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This repository was archived by the owner on Nov 1, 2020. It is now read-only.
In general, individuals working with datacenters think about their datacenter in terms of its topology.
@@ -497,7 +497,7 @@ \section{Design of a Datacenter Hardware Representation} \label{sec:design}
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Readability of the data structure allows for easier extensions in the future by making the data structure easier to debug, recreate, and test with.
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This is especially useful when working with extensions that interact with the API.
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The readablity of the format also supports possible future work, that may extend \opendc{} to include a console for programming prefabs and topologies.
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The readability of the format also supports possible future work, that may extend \opendc{} to include a console for programming prefabs and topologies.
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The database used in version 1.x of \opendc{} contains 35 SQL tables, 20 of which are used to store topologies.
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As a result, adding hardware items to the database requires a complex set of queries, with deletions and modifications requiring additional queries.
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One example of such a use would be to capture datacenter features of domains of societal interest, making them available as a library of prefabs for users of \opendc{}.
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This would provide a starting point for users intending to explore a wide variety of domains, making datacenter design in \opendc{} accessible to a wider audience of users.
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In Chapter~\ref{sec:domainspecificprefabs}, we present an approach to capturing datacenter features for a specific domain (namedly, High Performance Computing).
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In Chapter~\ref{sec:domainspecificprefabs}, we present an approach to capturing datacenter features for a specific domain (namely High Performance Computing).
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Another important potential application of prefabs would be to enable an increase in the speed of datacenter design, while retaining the same level of detail.
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Currently, designing a topology requires lots of interactions by the user, as they must build the entirety of every design from the ground up.
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Depending on the length of the copying session, it is also possible that the absence of prefabs may cause an increase in the presence of the $H$ action (whereby a user homes their hands on the keyboard or mouse), as the copying session is broken up into smaller sessions.
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Because copying is much faster with prefabs, this same phenomena may be avoided, as copying sessions would typically be much shorter than by conventional methods.
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The factor $M$ (whereby a user mentally prepares for a task) could have a larger effect for prefabs than for conventional appoaches, as a user must think about which prefab to copy.
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The factor $M$ (whereby a user mentally prepares for a task) could have a larger effect for prefabs than for conventional approaches, as a user must think about which prefab to copy.
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However, the reverse is just as likely to be true, with repeated additions of single items potentially requiring much more mental preparation in the conventional approach (e.g. as designers maintain a running count in their head of how many times they have copied an item).
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Real-world experiments where real human actions are measured for this cognitive task are necessary to effectively test for this effect.
Extending prefabs to support prefabs of varying sizes would also provide benefits.
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By enabling users to save whole rooms as prefabs, designing and experimenting using \opendc{} would get even faster, allowing dozens of racks of machines to be copied in just a few clicks.
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On the opposite end of the scale, heing able to provide small prefabs for chassis would also provide a more intuitive approach for sharing models of individual server nodes.
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On the opposite end of the scale, being able to provide small prefabs for chassis would also provide a more intuitive approach for sharing models of individual server nodes.
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Lastly, it would be interesting to design a means for \opendc{} to simulate user-specified workloads on systems designed by \opendc{} itself.
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\opendc{} would be able to leverage a large database of performance data to make component choices based on the workload specified by the user.
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