Dynamic software updating system
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Depending on the sysfem familiarity with the application, and the application's own degree of complexity, adding support for DSU using Kitsune can range from mild to difficult. In our own experience, virtually all of the work required to add Kitsune integration with an existing project occurs in the first version. After this point, the Kitsune support code becomes a separate concern. This code changes very little over time. What programs can be updated with Kitsune?
Potentially, any C program can be updated with Kitsune. Kitsune doesn't change process ID's on an update, so any resources allocated by the operating system will persist after an update. At face value, this does not seem to differentiate Kitsune from other C updating systems, like Ginseng or the more recent UpStare. However, both of those systems require target programs to pass a static analysis that may be over-conservative. They also perform significant source-to-source compilation of the original program, which adds overhead and can inhibit compiler optimizations. Kitsune is much gentler on the host application, requiring no static analysis, and very little potentially no source-to-source rewriting.
Can Kitsune update multithreaded programs? Half of Kitsune's benchmark programs are multithreaded: Memcached, Icecast, and Snort. How much overhead does Kitsune add to normal execution? In our experience, Kitsune introduces essentially zero overhead while your program is executing normally. How long does it take to update a program from one version to the next? In general, updates take place very quickly. In our benchmarks, the time from when a program first signals that an update is required, to the time the program transfers control into Kitsune, to the time when the new version of the program has taken over and finished initializing all state, is generally less than ms, and less than ms in the worst cases.
In general, the time for a dynamic update depends on the structure of the program, and the nature of the change. Updating systems differ in the types of program changes that they support. For example, Ksplice only supports code changes in functions, and does not support changes to state representation. This is because Ksplice primarily targets security changes, rather than general updates. In contrast, Ekiden can update a program to any other program capable of being executed, even one written in a different programming language. Systems designers can extract valuable performance or safety assurances by limiting the scope of updates.
For example, any update safety check limits the scope of updates to updates which pass that safety check. The mechanism used to transform code and state influences what kinds of updates a system will support. DSU systems, as tools, can also be evaluated on their ease-of-use and clarity to developers. Many DSU systems, such as Ginsengrequire programs to pass various static analyses. While these analyses prove properties of programs that are valuable for DSU, they are by nature sophisticated and difficult to understand. DSU systems that do not use a static analysis might require use of a specialized compiler.
Some DSU systems require neither static analysis nor specialty compilers. Programs that are updated by a DSU system are referred to as target programs. Academic publications of DSU systems commonly include several target programs as case studies. Since few programs are written with support for dynamic updating in mind, retrofitting existing programs is a valuable means of evaluating a DSU system for practical use.
Overall, the key of finding and commoner updated design values is automated. Particularly, system deposit-downs caused by registration bonuses could not be removed when high quality is required.
Related fields[ edit ] The problem space addressed by dynamic updating can be thought of as an intersection of several others. Examples include checkpointingdynamic linkingand persistence. As an example, a database that must be backward-compatible with previous versions of its on-disk file format, must accomplish the same type of state transformation expected of a dynamic updating system. Likewise, a program that has a plugin architecture, must be able to load and execute new code at runtime. Similar techniques are sometimes also employed for the purpose of dynamic dead-code elimination to remove conditionally dead or unreachable code at load or runtime, and recombine the remaining code to minimize its memory footprint or improve speed.
In a redundant environment, spare systems exist ready to take control of active computations in the event of a failure of the main system. These systems contain a main machine and a hot spare. The hot spare would be periodically seeded with a checkpoint of the primary system. In the event of a failure, the hot spare would take over, and the main machine would become the new hot spare. This pattern can be generalized to updating. In the event of an update, the hot spare would activate, the main system would update, and then the updated system would resume control. Implementation[ edit ] DSU systems must load new code into a running program, and transform existing state into a format that is understood by the new code.
Since many motivational use-cases of DSU are time-critical for example, deploying a security fix on a live and vulnerable systemDSU systems must provide adequate update availability.
Some DSU systems also attempt to softwzre that updates are safe before applying them. There is no one canonical solution to any of these problems. From that point on, that process will always use the patched code. Once the flag is set on all processes, kGraft drops the now-redundant indirection and jumps straight to the patched code. The problem with multi-version execution is that processes running two different code versions could interact, e. Given that the original data-structure has no space for new fields, the idea is to create a separate data-structure just for them and rewrite the original binary code to use the new structure when manipulating the new fields.
This approach, however, introduces a lot of complexity and opportunities for bugs as the code is maintained and patched further, going ahead. Full-featured DSU Linux 4.
Updating Dynamic system software
The research community has been looking at how to support highly-flexible Softaare for many years now. In this approach, the entirety of the new code is loaded into the memory of the running process and then control and data migrations directly update the execution state softawre to, or even in conjunction with, subsequent Dnamic that code. Kitsune and Rubah We have developed two systems that take the whole-process approach: Kitsunefor C programs, and Rubahfor Java programs. Besides their flexibility, these systems do not impose any measurable overhead on normal execution. First, they require the programmer to add update points to the program. These are points at which the program polls to see whether a dynamic update is available, and if so will start applying it.
For example, the state transformer might provide the default value for a new field. Fortunately, the process of finding and updating updated data values is automated. Kitsune updates all data at once, using a garbage collector-style mechanism.