Embedded Social Insect-Inspired Intelligence Networks for System-level Runtime Management

Large-scale distributed computing architectures such as, e.g. systems on chip or many-core devices, offer advantages over monolithic or centralised single-core systems in terms of speed, power/thermal performance and fault tolerance. However, these are not implicit properties of such systems and runtime management at software or hardware level is required to unlock these features. Biological systems naturally present such properties and are also adaptive and scalable. To consider how these can be similarly achieved in hardware may be beneficial. We present Social Insect behaviours as a suitable model for enabling autonomous runtime management (RTM) in many-core architectures. The emergent properties sought to establish are self-organisation of task mapping and systemlevel fault tolerance. For example, large social insect colonies accomplish a wide range of tasks to build and maintain the colony. Many thousands of individuals, each possessing relatively little intelligence, contribute without any centralised control. Hence, it would seem that social insects have evolved a scalable approach to task allocation, load balancing and robustness that can be applied to large many-core computing systems. Based on this, a self-optimising and adaptive, yet fundamentally scalable, design approach for many-core systems based on the emergent behaviours of social-insect colonies are developed. Experiments capture decision-making processes of each colony member to exhibit such high-level behaviours and embed these decision engines within the routers of the many-core system

Social Insect-Inspired Adaptive Hardware

Modern VLSI transistor densities allow large systems to be implemented within a single chip. As technologies get smaller, fundamental limits of silicon devices are reached resulting in lower design yields and post-deployment failures. Many-core systems provide a platform for leveraging the computing resource on offer by deep sub-micron technologies and also offer high-level capabilities for mitigating the issues with small feature sizes. However, designing for many-core systems that can adapt to in-field failures and operation variability requires an extremely large multi-objective optimisation space. When a many-core reaches the size supported by the densities of modern technologies (thousands of processing cores), finding design solutions in this problem space becomes extremely difficult. Many biological systems show properties that are adaptive and scalable. This thesis proposes a self-optimising and adaptive, yet scalable, design approach for many-core based on the emergent behaviours of social-insect colonies. In these colonies there are many thousands of individuals with low intelligence who contribute, without any centralised control, to complete a wide range of tasks to build and maintain the colony. The experiments presented translate biological models of social-insect intelligence into simple embedded intelligence circuits. These circuits sense low-level system events and use this manage the parameters of the many-core's Network-on-Chip (NoC) during runtime. Centurion, a 128-node many-core, was created to investigate these models at large scale in hardware. The results show that, by monitoring a small number of signals within each NoC router, task allocation emerges from the social-insect intelligence models that can self-configure to support representative applications. It is demonstrated that emergent task allocation supports fault tolerance with no extra hardware overhead. The response-threshold decision making circuitry uses a negligible amount of hardware resources relative to the size of the many-core and is an ideal technology for implementing embedded intelligence for system runtime management of large-complexity single-chip systems

Operating Beyond FPGA Tool Limitations : Nervous Systems for Embedded Runtime Management

Fabrication issues throttle VLSI designs with pes- simistic design constraints and speed-grade device binning nec- essary to avoid failure of devices. We propose that a on chip monitoring system (a Nervous System) can reduce this margin by automatically sensing and reacting to failures and environmental changes at runtime. We demonstrate that pessimistic margins in the FPGA tools allow our test circuit to be overclocked by twice the maximum design tool frequency and run at 50 °C above its maximum operating temperature without error. The Configurable Intelligence Array is introduced as a low-overhead intelligence platform and used for a prototype neural circuit that can close the loop between a timing-fault detector and a programmable Phase Locked Loop (PLL) oscillator

HSO: A Hybrid Swarm Optimization Algorithm for Re-Ducing Energy Consumption in the Cloudlets

Mobile Cloud Computing (MCC) is an emerging technology for the improvement of mobile service quality. MCC resources are dynamically allocated to the users who pay for the resources based on their needs. The drawback of this process is that it is prone to failure and demands a high energy input. Resource providers mainly focus on resource performance and utilization with more consideration on the constraints of service level agreement (SLA). Resource performance can be achieved through virtualization techniques which facilitates the sharing of resource providers’ information between different virtual machines. To address these issues, this study sets forth a novel algorithm (HSO) that optimized energy efficiency resource management in the cloud; the process of the proposed method involves the use of the developed cost and runtime-effective model to create a minimum energy configuration of the cloud compute nodes while guaranteeing the maintenance of all minimum performances. The cost functions will cover energy, performance and reliability concerns. With the proposed model, the performance of the Hybrid swarm algorithm was significantly increased, as observed by optimizing the number of tasks through simulation, (power consumption was reduced by 42%). The simulation studies also showed a reduction in the number of required calculations by about 20% by the inclusion of the presented algorithms compared to the traditional static approach. There was also a decrease in the node loss which allowed the optimization algorithm to achieve a minimal overhead on cloud compute resources while still saving energy significantly. Conclusively, an energy-aware optimization model which describes the required system constraints was presented in this study, and a further proposal for techniques to determine the best overall solution was also made

Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant

The Victoria Amazonica plant, often known as the Giant Water Lily, has the largest floating spherical leaf in the world, with a maximum leaf diameter of 3 meters. It spreads its leaves by the force of its spines and creates a large shadow underneath, killing any plants that require sunlight. These water tyrants use their formidable spines to compel each other to the surface and increase their strength to grab more space from the surface. As they spread throughout the pond or basin, with the earliest-growing leaves having more room to grow, each leaf gains a unique size. Its flowers are transsexual and when they bloom, Cyclocephala beetles are responsible for the pollination process, being attracted to the scent of the female flower. After entering the flower, the beetle becomes covered with pollen and transfers it to another flower for fertilization. After the beetle leaves, the flower turns into a male and changes color from white to pink. The male flower dies and sinks into the water, releasing its seed to help create a new generation. In this paper, the mathematical life cycle of this magnificent plant is introduced, and each leaf and blossom are treated as a single entity. The proposed bio-inspired algorithm is tested with 24 benchmark optimization test functions, such as Ackley, and compared to ten other famous algorithms, including the Genetic Algorithm. The proposed algorithm is tested on 10 optimization problems: Minimum Spanning Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color Quantization, and Image Segmentation and compared to traditional and bio-inspired algorithms. Overall, the performance of the algorithm in all tasks is satisfactory.Comment: 45 page

Personal mobile grids with a honeybee inspired resource scheduler

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The overall aim of the thesis has been to introduce Personal Mobile Grids (PMGrids) as a novel paradigm in grid computing that scales grid infrastructures to mobile devices and extends grid entities to individual personal users. In this thesis, architectural designs as well as simulation models for PM-Grids are developed. The core of any grid system is its resource scheduler. However, virtually all current conventional grid schedulers do not address the non-clairvoyant scheduling problem, where job information is not available before the end of execution. Therefore, this thesis proposes a honeybee inspired resource scheduling heuristic for PM-Grids (HoPe) incorporating a radical approach to grid resource scheduling to tackle this problem. A detailed design and implementation of HoPe with a decentralised self-management and adaptive policy are initiated. Among the other main contributions are a comprehensive taxonomy of grid systems as well as a detailed analysis of the honeybee colony and its nectar acquisition process (NAP), from the resource scheduling perspective, which have not been presented in any previous work, to the best of our knowledge. PM-Grid designs and HoPe implementation were evaluated thoroughly through a strictly controlled empirical evaluation framework with a well-established heuristic in high throughput computing, the opportunistic scheduling heuristic (OSH), as a benchmark algorithm. Comparisons with optimal values and worst bounds are conducted to gain a clear insight into HoPe behaviour, in terms of stability, throughput, turnaround time and speedup, under different running conditions of number of jobs and grid scales. Experimental results demonstrate the superiority of HoPe performance where it has successfully maintained optimum stability and throughput in more than 95% of the experiments, with HoPe achieving three times better than the OSH under extremely heavy loads. Regarding the turnaround time and speedup, HoPe has effectively achieved less than 50% of the turnaround time incurred by the OSH, while doubling its speedup in more than 60% of the experiments. These results indicate the potential of both PM-Grids and HoPe in realising futuristic grid visions. Therefore considering the deployment of PM-Grids in real life scenarios and the utilisation of HoPe in other parallel processing and high throughput computing systems are recommended

Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

Inspired Design: Using Interdisciplinarity And Biomimicry For Software Innovation

This thesis presents research and proposes a framework for increasing the breadth and depth of interdisciplinary knowledge in the field of computer science. The intent is to address an increasing problem of complexity in software and computing systems. The approach is to equip software developers and computer scientists with a contextual perspective and a set of strategies for injecting innovation and creativity into the solutions they design by leveraging knowledge and models outside the traditional realm of computer science. A review of current and historical forms of interdisciplinarity and biomimicry are presented to build that context. The strategies presented include interdisciplinary education, interdisciplinary collaboration, interdisciplinary tools, biomimetic design, and the creation of new pattern languages based on nature\u27s design solutions. Each of these strategies stems from and leads to an open exchange of knowledge across disciplinary boundaries. When taken together, the knowledge and strategies presented are intended to inspire and foster a paradigm that recognizes the value of human and natural diversity as a source of innovation