Simulated Annealing Algorithm Combined with Chaos for Task Allocation in Real-Time Distributed Systems

This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality

Presentation an Approach for Placement Phase in Mapping Algorithm

The data requirements of both scientific and commercial applications have been increasing drastically in recent years. Just a couple of years ago, the data requirements for an average scientific application were measured in terabytes, whereas today we use petabytes to measure them. Moreover, these data requirements continue to increase rapidly every year, and in less than a decade they are expected to reach the exabyte (1 million terabytes) scale.. In this work, the data duplication technique has not been used by us. That’s because of increase in costs and expenses of using a cloud system.In this paper, an approach to mapping workflow tasks and data between cloud system data centers has been presented. This approach encompasses 2 phases: both of which both have been given enough input to appropriately map tasks and data between data centers in such a way that the total time for task execution and data movement becomes minimal. In other words, the goal of mentioned approach is to present a trade-off between these two Goals. Simulations have demonstrated that the said approach can fulfill stated goals effectively. Keywords:Distributed system, scientific application, application, data requirement

Bio-Inspired Load Balancing In Large-Scale WSNs Using Pheromone Signalling

Wireless sensor networks (WSNs) consist of multiple, distributed nodes each with limited resources. With their strict resource constraints and application-specific characteristics, WSNs contain many challenging tradeoffs. This paper proposes a bioinspired load balancing approach, based on pheromone signalling mechanisms, to solve the tradeoff between service availability and energy consumption. We explore the performance consequences of the pheromone-based load balancing approach using (1) a system-level simulator, (2) deployment of real sensor testbeds to provide a competitive analysis of these evaluation methodologies. The effectiveness of the proposed algorithm is evaluated with different scenario parameters and the required performance evaluation techniques are investigated on case studies based on sound sensors

An adaptive multi-population differential artificial bee colony algorithm for many-objective service composition in cloud manufacturing

Several conflicting criteria must be optimized simultaneously during the service composition and optimal selection (SCOS) in cloud manufacturing, among which tradeoff optimization regarding the quality of the composite services is a key issue in successful implementation of manufacturing tasks. This study improves the artificial bee colony (ABC) algorithm by introducing a synergetic mechanism for food source perturbation, a new diversity maintenance strategy, and a novel computing resources allocation scheme to handle complicated many-objective SCOS problems. Specifically, differential evolution (DE) operators with distinct search behaviors are integrated into the ABC updating equation to enhance the level of information exchange between the foraging bees, and the control parameters for reproduction operators are adapted independently. Meanwhile, a scalarization based approach with active diversity promotion is used to enhance the selection pressure. In this proposal, multiple size adjustable subpopulations evolve with distinct reproduction operators according to the utility of the generating offspring so that more computational resources will be allocated to the better performing reproduction operators. Experiments for addressing benchmark test instances and SCOS problems indicate that the proposed algorithm has a competitive performance and scalability behavior compared with contesting algorithms

IST Austria Thesis

Social insect colonies tend to have numerous members which function together like a single organism in such harmony that the term ``super-organism'' is often used. In this analogy the reproductive caste is analogous to the primordial germ cells of a metazoan, while the sterile worker caste corresponds to somatic cells. The worker castes, like tissues, are in charge of all functions of a living being, besides reproduction. The establishment of new super-organismal units (i.e. new colonies) is accomplished by the co-dependent castes. The term oftentimes goes beyond a metaphor. We invoke it when we speak about the metabolic rate, thermoregulation, nutrient regulation and gas exchange of a social insect colony. Furthermore, we assert that the super-organism has an immune system, and benefits from ``social immunity''. Social immunity was first summoned by evolutionary biologists to resolve the apparent discrepancy between the expected high frequency of disease outbreak amongst numerous, closely related tightly-interacting hosts, living in stable and microbially-rich environments, against the exceptionally scarce epidemic accounts in natural populations. Social immunity comprises a multi-layer assembly of behaviours which have evolved to effectively keep the pathogenic enemies of a colony at bay. The field of social immunity has drawn interest, as it becomes increasingly urgent to stop the collapse of pollinator species and curb the growth of invasive pests. In the past decade, several mechanisms of social immune responses have been dissected, but many more questions remain open. I present my work in two experimental chapters. In the first, I use invasive garden ants (*Lasius neglectus*) to study how pathogen load and its distribution among nestmates affect the grooming response of the group. Any given group of ants will carry out the same total grooming work, but will direct their grooming effort towards individuals carrying a relatively higher spore load. Contrary to expectation, the highest risk of transmission does not stem from grooming highly contaminated ants, but instead, we suggest that the grooming response likely minimizes spore loss to the environment, reducing contamination from inadvertent pickup from the substrate. The second is a comparative developmental approach. I follow black garden ant queens (*Lasius niger*) and their colonies from mating flight, through hibernation for a year. Colonies which grow fast from the start, have a lower chance of survival through hibernation, and those which survive grow at a lower pace later. This is true for colonies of naive and challenged queens. Early pathogen exposure of the queens changes colony dynamics in an unexpected way: colonies from exposed queens are more likely to grow slowly and recover in numbers only after they survive hibernation. In addition to the two experimental chapters, this thesis includes a co-authored published review on organisational immunity, where we enlist the experimental evidence and theoretical framework on which this hypothesis is built, identify the caveats and underline how the field is ripe to overcome them. In a final chapter, I describe my part in two collaborative efforts, one to develop an image-based tracker, and the second to develop a classifier for ant behaviour

Computational Frameworks for Multi-Robot Cooperative 3D Printing and Planning

This dissertation proposes a novel cooperative 3D printing (C3DP) approach for multi-robot additive manufacturing (AM) and presents scheduling and planning strategies that enable multi-robot cooperation in the manufacturing environment. C3DP is the first step towards achieving the overarching goal of swarm manufacturing (SM). SM is a paradigm for distributed manufacturing that envisions networks of micro-factories, each of which employs thousands of mobile robots that can manufacture different products on demand. SM breaks down the complicated supply chain used to deliver a product from a large production facility from one part of the world to another. Instead, it establishes a network of geographically distributed micro-factories that can manufacture the product at a smaller scale without increasing the cost. In C3DP, many printhead-carrying mobile robots work together to print a single part cooperatively. While it holds the promise to mitigate issues associated with gantry-based 3D printers, such as lack of scalability in print size and print speed, its realization is challenging because existing studies in the relevant literature do not address the fundamental issues in C3DP that stem from the amalgamation of the mobile nature of the robots, and continuous nature of the manufacturing tasks. To address this challenge, this dissertation asks two fundamental research questions: RQ1) How can the traditional 3D printing process be transformed to enable multi-robot cooperative AM? RQ2) How can cooperative manufacturing planning be realized in the presence of inherent uncertainties in AM and constraints that are dynamic in both space and time? To answer RQ1, we discretize the process of 3D printing into multiple stages. These stages include chunking (dividing a part into smaller chunks), scheduling (assigning chunks to robots and generating print sequences), and path and motion planning. To test the viability of the approach, we conducted a study on the tensile strength of chunk-based parts to examine their mechanical integrity. The study demonstrates that the chunk-based part can be as strong as the conventionally 3D-printed part. Next, we present different computational frameworks to address scheduling issues in C3DP. These include the development of 1) the world-first working strategy for C3DP, 2) a framework for automatic print schedule generation, evaluation, and validation, and 3) a resource-constrained scheduling approach for C3DP that uses a meta-heuristic approach such as a modified Genetic Algorithm (MGA) and a new algorithm that uses a constraint-satisficing approach to obtain collision-free print schedules for C3DP. To answer RQ2, a multi-robot decentralized approach based on a simple set of rules is used to plan for C3DP. The approach is resilient to uncertainties such as variation in printing times and can even outperform the centralized approach that uses MGA with a conflict-based search for large-scale problems. By answering these two fundamental questions, the central objective of the research project to establish computational frameworks to enable multi-robot cooperative manufacturing was achieved. The search for answers to the RQs led to the development of novel concepts that can be used not only in C3DP, but many other manufacturing tasks, in general, requiring cooperation among multiple robots

University of Michigan Undergraduate Research Journal, Issue 9, Winter 2012

Articlehttp://deepblue.lib.umich.edu/bitstream/2027.42/97010/1/UMURJ-Issue09_2012.pd

Efficient Learning Machines

Computer scienc