When reading notes, don't go linearly. Focus on the intro and conclusion to get the meat of the paper. Also, if the paper hasn't already been selected for you by someone, approach it with some skepticism.
Practice active reading: write notes on each paper you read, make references between sections.
You generally have a few types of problems that you need to deal with when working with large systems and change:
Complexity in a large software system is almost inevitable. You have a large number of elements, a large number of interconnections, irregularity in how those connections are formed, lack of a methodical description, and a minimum team size required to understand the system.
Some sources of system complexity are taking on too many objectives, needing high usage of limited resources, and making design decisions that try to be general but are ultimately unnecessary and have to be revisited later.
So in this course, we'll try to approach understanding and controlling complexity through the examination of case studies and existing experience. We'll see how OS design changes in response to technological advances, new application requirements, and advanced system objectives.
OSs abstract system resources, providing access to processes, files, sockets, memory, etc. They are constantly changing, due to shifting hardware and software, and tend to be highly complex for the reasons mentioned above. Combining systems together makes everything even more complicated.
There are a few techniques we can apply to deal with complexity:
-Modularity: Break the implementation up into different components as necessary. -Abstraction: Separate the interface from the specification of the implementation, so changes to the internal details do not propogate. -Hierarchy: Build on modularity by recursively grouping different sets of components based on their purpose(e.g. middleware, workhorses)
The end-to-end argument is that you should not implement something at a low-level if it could be done just as well at a higher level. Consider the example of transferring a file from node A to node B. The end-to-end argument says that there is no need to handle verification at intermediate low level steps when you could just do a final high-level verification step at the end that was necessary anyways. However, this doesn't necessarily apply when you're dealing with very unreliable intermediate steps or extremely large files.
Try to keep things simple. Do one things at a time, and do it well. Make it fast, don't hide power, and leave it to the client. Make these interfaces as stable as possible.
Make an implementation work, and plan to throw one attempt at an implementation away.
Isolate the normal and worst case. Handle the worst case safely, you can do it slow, but the normal case needs to be fast.
The THE: Operating system is cleanly separated into different layers (User programs > buffering of I/O > console > segment controller > processor allocation and interrupt handling). Every layer has a well-defined function and interface. Limits complexity, can test the system nicely using simulated input, can't deadlock since there are no cycles. However, it's really hard to partition things this way.
Monolithic OS: You have processes for each application in user-space, and the OS kernel in kernel-space, which has access to the different resource managers, which in turn have access to the hardware. Common in commercial systems. Very flexible, well-understood, and very performant. Good protection of OS against applications. However, you have no protection between kernel components, you can't safely/easily extend it, and as the OS grows it becomes much more complex.
Open Systems: There is no division between kernel and application space. Apps have direct access to the hardware resources they need. It sounds crazy today, but used to be very common. When resources were extremely limited, you couldn't afford to deal with all those levels of indirection. Very extensible and works for a single user system. However, not very stable and composing extensions leads to much more unpredictable behavior.