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It is possible to de ne a notion of traf c demand for elastic ows, in analogy with the de nition given above for stream traf c, as the product of an average arrival rate in a representative busy period and the average object size.



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Checkpointing can be done at the application level, with extra code to handle writing out critical state information; it can be done using a job scheduler or tool such as the Load Share Facility (LSF, from Platform Computing), or it can rely on operating system support for preserving an entire process and its in-memory state at any point in time Checkpoint-restart facilities are not common, so we ll stop at this brief mention of them All of the techniques and issues described in the following text apply to OS-level support as well, but we re going to focus on more application-controlled techniques to allow restart and recovery What kind of failures scare the scientific coder the most Obviously, resource shortages such as memory, swap space, and filesystem space represent threats to the completion of a long-running code.





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Another category of traf c arises when individual ows and transactions are grouped together in an aggregate traf c stream This occurs currently, for example, when the ow between remotely located LANs must be treated as a traf c entity by a wide area network Proposed evolutions to the Internet service model such as differentiated services and multiprotocol label switching (MPLS) also rely heavily on the notion of traf c aggregation Through aggregation, quality of service requirements are satis ed in a two-step process: the network guarantees that an aggregate has access to a given bandwidth between designated end points; this bandwidth is then shared by ows within the aggregate according to mechanisms like those described in the rest of this chapter.

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There are also issues of poorly conditioned inputs, application bugs, and floating-point exceptions that can cause a finely tuned, sensitive piece of code to crash if given slightly erroneous inputs midway through the simulation With checkpoints made along the way, the application can back up to a well-known point in time and restart, perhaps with finer input checking or resolution of system resource constraints The most obvious approach: At well-defined intervals, copy out state information such as in-memory matrices, file I/O position indicators, locking information, counters, and intermediate results After ensuring that the disk I/O has completed, and that the state variables dumped are consistent with each other, the application can continue When the application is restarted, it should check for the existence of a clean checkpoint file or have a flag passed on the command line indicating it should resume a checkpointed operation.

Typically, the network provider has the simple traf c management task of reserving the guaranteed bandwidth while the responsibility for sharing this bandwidth between individual stream and elastic ows devolves to the customer This division of responsibilities alleviates the so-called scalability problem, where the capacity of network elements to maintain state on individual ows cannot keep up with the growth in traf c The situation would be clear if the guarantee provided by the network to the customer were for a xed constant bandwidth throughout a given time interval In practice, because traf c in an aggregation is generally extremely variable (and even self-similar), a constant rate is not usually a good match to user requirements Some burstiness can be accounted for through a leaky bucket based traf c descriptor, although this is not a very satisfactory solution, especially for self-similar traf c (see Section 1632).

You ll need to manage the time required to create the checkpoint and write out the state information as an overhead factor; if it takes you 6 minutes to write out state for a 200GB memory segment, you don t want to do this every 10 minutes Once an hour reduces your duty cycle to 90 percent, with 10 percent of the time spent writing out checkpoints Balance the checkpoint interval against the difficulty in backing out changes made since the last known good point If you re walking through a file, updating records, make sure the checkpoint contains a copy of the file as it was at the time of the checkpoint, because you may update another 3,000 records before.

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