Hidden in Plain Sight – ACM Queue
Hidden in Plain Sight
Enhancements within the observability of software program might help you diagnose your most crippling efficiency issues.
BRYAN CANTRILL, SUN MICROSYSTEMS
In December 1997, Solar Microsystems had simply introduced its new flagship machine: a 64-processor symmetric multiprocessor supporting as much as 64 gigabytes of reminiscence and 1000’s of I/O gadgets. As with every new machine launch, Solar was working feverishly on benchmarks to show the machine’s efficiency. Whereas the benchmarks had been typically spectacular, there was one particularly—an particularly difficult benchmark involving a number of machines—that was exhibiting unexpectedly low efficiency. The benchmark machine—a completely racked-out behemoth with the utmost configuration of 64 processors—would often grow to be mysteriously distracted: Benchmark exercise would virtually stop, however the working system kernel remained furiously busy. After some variety of minutes spent on unknown work, the working system would immediately proper itself: Benchmark exercise would resume at full throttle and run to completion. These operating the benchmark might see that the machine was on track to interrupt the world report, however these minutes-long durations of unknown kernel exercise had been sufficient to be the distinction between first and worst.
Given the significance of each the benchmark and the machine, Solar’s high improvement and help engineers had been known as in, and work proceeded on the issue across the clock and across the globe. Starting with the preliminary symptom of the issue—excessive lock competition within the networking stack—progress on the issue was very sluggish and painful. Primarily based on obtainable information, a speculation can be shaped, however as a result of there was no solution to discover the speculation with out gathering further information, customized data-gathering kernel modules needed to be created and loaded. Creating these modules was a fragile endeavor. The whole cycle of speculation creation by way of instrumentation and evaluation was taking hours—and that was assuming that no errors had been made. Errors had been painful, as any error in creating the customized module would end in a reboot—and reboots of this specific machine and configuration took on the order of 90 minutes.
Whereas iterations on the issue revealed a number of methods during which the networking stack might be improved to alleviate the noticed signs, it took almost every week to find out the true underlying root trigger: The benchmark machine had been misconfigured to behave as a router. Usually, this wouldn’t be an issue, however due to intermittent router failure elsewhere within the lab, the benchmark machine would often start to route packets furiously between two different machines related to the benchmark. This routing exercise would induce sufficient competition within the networking stack to pressure the system right into a heretofore unseen failure mode—one which induced but extra competition.
Was this an apparent drawback? Completely, looking back. However the cascading signs of the issue had been up to now faraway from the basis trigger that it was unimaginable to see the bigger drawback with out modifying the system itself. Maybe most disconcertingly, this drawback was a comparatively easy one, involving comparatively few layers of software program. Had extra elements of the software program stack been concerned—had the system needed to be modified at a number of layers simply to watch it—it could have been exponentially tougher to diagnose.
The bigger drawback right here is software program observability, or extra precisely, the pronounced lack of it. Now we have constructed mind-bogglingly difficult techniques that we can not see, permitting evident efficiency issues to cover in broad daylight in our techniques. How did we get right here? And what will be carried out about it?
Roots of the Drawback
The origins of the software program observability drawback, as with so many different software program issues, will be present in software program’s unusual duality as each data and machine: Software program has solely bodily illustration, not bodily manifestation. That’s, operating software program doesn’t replicate mild or emit warmth or entice mass or have every other bodily property that we would use to see it. In antiquity, computer systems addressed this by having specific modes during which each instruction was indicated because it was executed. Within the age of the microprocessor—and with the flexibility to execute billions of directions per second—such amenities have lengthy since grow to be impractical, if not unimaginable. There may be, due to this fact, no bodily solution to decide which directions are being executed; the one solution to see the software program being executed is to switch the software program itself. Software program engineers have lengthy carried out precisely that, including constructs that enable their software program to be optionally seen. Whereas the precise manifestations range, these constructs usually look one thing like this:
if (tracing_enabled)
printf(“we bought right here!n”);
There are numerous variants of this idea—for instance, a bit of knowledge could also be logged to a hoop buffer as an alternative of being explicitly printed, or the conditional could also be derived from an atmosphere variable as an alternative of a static variable—however the widespread concept is conditional execution primarily based upon the worth of the info. Constructs of this nature share an unlucky property: They make the system slower even when they aren’t in use. That’s, the mere presence of the conditional execution induces a load, a evaluate, and a department. Whereas clearly not problematic to execute each infrequently, the price of these directions turns into debilitating if they’re used extensively: If one had been to litter each perform or each fundamental block with this sort of assemble, the system can be too sluggish to ship. It has due to this fact grow to be a typical software program engineering follow to retain such constructs, however to robotically strip them out of manufacturing code utilizing strategies similar to conditional compilation. Whereas this permits the infrastructure to stay, it sadly bifurcates software program into two variations: one that may be seen that’s utilized in improvement and take a look at, and one that may’t be seen that’s shipped or deployed into manufacturing.
This offers rise to a paradox: Efficiency issues are more and more prone to be seen in manufacturing, however they are often understood solely in improvement. To handle a efficiency drawback seen in manufacturing, the issue should due to this fact be reproduced in both a improvement or take a look at atmosphere. In a software program system, as in any sufficiently difficult system, disjointed pathologies can manifest the identical signs: Reproducing signs (e.g., excessive CPU load, heavy I/O site visitors, lengthy latency transactions, and many others.) doesn’t assure reproducing the identical underlying drawback. To phrase this phenomenon within the vernacular of IT, you may need heard or mentioned one thing like this:
“Excellent news: We had been capable of reproduce it in dev, and we predict that we discovered the issue. Dev has a repair, and we’re scheduling downtime for tonight to place it into prod. Let’s maintain our fingers crossed…”
Solely to have it adopted the following day by one thing like this:
“Hey, yeah, about that repair…effectively, the excellent news is that we’re sooner this morning; the unhealthy information is that we’re solely about 3 % sooner. So it’s again to the warfare room…”
If this sounds eerily acquainted, it’s as a result of you’ve fallen into the lure endemic to attempting to know manufacturing issues by reproducing their signs elsewhere: You discovered an issue that was not the issue.
However Wait, It Will get Worse
The shortage of observability endemic to manufacturing software program is unhealthy sufficient, however the layering of software program abstraction makes the efficiency drawback way more acute. Usually, software program abstraction is sweet information, for it’s abstraction that empowers one to develop an utility with out having to develop the Internet server, utility server, database server, and working system that the appliance rests upon. This energy has a darker facet, nonetheless: When growing at increased layers of abstraction, it’s simpler to by accident induce unintended or pointless work within the system. That is tautologically true: To be at a better layer of abstraction, much less code should induce extra work, that means it takes much less of a misstep to induce extra unintended penalties.
Sadly, this pointless or unintended work tends to multiply because it cascades down the stack of abstraction, a lot in order that efficiency issues are usually first understood or characterised on the very lowest layer of the stack when it comes to excessive CPU utilization, acute reminiscence strain, irregular I/O exercise, extreme community site visitors, and many others. Regardless of being a consequence of higher-level software program, signs on the lowest layer of the stack are prone to be most instantly attributable to exercise within the subsequent lowest layer—for instance, the working system or the database.
This presents one other paradox: System efficiency issues are usually launched on the highest layers of abstraction, however they’re usually first encountered and attributed on the lowest layers of abstraction. It’s due to this paradox that we have now adopted the parable that the trail to efficiency lies almost solely with sooner {hardware}: sooner CPUs, extra networking bandwidth, and many others. When this fails, we have now taught ourselves to maneuver to the following layer of the stack: to demand sooner working techniques, sooner databases, and higher compilers. Bettering these elements undoubtedly improves efficiency, however (to make use of a maybe insensitive metaphor) it quantities to searching vermin: Relying on the comparatively small iterative enhancements on the lowest layers of the stack quantities to attempting to feed a household on the likes of squirrel and skunk. If we are able to transfer up the stack—if we are able to discover the underlying efficiency issues as an alternative of merely addressing their signs—we are able to unlock way more substantial efficiency positive aspects. That is larger recreation to make sure; by specializing in efficiency issues increased within the stack, we are able to transition from searching vermin to searching cow—large, sluggish, silly, tasty cow.
Constraints on a Resolution
To hunt cow in its native habitat, the main target of observability infrastructure should make two profound shifts: from improvement to manufacturing, and from packages to techniques. These shifts have a number of vital implications. First, the shift from improvement to manufacturing implies that observability infrastructure will need to have zero disabled probe impact: The mere skill to watch the system should not make the delivered system any slower. This constraint permits just one actual answer: Software program have to be optimized when it ships, and—when one needs to watch it—the software program have to be dynamically modified. Additional, the shift from packages to techniques calls for that the complete stack should be capable to be dynamically instrumented on this manner, from the depths of the working system, by way of the system libraries, and into the vaulted heights of higher-level languages and environments. There have to be no dependency on compile-time choices, having supply code, restarting elements, and many others.; it have to be assumed that the primary time a physique of software program is to be noticed, that software program is already operating in manufacturing.
Dynamically instrumenting manufacturing techniques on this manner is a scary proposition, particularly when the working system kernel is introduced into the combo. This leads us to a very powerful implication of the shift in focus from improvement to manufacturing: Observability infrastructure have to be completely protected. This security constraint can’t be overemphasized: In manufacturing techniques, outage is all the time unacceptable, and even an outage ensuing from operator error is prone to be blamed as a lot on the system as on the operator. If using a software is in any manner concerned with a manufacturing outage, it’s possible that the software will probably be banished without end from the manufacturing atmosphere. Security in observability infrastructure, like safety in an working system, can’t be an afterthought: It have to be thought-about an absolute, non-negotiable constraint on the structure.
One Resolution: DTrace
At Solar we have now developed DTrace, a facility for dynamic instrumentation of manufacturing techniques. DTrace is most clearly differentiated from prior work by its give attention to manufacturing techniques and particularly by its strict adherence to the security constraint. Whereas it’s a part of the working system (it was initially developed for Solaris 10), DTrace itself is open supply and as such will be ported to different techniques (particularly, a port of DTrace to FreeBSD has been initiated1). DTrace is a complicated system (see Cantrill, Shapiro, and Leventhal from the 2004 Usenix conference2 and the Solaris Dynamic Tracing Guide3 for extra data), however it’s value elucidating its higher-level ideas and the way these ideas guided the evolution of its structure.
To guarantee zero disabled probe impact, DTrace was designed across the concept of dynamic instrumentation. Whereas the precise strategies for dynamic instrumentation are sometimes instruction-set-specific and arcane in nature, they share a basic precept: They modify operating code to revector management movement to a physique of interposition code that collects some data, after which returns management movement to only past the purpose of instrumentation. The mechanism for revectoring management varies, however it’s most frequently an instruction both to situation a software program lure or to unconditionally department to interpositioned code. In sharing this basic precept, dynamic instrumentation strategies additionally share a typical failing: No method can work in all contexts within the system. That’s, in any system there are some contexts which are just too delicate to be dynamically instrumented. (Such contexts usually embody low-level code for interrupt dealing with, context switching, or synchronization.)
Abiding by the security constraint whereas nonetheless permitting dynamic instrumentation thus poses one thing of a problem: How does one give the flexibleness of dynamic instrumentation with out compromising the security required in a manufacturing system? In DTrace, we achieved this by separating the way in which the system is instrumented from the framework that consumes that information. The methodology for instrumenting the system lies in suppliers; these suppliers make obtainable probes by way of the DTrace framework. Security of instrumentation thus turns into the duty of the suppliers, which publish solely these probes that may be safely enabled. By constraining DTrace customers to enabling solely printed probes, the system—not the consumer—turns into liable for figuring out what will be safely instrumented. It is a compromise of kinds in that it comes at some lack of flexibility: For instance, a supplier could also be discovered to be unnecessarily conservative when deciding {that a} specific physique of code can’t be instrumented. This minor lack of flexibility is required to guarantee that the consumer can not by accident or willfully sacrifice system integrity. That is an assurance that any software should be capable to make earlier than it may be used on manufacturing techniques.
The suppliers implement the mechanism for instrumenting totally different elements of the system, however find out how to act on that instrumentation? That’s, what motion does one want to take when instrumenting a part of the system? Our experiences with a number of the extra primitive tracing amenities that predated DTrace usually left us annoyed: It all the time appeared as if the info that we needed at a given level was simply past the info supplied. In designing DTrace, we needed to obviate these frustrations by making actions solely programmable.
This instantly implied two courses of architectural choices: First, in what language ought to actions be specified? Second, how can the system be designed to guarantee that these programmable actions are executed in a protected method? On condition that security was our constraint, we addressed the latter of those first: to permit programmable actions to be executed safely in arbitrary contexts, it was clear that we wanted to develop a digital machine that would act as a goal instruction set for a customized compiler. This was clear as a result of the choice—executing user-specified code natively within the kernel—is untenable from a security perspective: Even should you might implement the substantial quantity of static evaluation essential to guarantee that native code doesn’t carry out an unlawful operation, you might be nonetheless left with the intractability of the Halting Drawback.
We due to this fact developed a easy, purpose-built digital machine designed to execute user-specified code within the working system kernel with out facet impact. Security is assured by rigorously limiting the instruction set structure: Our digital instruction set has no backwards branches, helps calls solely to outlined subroutines within the runtime, doesn’t enable arbitrary shops, and many others. (Observe that we clearly didn’t resolve the Halting Drawback; we merely prevented it by designing a digital machine that doesn’t enable Turing-complete languages.) Importantly, the digital machine does enable arbitrary hundreds; hundreds to unmapped reminiscence (or worse, memory-mapped gadgets for which even hundreds could have unwanted side effects) are caught, flagged, and dealt with gracefully.
With the flexibility to execute arbitrary actions safely, we might give attention to designing the language during which these actions must be expressed. For this objective, we developed D, a C-like language with DTrace-specific extensions similar to associative arrays, thread-local variables, and so forth. The design of D displays the security constraint: It explicitly lacks loop constructs, user-defined features, shops to arbitrary addresses, and different options that would compromise security. As a result of our digital machine has a mechanism for protected hundreds, D can (and does) enable for C-like pointer chasing.
Given a protected basis for dynamic instrumentation and arbitrary actions, the following sequence of issues centered on information administration. Most clearly, we wanted a mechanism for filtering out undesirable information on the supply. For this, we added the notion of a predicate: a conditional D expression connected to an motion such that the motion will probably be taken provided that the expression evaluates to true. Predicates, mixed with the ability of D and the heterogeneous instrumentation of DTrace, enable for stylish filtering. For instance, determine 1 reveals a script that makes use of predicates, a thread-local variable, and suppliers for each user- and kernel-level code to comply with movement management by way of an utility and into the working system kernel; determine 2 reveals the output from operating this script.
As determine 2 reveals, DTrace permits the flexibility to chop by way of totally different elements within the stack of abstraction, even throughout safety boundaries. Any framework for observability should not solely be capable to reduce by way of layers of elements, but in addition enable for patterns to be seen within the interactions. To impact this in DTrace, we elevated the aggregation of knowledge to a first-class notion: As an alternative of getting to clog the system with information to be post-processed, DTrace permits information to be keyed on an arbitrary n-tuple and aggregated on the supply. This reduces the info movement probably by an element of the variety of information factors—with out introducing any of the statistical inaccuracy endemic in sampling methodologies. Importantly, the DTrace aggregation mechanism scales linearly: Data is saved on a per-CPU foundation after which aggregated throughout CPUs periodically on the consumer degree. That is no mere implementation element; instruments for observing parallel techniques should all the time scale higher than the software program they’re attempting to watch, lest their very own bottlenecks obscure the scalability of the underlying system. The DTrace aggregation mechanism, as demonstrated in determine 3, permits questions concerning the system to be answered concisely.
The architectural parts of DTrace—protected and heterogeneous dynamic instrumentation, arbitrary actions and predicates, and scalable, in situ information aggregation—enable for unprecedented observability of manufacturing techniques. In utilizing DTrace, nonetheless, we shortly encountered a thorny drawback: Instrumenting the system successfully required data of the implementation. To rectify this shortcoming, we launched mechanisms for suppliers each to outline the interface stability of their probes and to declare translators that may translate from implementation-specific information buildings into extra summary, implementation-neutral ones. Collectively, these mechanisms enable for suppliers to summary away from their implementation by offering probes and arguments that signify subsystem semantics. For instance, as an alternative of requiring customers to know the features that carry out I/O within the working system, we launched an io supplier with probes with names similar to begin and carried out and with implementation-neutral arguments related to an I/O operation similar to details about the buffer, the machine, the file, and so forth. An instance, together with pattern output, of utilizing the I/O supplier is proven in determine 4.
With the flexibility to instrument the system when it comes to its semantics, a a lot bigger drawback got here into focus: Whereas we might instrument purposes written in natively compiled languages similar to C, C++, Fortran, Goal C, and many others., we had no perception into purposes written in interpreted languages or languages focused at a digital machine atmosphere. This was a crucial drawback, as a result of the stack of abstraction towers is far increased than the natively compiled languages: Any observability framework that claims systemic observability should be capable to attain into the extra summary ilk of Java, Python, Perl, PHP, and Ruby.
Instrumenting these environments is troublesome: Whether or not interpreted or compiled to bytecode, the system has little or no visibility into such an atmosphere’s notion of program textual content—not to mention find out how to instrument it. To permit for instrumentation of those environments, we developed a mechanism for user-level suppliers, permitting probes to be exported that correspond to semantically vital occasions of their atmosphere. These occasions range, however they usually embody calling a perform or methodology, creating an object, performing rubbish assortment, and many others. As of this writing, suppliers have been carried out for Java, Perl, PHP, Python, Ruby, Tcl, and (we’re not making this up) APL. Instance output of extending the script in determine 1 to PHP is displayed in determine 5. These suppliers are largely nonetheless prototypes, however they’re exceptional for the diploma to which they’ve improved the debuggability of their respective environments.
Expertise
Within the greater than two years that DTrace has been publicly obtainable, it has been used efficiently on many attention-grabbing “cow hunts,” each inside and outdoors of Solar. One significantly fruitful searching floor was a manufacturing SunRay server within Solar. This was a 10-CPU machine with 32 gigabytes of reminiscence and roughly 170 customers on which DTrace was used to search out many issues (probably the most notorious of which is mentioned extensively within the aforementioned reference 2).
One widespread grievance about this technique was its excessive degree of CPU utilization, which led to a pure query: Which purposes had been consuming CPU cycles? There are a number of methods to reply this with DTrace; the best is to make use of the DTrace profile supplier, whose probes fireplace at a uniform (and completely programmable) interval. This permits for a sampling methodology, and it permits one to, for instance, pattern the at the moment operating utility 1,234 occasions per second on every CPU:
profile-1234hz
{
@[execname] = rely();
}
Working such a script on the server for 30 seconds or so yielded the next output (the left column is the appliance identify and the fitting column is the variety of samples):
| mozilla-bin | 662 |
|
xprop |
719 |
| dtmail | 886 |
| netscape-bin | 1412 |
| nautilus | 1438 |
| ps | 2523 |
| java | 3451 |
| Xsun | 6197 |
| netscape | 6600 |
A lot of this output wasn’t too stunning: On condition that this machine was serving up desktops to 170 customers, one would anticipate to see X servers (Xsun) and Internet browsers (netscape) and mail readers (dtmail) as the highest CPU customers. There was one very stunning information level, nonetheless: The fourth greatest CPU client was ps, the Unix command to checklist the processes within the system. That is an administrative command that’s usually executed interactively. Had been a ton of individuals operating ps for some motive? The D script in determine 6 explores this query, with the output proven in determine 7.
This revealed that the ps command was being run with disturbing regularity. (That every invocation was taking almost a second and a half to run was not terribly stunning; with greater than 3,000 processes, one would anticipate a whole course of itemizing to be moderately time consuming.) One wouldn’t anticipate the ps command to be run each ~10 seconds on a standard machine. Who was operating this? And why? Answering this sort of query is a snap with DTrace:
proc:::exec /args[0] == “/usr/bin/ps”/ { @[execname, ustack()] = rely(); }
This enabling aggregates on each the appliance identify and a consumer stack backtrace. Working the above revealed that each one invocations of ps had been in the end coming from an utility named cam, and so they had been all coming from a perform named SendUsageRec.
This was instantly suspicious: Cam is a wrapper program written by Solar’s IT division that’s designed to trace utilization of an underlying utility; one wouldn’t anticipate cam to be doing a lot work in any respect, not to mention launching a ps command. Wanting on the supply to SendUsageRec proven in determine 8 revealed not less than a part of the issue.
In case you’ve carried out a lot Unix techniques programming, this code may provoke a verbal exclamation—like having your toes stepped on or being pricked with a pin. What is that this code doing? The reply requires some rationalization of the cam wrapper. To trace utilization, cam obtains a timestamp and launches the underlying utility; later, when the underlying program exits, cam once more obtains a timestamp, takes the distinction, and sends the full utilization time to a daemon. To cope with the case of an underlying utility that doesn’t exit earlier than the machine is rebooted, cam additionally wakes up each half-hour and executes the (horribly inefficient) code in determine 8 to find out how a lot time the underlying utility has been operating, sending the utilization time to the daemon. (Not solely is that this code horribly inefficient, additionally it is terribly damaged: Chopping from column 13 contains not solely the specified TIME discipline, but in addition the ultimate two characters from the TTY discipline.) On a desktop, which is perhaps operating two or three cam-wrapped purposes, this isn’t an issue. On this server, nonetheless, with its 170 customers, somebody’s half-hour had been up each ten seconds.
Some back-of-the-envelope math revealed that sooner or later—in all probability round 400 customers or so—the server would have (on common) one ps operating on a regular basis. Evidently, neither ps nor its underpinnings within the /proc file system within the kernel had been designed to be in this sort of fixed use. Certainly, after having found this drawback, a number of beforehand mysterious issues within the system—together with abnormally excessive lock competition within the kernel’s course of administration subsystem—started to make way more sense. So resolving this drawback not solely lowered the CPU utilization within the system (thus decreasing latency), but in addition improved scalability (thus bettering throughput).
It is a good instance of the sorts of issues which have been discovered with DTrace: It was a knuckleheaded drawback excessive within the stack of abstraction, and the signs of the issue had been systemic in nature. This drawback couldn’t have been understood when it comes to a single program as a result of it was solely when a number of cases of the identical program had been taken collectively that the systemic drawback might be seen. Though this drawback may need been foolish, the results that it had on the system had been substantial. Maybe most distressing, earlier than DTrace was introduced in, there had been a number of months of intensive (however fruitless) effort spent attempting to know the system. This was not a failing of the sharp individuals who had been beforehand analyzing the system, it was a failing of the boring instruments that that they had been caught utilizing—instruments that didn’t enable them to correlate signs on the backside of the stack with the causes on the high.
Future Work
Whereas DTrace has allowed for brand spanking new levels of system observability, there may be nonetheless a lot work to be carried out. For instance, though our user-level supplier mechanism has allowed for some visibility into dynamic environments, it’s too coarse-grained: With single probes akin to high-frequency occasions similar to getting into a way or perform, the enabled probe impact is simply too excessive when one is concerned about instrumenting solely a small subset of a program. Whereas DTrace can instrument factors of textual content, it has no possible way of recording information (and even arguments) from these environments. These issues are nontrivial; the place they’ve been solved (utilizing bytecode instrumentation or comparable strategies), it’s almost all the time with an answer particular to the language as an alternative of generic to the system.
Past DTrace, new capacities for software program observability have opened up new potential in established fields similar to software program visualization. Specifically, attention-grabbing system visualization work similar to IBM’s PV analysis prototype4 ought to now be potential on manufacturing techniques, dramatically rising their scope and influence.
Lastly, whereas fixing the single-system observability drawback pours a crucial basis, it’s however incremental work on a way more difficult drawback: observing distributed techniques. One probably promising space is combining single-system observability infrastructure with OS-level virtualization technologies5 to acquire distributed debugging throughout the confines of a single machine. Whereas promising, that is in the end an answer restricted to improvement techniques: Observing manufacturing distributed techniques stays an open drawback. One noteworthy physique of labor on this area is Google’s Sawzall,6 a system designed to research logging information throughout many machines. With its filters and aggregators, Sawzall has some comparable notions to DTrace, however differs in that it post-processes information, whereas DTrace processes information in situ. Nonetheless, the confluence of concepts from techniques similar to DTrace and Sawzall could present some progress on the long-standing drawback of distributed techniques observability.
Given the success of DTrace, we anticipate different options to the system observability drawback. To guarantee success, future options ought to abide by the identical constraint: absolute security on manufacturing techniques above all else. Abiding by this constraint—and including mechanisms to instrument the complete stack of abstraction in ways in which spotlight patterns—has allowed for brand spanking new observability the place it’s most badly wanted: in manufacturing techniques, and excessive within the stack of abstraction. With the flexibility to watch the complete stack of operating, manufacturing software program, cow season is open, and there’s no bag restrict. Q
References
- FreeBSDTrace mission; http://www.sitetronics.com/wordpress/
- Cantrill, B., Shapiro, M., and Leventhal, A. 2004. Dynamic instrumentation of manufacturing techniques. Proceedings of the 2004 Usenix Annual Technical Convention.
- Solar Microsystems. 2005. Solaris Dynamic Tracing Information.
- Kimelman, D., Rosenburg, B., and Roth, T. 1997. Visualization of dynamics in real-world software program techniques. In Software program Visualization: Programming as a Multi-Media Expertise, MIT Press.
- Worth, D., and Tucker, A. 2004. Solaris zones: Working system help for consolidating industrial workloads. Proceedings of the 18th Usenix LISA Convention.
- Pike, R., Dorward, S., Griesemet, R., and Quinlan, S. Forthcoming. Decoding the info: Parallel evaluation with Sawzall. Scientific Programming Journal.
BRYAN CANTRILL is a senior employees engineer within the Solaris Kernel Improvement Group at Solar Microsystems. His pursuits embody dynamic software program instrumentation, postmortem diagnosability, and realtime kernel and microprocessor structure. Most just lately, he (and two colleagues) designed, carried out, and shipped DTrace, a facility for systemic dynamic instrumentation of Solaris. Cantrill obtained a Sc.B. in pc science from Brown College.
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