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Wide area distributed cloud computing, where data are processed and stored at micro datacenters (P PDCs) located around terminals, improves response time and reduces the amount of traffic in wide area networks. However, it is difficult for the cloud to maintain service in the case of PDC failure because the robustness of PDCs is substantially lower than that of conventional DCs. We propose a flow control system that transfers the flow to other PDCs by converting the L2, L3, and L4 headers of received packets via OpenFlow when PDC failures occur. The proposed system enables flow to be transferred to other PDCs even from M2M terminals such as sensors that are unable to change the destination IP address. We also developed a distributed control system to solve the critical problem of switching delay caused by the packet-in mechanism used when applying the OpenFlow to the distributed cloud. Emulation results showed that throughput in the distributed control became stable 12 times as quickly as that in the centralized control when flow was transferred.

Genetic Network Programming (GNP) extended from other evolutionary computations such as Genetic Algorithm (GA) and Genetic Programming (GP) has network structures as gene. Previously, the program size of conventional GNP was fixed and GNP programs have not introduced the concept of sub-routines, although GA and GP paid attention to sub-routines. In this paper, a new method where GNP with Automatically Generated Macro Nodes (GNP with AGMs) composed of a number of nodes is proposed for improving the performance of GNP. These AGMs also have network structures and are evolved like main GNP. In addition to that, AGMs have multiple inputs and outputs that have not been introduced in the past. In the simulations, comparisons between GNP program only and GNP with AGMs are carried out using the tile world. Simulation results shows that the proposed method brings better results compared with traditional GNP. And it is clarified from simulations that the node transition rules obtained by AGMs show the generalized rules able to deal with unknown environments.ADFs)

This research investigates service creation in/after effect of coronavirus pandemic targeting the essential business environment. It follows prevention through design approach to facilitate business owners to maintain their business environments at low COVID contraction risks, for both customers and staff. The effectiveness of recommended prevention practices (like social distancing and hand-sanitising) is uncertain at public workplaces, simply due to inevitable workers and customers interactions. Such uncertainty, especially in cases of retail stores and hospitals, raises a need for the design of services and support systems for common/necessary public business activities to reduce the burden on people involved. This research investigates the risk-related metrics to realise such digital services, focussing on three types: congestion at the work environment, disinfection of store area/objects, and sanitisation of people and staffs involved. Based on this, a digital technology-based service COVSAFE was created and tested through a proof-of-concept implementation for a supermarket business environment. This implementation and its evaluations highlight the bottlenecks/challenges for realising this system in everyday scenarios.

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