A distributed optimization method for the geographically distributed data centres problem

The geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method

Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations

The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents

Efficient Routing for Disaster Scenarios in Uncertain Networks: A Computational Study of Adaptive Algorithms for the Stochastic Canadian Traveler Problem with Multiple Agents and Destinations

The primary objective of this research is to develop adaptive online algorithms for solving the Canadian Traveler Problem (CTP), which is a well-studied problem in the literature that has important applications in disaster scenarios. To this end, we propose two novel approaches, namely Maximum Likely Node (MLN) and Maximum Likely Path (MLP), to address the single-agent single-destination variant of the CTP. Our computational experiments demonstrate that the MLN and MLP algorithms together achieve new best-known solutions for 10,715 instances. In the context of disaster scenarios, the CTP can be extended to the multiple-agent multiple-destination variant, which we refer to as MAD-CTP. We propose two approaches, namely MAD-OMT and MAD-HOP, to solve this variant. We evaluate the performance of these algorithms on Delaunay and Euclidean graphs of varying sizes, ranging from 20 nodes with 49 edges to 500 nodes with 1500 edges. Our results demonstrate that MAD-HOP outperforms MAD-OMT by a considerable margin, achieving a replan time of under 9 seconds for all instances. Furthermore, we extend the existing state-of-the-art algorithm, UCT, which was previously shown by Eyerich et al. (2010) to be effective for solving the single-source single-destination variant of the CTP, to address the MAD-CTP problem. We compare the performance of UCT and MAD-HOP on a range of instances, and our results indicate that MAD-HOP offers better performance than UCT on most instances. In addition, UCT exhibited a very high replan time of around 10 minutes. The inferior results of UCT may be attributed to the number of rollouts used in the experiments but increasing the number of rollouts did not conclusively demonstrate whether UCT could outperform MAD-HOP. This may be due to the benefits obtained from using multiple agents, as MAD-HOP appears to benefit to a greater extent than UCT when information is shared among agents

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents\u27 track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified

Planning graph heuristics for selecting objectives in over-subscription planning problems

Partial Satisfaction or Over-subscription Planning problems arise in many real world applications. Applications in which the planning agent does not have enough resources to accomplish all of their given goals, requiring plans that satisfy only a subset of them. Solving such partial satisfaction planning (PSP) problems poses several challenges, from new models for handling plan quality to efficient heuristics for selecting the most beneficial goals. In this paper, we extend planning graph-based reachability heuristics with mutex analysis to overcome complex goal interactions in PSP problems. We start by describing one of the most general PSP problems, the PSP NET BENEFIT problem, where actions have execution costs and goals have utilities. Then, we present AltWlt, 1 our heuristic approach augmented with a multiple goal set selection process and mutex analysis. Our empirical studies show that AltWlt is able to generate the most beneficial solutions, while incurring only a small fraction of the cost of other PSP approaches