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Self-organizing agent communities for autonomic resource management

机译：自组织代理社区以进行自主资源管理

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The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the runtime reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system's scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions. In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximized when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organizes into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimize any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes.
机译：自主计算范式通过建议它们由许多自治组件组成，从而解决了日益复杂的软件系统所带来的操作难题，每个组件负责其专用硬件和软件组件的运行时重新配置。因此，整个软件系统的调节已成为这些自主系统元素进行局部适应和学习的新兴属性。为此类系统的组件设计适当的本地适应策略仍然是一个重大挑战。当系统的规模和动态性损害了中央执行官的效率和/或阻止各部门汇集信息以实现其谈判和决策的共享，准确的证据基础时，尤其如此。在本文中，我们研究了当对自适应资源消耗/提供计算资源或服务的自主系统元素之间的局部交互作用不断变化时，自调控系统响应是如何自发产生的。我们证明，当所有系统组件都能够彼此自由共享信息时，系统性能不会得到最大化。而是，当单个组件仅对同级组件了解有限时，可以实现最大效率。在这种情况下，系统会自组织成适当的社区结构。通过在社区级别上保持信息流，该系统能够保持足够的稳定性，以在资源受限的环境中有效满足服务需求，从而使任何不必要的重新配置最小化，同时保持足够的适应性，以便能够在服务需求发生变化时进行重新配置。

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