Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures

Risk-Aware Planning for Sensor Data Collection

With the emergence of low-cost unmanned air vehicles, civilian and military organizations are quickly identifying new applications for affordable, large-scale collectives to support and augment human efforts via sensor data collection. In order to be viable, these collectives must be resilient to the risk and uncertainty of operating in real-world environments. Previous work in multi-agent planning has avoided planning for the loss of agents in environments with risk. In contrast, this dissertation presents a problem formulation that includes the risk of losing agents, the effect of those losses on the mission being executed, and provides anticipatory planning algorithms that consider risk. We conduct a thorough analysis of the effects of risk on path-based planning, motivating new solution methods. We then use hierarchical clustering to generate risk-aware plans for a variable number of agents, outperforming traditional planning methods. Next, we provide a mechanism for distributed negotiation of stable plans, utilizing coalitional game theory to provide cost allocation methods that we prove to be fair and stable. Centralized planning with redundancy is then explored, planning for parallel task completion to mitigate risk and provide further increased expected value. Finally, we explore the role of cost uncertainty as additional source of risk, using bi-objective optimization to generate sets of alternative plans. We demonstrate the capability of our algorithms on randomly generated problem instances, showing an improvement over traditional multi-agent planning methods as high as 500% on very large problem instances

Multi-objective optimisation of safety-critical hierarchical systems

Achieving high reliability, particularly in safety critical systems, is an important and often mandatory requirement. At the same time costs should be kept as low as possible. Finding an optimum balance between maximising a system's reliability and minimising its cost is a hard combinatorial problem. As the size and complexity of a system increases, so does the scale of the problem faced by the designers. To address these difficulties, meta-heuristics such as Genetic Algorithms and Tabu Search algorithms have been applied in the past for automatically determining the optimal allocation of redundancies in a system as a mechanism for optimising the reliability and cost characteristics of that system. In all cases, simple reliability block diagrams with restrictive assumptions, such as failure independence and limited 2-state failure modes, were used for evaluating the reliability of the candidate designs produced by the various algorithms.This thesis argues that a departure from this restrictive evaluation model is possible by using a new model-based reliability evaluation technique called Hierachically Performed Hazard Origin and Propagation Studies (HiP-HOPS). HiP-HOPS can overcome the limitations imposed by reliability block diagrams by providing automatic analysis of complex engineering models with multiple failure modes. The thesis demonstrates that, used as the fitness evaluating component of a multi-objective Genetic Algorithm, HiP-HOPS can be used to solve the problem of redundancy allocation effectively and with relative efficiency. Furthermore, the ability of HiP-HOPS to model and automatically analyse complex engineering models, with multiple failure modes, allows the Genetic Algorithm to potentially optimise systems using more flexible strategies, not just series-parallel. The results of this thesis show the feasibility of the approach and point to a number of directions for future work to consider

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Fault Tolerant Real Time Dynamic Scheduling Algorithm For Heterogeneous Distributed System

Fault-tolerance becomes an important key to establish dependability in Real Time Distributed Systems (RTDS). In fault-tolerant Real Time Distributed systems, detection of fault and its recovery should be executed in timely manner so that in spite of fault occurrences the intended output of real-time computations always take place on time. Hardware and software redundancy are well-known e ective methods for faulttolerance, where extra hard ware (e.g., processors, communication links) and software (e.g., tasks, messages) are added into the system to deal with faults. Performances of RTDS are mostly guided by eciency of scheduling algorithm and schedulability analysis are performed on the system to ensure the timing constrains. This thesis examines the scenarios where a real time system requires very little redundant hardware resources to tolerate failures in heterogeneous real time distributed systems with point-to-point communication links. Fault tolerance can be achieved by..