Transferable Lessons from Biological and Supply Chain Networks to Autonomic Computing

Abstract

Autonomic computing undoubtedly represents the solution for dealing with the complexity of modern computing systems, driven by ever increasing user needs and requirements. The design and management of autonomic computing systems must be performed both rigorously and carefully. Valuable lessons in this direction can be learned from biological and supply chain networks. This paper identifies and discusses but a few transferable lessons from biological and supply chain networks to autonomic computing. Characteristics such as structural and operational complexity and the agent information processing capabilities are considered for biological and supply chain networks. The relevance of the performance measures and their impact on the design, management and performance of autonomic computing systems are also considered. For example, spare resources are often found in biological systems. On the other hand, spare resources are frequently considered a must-not property in designed systems, due to the additional costs associated with them. Several of the lessons include the fact that a surprisingly low number of types of elementary agents exist in many biological systems, and that architectural and functional complexity and dependability are achieved through complex and hierarchical connections between a large number of such agents. Lessons from supply chains include the fact that, when designed and managed appropriately, complexity becomes a value-adding property that can bring system robustness and flexibility in meeting the users’ needs. Furthermore, information-theoretic methods and case-study results have shown that an integrated supply chain requires in-built spare capacity if it is to remain manageable.