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Finally, open access competition depends on the data link operator provisioning and policing the quality of service provided to each network layer competitor With data link competition, QoS of each service provider is essentially independent and under the competitor s control Link layer competition is not just about ATM vs Ethernet The link layer also de nes the port speed (downstream and upstream capacity), the number of splits (in a PON) and therefore, in effect, bandwidth per home So, one can imagine that with UNE-based competition, different competitors can provision the same avor of data link technology (whether APON, EPON, BPON or GPON) with different upstream and downstream capacities and split ratios It is conceivable that competitors may even choose to deploy an active star, while a competitor deploys a PON (for example using hardened electronics at the OFAP cabinet) In the event that the loop architecture facilitates data link layer competition between multiple players, each data link layer service provider could either choose to integrate vertically with higher layer service providers (like ISPs or video service providers) or choose to provide open access Vertical integration would permit only as many higher layer service providers as there are data link layer service providers Whether this is a suf cient number of ISPs or video service providers depends on precisely how many data link service providers can be supported, and on second mile costs, operations and marketing costs, all of which we have yet to consider If the number of link layer competitors is small, it may be desirable to have open access as well, to ensure maximum competition at the services layer

2 The throughput of executing work items from the queue The first is important because many parallel algorithms of the kind we look at in the Algorithms Section of this book make frequent calls to queue new work items Substantial overhead here stretches the sequential amount of work done by any given thread, particularly as many such algorithms must queue more than one work item The second is also important because the overhead imposed on each work item can make concurrency look less attractive, particularly for very fine-grained work items Both limit the possible parallel speedups that can be realized and are affected by adding more processors: as more processors are added, there may be more contention for enqueuing new work items (metric 1) in addition to dequeuing work items for execution (metric 2) We will take a quick look at scalability after examining these micro-benchmark style metrics In the native code arena, the move to Vista brings with it vastly better performance all around This is primarily due to the thread pool's code liv ing in user-mode rather than kernel-mode, incurring fewer kernel transi tions Even programs still using the legacy APIs but running on Windows Vista will benefit from this new architecture, because the old APIs are just reimplemented in terms of the new ones

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The CLR' s thread pool has also had some large performance improvements over the years Considering the first metric, from 1 1 to 20 the performance distance between Qu e u e U s e rWo r k ltem and U n s afe Qu e u e U s e rWo r k ltem was shortened dramatically It used to be the case that Qu e u e U s e rWo r k ltem was more than twice the cost of U n s a feQu e u e U s e r Wo r k l t em, but in 20 this was reduced to about 1 5 to 30 percent more costly, on average That margin is certainly not 0 percent, but it's much better This comparison is a little unfair because Qu e u e U s e rWo r k Item in 20 actually costs less than U n s afeQu e u e U s e rWo r k ltem did in 1 1 , so programs that use

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