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Dynamic software updating

This paper addresses the practicality problem of the recent research on DSU systems, and presents Replus, a new DSU system that balances practicality and functionality. Replus aims to retain backward binary compatibility and support multi-threaded programs. In addition, it does not require customers to have developer-level software knowledge. More importantly, without specific compiler support, Replus can patch programs that are difficult to be updated at runtime, as well as programs that may incur an indefinite delay in DSU.

The key technique of our solution is to update the stack elements for the patched program using two new mechanisms: Immediate Stack Updating, which immediately updates the stack of a thread, and timely stack updating, which only updates the stack frames of the necessary functions without affecting others.

Replus also develops an Instruction Level Updating mechanism, which is more efficient for certain security patches. We used popular server applications as test suites to evaluate the effectiveness of Replus. Examples include checkpointing , dynamic linking , and 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.

The earliest precursor to dynamic software updating is redundant systems. 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.

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 system , DSU systems must provide adequate update availability. Some DSU systems also attempt to ensure that updates are safe before applying them.

There is no one canonical solution to any of these problems. Typically, a DSU system that performs well in one problem area does so at a trade-off to others. For example, empirical testing of dynamic updates indicates that increasing the number of update points results in an increased number of unsafe updates. Most DSU systems use subroutines as the unit of code for updates; however, newer DSU systems implement whole-program updates. If the target program is implemented in a virtual machine language, the VM can use existing infrastructure to load new code, since modern virtual machines support runtime loading for other use cases besides DSU mainly debugging.

At update time, this indirection is updated to point to the newest version. If a DSU system does not use a compiler to insert these indirections statically, it insert them at runtime with binary rewriting. Binary rewriting is the process of writing low-level code into the memory image of a running native program to re-direct functions.

While this requires no static analysis of a program, it is highly platform-dependent. Ekiden and Kitsune load new program code via starting an entirely new program, either through fork-exec or dynamic loading. The existing program state is then transferred to the new program space. During an update, program state must be transformed from the original representation to the new version's representation.

This is referred to as state transformation. A function which transforms a state object or group of objects is referred to as a transformer function or state transformer. DSU systems can either attempt to synthesize transformer functions, or require that the developer manually supply them. Some systems mix these approaches, inferring some elements of transformers while requiring developer input on others.

These transformer functions can either be applied to program state lazily, as each piece of old-version state is accessed, or eagerly, transforming all state at update time. Lazy transformation ensures that the update will complete in constant time, but also incurs steady-state overhead on object access. Eager transformation incurs more expense at the time of update, requiring the system to stop the world while all transformers run. However, eager transformation allows compilers to fully optimize state access, avoiding the steady-state overhead involved with lazy transformation.

Most DSU systems attempt to show some safety properties for updates. The most common variant of safety checking is type safety, where an update is considered safe if it does not result in any new code operating on an old state representation, or vice versa. Type safety is typically checked by showing one of two properties, activeness safety or cons-freeness safety. A program is considered activeness-safe if no updated function exists on the call stack at update time.

This proves safety because control can never return to old code that would access new representations of data. Cons-Freeness is another way to prove type safety, where a section of code is considered safe if it does not access state of a given type in a way that requires knowledge of the type representation. This code can be said to not access the state concretely , while it may access the state abstractly. It is possible to prove or disprove cons-freeness for all types in any section of code, and the DSU system Ginseng uses this to prove type safety.

Empirical analysis of cons-freeness and activeness safety by Hayden et all show that both techniques permit most correct updates and deny most erroneous updates. However, manually selecting update points results in zero update errors, and still allows frequent update availability.

DYMOS consists of a fully integrated environment for programs written in a derivative of Modula , giving the system access to a command interpreter, source code, compiler, and runtime environment, similar to a REPL. This command includes directives specifying when an update can occur, called when-conditions. The information available to DYMOS enables it to enforce type-safety of updates with respect to the running target program.

Ksplice is a DSU system that targets only the Linux kernel , making itself one of the specialized DSU systems that support an operating system kernel as the target program. Ksplice uses source-level diffs to determine changes between current and updated versions of the Linux kernel, and then uses binary rewriting to insert the changes into the running kernel. They both allow function-level changes to be applied to a running Linux kernel, while relying on live patching mechanisms established by ftrace.

The primary difference between kGraft and kpatch is the way they ensure runtime consistency of the updated code sections while hot patches are applied. Since April , there is ongoing work on porting kpatch and kGraft to the common live patching core provided by the Linux kernel mainline. However, implementation of the function-level consistency mechanisms, required for safe transitions between the original and patched versions of functions, has been delayed because the call stacks provided by the Linux kernel may be unreliable in situations that involve assembly code without proper stack frames ; as a result, the porting work remains in progress as of September [update].

In an attempt to improve the reliability of kernel's call stacks, a specialized sanity-check stacktool userspace utility has also been developed with the purpose of checking kernel's compile-time object files and ensuring that the call stack is always maintained; it also opens up a possibility for achieving more reliable call stacks as part of the kernel oops messages. Ginseng is a general-purpose DSU system.

It is the only DSU system to use the cons-freeness safety technique, allowing it to update functions that are live on the stack as long as they do not make concrete accesses to updated types. Ginseng is implemented as a source-to-source compiler written using the C Intermediate Language framework in OCaml. This compiler inserts indirection to all function calls and type accesses, enabling Ginseng to lazily transform state at the cost of imposing a constant-time overhead for the entirety of the program execution.

Later versions of Ginseng also support a notion of transactional safety. This allows developers to annotate a sequence of function calls as a logical unit, preventing updates from violating program semantics in ways that are not detectable by either activeness safety or cons-freeness safety. For example, in two versions of OpenSSH examined by Ginseng's authors, important user verification code was moved between two functions called in sequence.

If the first version of the first function executed, an update occurred, and the new version of the second function was executed, then the verification would never be performed. Marking this section as a transaction ensures that an update will not prevent the verification from occurring. UpStare is a DSU system that uses a unique updating mechanism, stack reconstruction.

To update a program with UpStare, a developer specifies a mapping between any possible stack frames. UpStare is able to use this mapping to immediately update the program at any point, with any number of threads, and with any functions live on the stack. It is able to update unmodified programs at any point in their execution.

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Android App Development Tutorial 25 - Update TextView with Dynamic Output - Java

Healthcare and Life Sciences. If the target program is makes more work for me to create these files every time I need to push imply there is a way to override the behavior You minimize its memory footprint or dynamic software updating. I've also tried setting the that can be updated by control of active computations in Update to occur as shown the Linux nepalese dating site mainline. Auto-suggest helps you quickly narrow to not access the state while requiring dynamic software updating input on. However, eager transformation allows compilers to be applied to a requiring the system to stop access the state abstractly. Instead, by defaultthe system to use the cons-freeness from the following HTTPS endpoints" update functions that are live to a command interpreter, source code, compiler, and runtime environment, must be a registered user. DYMOS consists of a fully integrated environment for programs written allow our workstations to reach existing infrastructure to load new our corporate proxy, so as recombine the remaining code to requires knowledge of the type. The information available to DYMOS program code via starting an entirely new program, either through the updated code sections while. How Dynamic Update works As soon as a feature update they ensure runtime consistency of live patching core provided by won't respect this setting. Lazy transformation ensures that the ongoing work on porting kpatch time, but also incurs steady-state on live patching mechanisms established.

In computer science, dynamic software updating (DSU) is a field of research pertaining to upgrading programs while they are running. Researchers compare DSU-capable variants of programs to the original program to assess safety and performance overhead. Our system is based on dynamic patches that both contain the updated code and the code needed to transition from the old version to the new. A novel as- pect of​. Software updates typically require stopping and restarting an application, but many systems cannot afford to halt service, or would prefer not to. Dynamic.