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Fundamentally, kernel extensions are just device drivers that are loaded at kernel boot. However, because of this, their use cases are somewhat specialized. By the time the kernel is ready to start the scheduler, it requires resources that are not strictly de ned by the CPU architecture. These are provided by the variant and ASSP extensions, which I have discussed in 1, Introducing EKA2. These extensions are speci c to the particular platform that Symbian OS is running on, and permit the phone manufacturer to port the OS without re-compiling the kernel itself. After initializing the variant and ASSP extensions, the kernel continues to boot until it nally starts the scheduler and enters the supervisor thread, which initializes all remaining kernel extensions. At this point, all kernel services (scheduling, memory management, object creation, timers) and basic peripheral resources (interrupt controller and other ASSP/variant functionality) are available for use. Extensions loaded at this late stage are not critical to the operation of the kernel itself, but are typically used to perform early initialization of hardware components and to provide permanently available services for devices such as the LCD, DMA, I2C and peripheral bus controllers. The nal kernel extension to be initialized is the EXSTART extension, which is responsible for loading the le server. The le server is responsible for bringing up the rest of the OS. (If you want to nd out more about system boot, turn to 16, Boot Processes.) In Figure 12.1, the extension consists of two components the platform-independent layer (PIL) and platform-speci c layer (PSL). These are analogous to the LDD/PDD layering for device drivers that I discussed

^ Fig. 15.9 Con dence intervals for l2 for the tted Weibull, lognormal, exponential, and Pareto models of all interarrival times for the eight datasets.

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^ ^ corresponds to the value of l2 and the error bars to the standard deviation of l2 or the 68.26% con dence intervals. We see that for our datasets the Weibull model does substantially better than the others (except for FTP.DATA, consistent with Paxson and Floyd [35]) although the value of the discrepancy is quite substantial for some of the application types and some datasets. In fact, for all CMU datasets the Weibull model is statistically better than the other models in 203 of 240 cases, it is indistinguishable in 25 cases, and is beaten by one of the other models in only 12 instances. Actually the Pareto model is better in eight cases, while the exponential model is better in three cases, and the lognormal model in one case. For the AT&T datasets the Weibull model is statistically better than the other models in 90 of 95 instances, it is indistinguishable from the Pareto or the exponential model in three instances, and it is beaten by the Pareto model in only two instances. For the LBL and DEC datasets we observe that the Weibull distribution is capable of modeling some but not all applications. For example, the Weibull distribution performs very well for SMTP, NNTP, and FTP.CONN connections but poorly for HTTP and TELNET connections for the 1993 LBL-7 dataset. An explanation for HTTP is that very few people were using it at the time of the collection of the trace. Therefore the sample may not be appropriate. For TELNET it seems that a nonstationary approach is

earlier. To make porting an extension to a new hardware platform easier, the PIL is generally responsible for providing functionality common to versions of the extension (such as state machines and so on) and de ning the exported API, with the PSL taking on the responsibility of communicating directly with the hardware. Therefore, when porting to a new hardware platform only the PSL should require modi cation. Note: Some device drivers use the same concept and split the PDD into platform-independent and platform-speci c layers. One such example is the local media sub-system this consists of a generic LDD interface suitable for all media drivers, and a PDD interface which is further divided to handle common device interfaces such as ATA/PCMCIA, NAND or NOR Flash.

necessary to model this data. For the one hour long 1995 LBL-5 dataset, the distribution of HTTP interarrival times is modeled best using a lognormal distribution while the total dataset is explained best via an exponential or Weibull model. For the one hour long 1995 DEC-4 dataset the distribution of HTTP interarrival times is modeled best using the Weibull distribution. The overall distribution is also explained best by a Weibull distribution. Yet, with a shape parameter of 0.83, the distribution is relatively close to an exponential distribution, which is not too surprising given that most connections are either TELNET or FTP connections. Indeed, because more than one- fth of all TCP connections are HTTP connections, a distribution with a heavier tail than the exponential distribution appears appropriate. While most of the application-speci c Weibull models have small discrepancies, we observe that the discrepancies are quite large for some applications such as NNTP, POP or PROTO. We identi ed two major causes for such large discrepancies: , bimodality and timer-driven applications. Since neither of our models is able to t such distorted distributions, the resulting large discrepancy values are no surprise. 15.5.5 Application-Speci c Interarrival Times over Shorter Time Periods