avoids the problems of deciding who will pay for expanding the pool and when to do so. We anticipate that the usual cause for doing so will be deadlines; the end-user has the best knowledge of their own deadlines and how much, in monetary terms, it's worth to complete their work by that deadline.

6.1.2 Use Case: Capabilities

Cloud services may offer (virtual) hardware in configurations unavailable in the local pool, or in quantities that it would be prohibitively expensive to provide on an on-going basis. Examples in 2017 may include GPU-based computation, or computations requiring a terabyte of main memory. A cloud service may also offer fast and cloud-local storage for shared data, which may have substantial performance benefits for some workflows. Some cloud providers (for example, AWS) have pre-populated this storage with common public datasets, to further ease adoption.

By using cloud resources, an HTCondor pool administrator may also experiment with or temporarily offer different software and configurations. For example, a pool may be configured with a maximum job runtime, perhaps to reduce the latency of fair-share adjustments or to protect against hung jobs. Adding cloud resources which permit longer-running jobs may be the least-disruptive way to accomodate a user whose jobs need more time.

6.1.3 Use Case: Capacities

It may be possible for an HTCondor administrator to lower the cost of their pool by increasing utilization and meeting peak demand with cloud computing.

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