5 research outputs found

    Efficient Dispatch of Multi-object Polymorphic Call Sites in Contextual Role-Oriented Programming Languages

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    Adaptive software becomes more and more important as computing is increasingly context-dependent. Runtime adaptability can be achieved by dynamically selecting and applying context-specific code. Role-oriented programming has been proposed as a paradigm to enable runtime adaptive software by design. Roles change the objects’ behavior at runtime, thus adapting the software to a given context. The cost of adaptivity is however a high runtime overhead stemming from executing compositions of behavior-modifying code. It has been shown that the overhead can be reduced by optimizing dispatch plans at runtime when contexts do not change, but no method exists to reduce the overhead in cases with high context variability. This paper presents a novel approach to implement polymorphic role dispatch, taking advantage of run-time information to effectively guard abstractions and enable reuse even in the presence of variable contexts. The concept of polymorphic inline caches is extended to role invocations. We evaluate the implementation with a benchmark for role-oriented programming languages achieving a geometric mean speedup of 4.0× (3.8× up to 4.5×) with static contexts, and close to no overhead in the case of varying contexts over the current implementation of contextual roles in Object Team

    Using a LowLevel Virtual Machine to improve dynamic aspect support in operating system kernels

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    ABSTRACT Current implementations of software providing dynamic aspect functionality in operating system (OS) kernels are quite restricted in the possible joinpoint types for native code they are able to support. Most of the projects implementing advice for native code use basic technologies adopted from instrumentation methods which allow to provide before, after and around joinpoints for functions. More elaborate joinpoints, however, are not available since support for monitoring native code execution in current CPUs is very restricted without extensive extensions of the compiler toolchain. To realize improved ways of aspect activation in OS kernels, we present an architecture that provides an efficient low-level virtual machine running on top of a microkernel system in cooperation with an aspect deployment service to provide novel ways of aspect activation in kernel environments

    ContextErlang: A language for distributed context-aware self-adaptive applications

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    Self-adaptive software modifies its behavior at run time to satisfy changing requirements in a dynamic environment. Context-oriented programming (COP) has been recently proposed as a specialized programming paradigm for context-aware and adaptive systems. COP mostly focuses on run time adaptation of the application’s behavior by supporting modular descriptions of behavioral variations. However, self-adaptive applications must satisfy additional requirements, such as distribution and concurrency, support for unforeseen changes and enforcement of correct behavior in the presence of dynamic change. Addressing these issues at the language level requires a holistic design that covers all aspects and takes into account the possibly cumbersome interaction of those features, for example concurrency and dynamic change. We present ContextErlang, a COP programming language in which adaptive abstractions are seamlessly integrated with distribution and concurrency. We define ContextErlang’s formal semantics, validated through an executable prototype, and we show how it supports formal proofs that the language design ensures satisfaction of certain safety requirements. We provide empirical evidence that ContextErlang is an effective solution through case studies and a performance assessment. We also show how the same design principles that lead to the development of ContextErlang can be followed to systematically design contextual extensions of other languages. A concrete example is presented concerning ContextScala
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