The Serverless Clouds Computing environment (or platform) manages the resource management of its respective clients who generally submit their respective applications as sets of functions (tasks). A client may submit his application as a set of tasks (functions) or as a monolithic task (single function). Each set of functions (tasks) compiled in the form of Directed Acyclic Graph (DAG), where each node is a function representing a fine-grained task and each edge represents a dependency among two functions. The decisions made through performance forecasting systems (PFS) or resource forecasting engines are of immense importance to such resource management systems. However, the forecasting of future resources is a complex problem. Several of PFS projects span over several computer resources in several dimensions. The most of the PFS projects have already been designed for performance forecasting of resources on the Distributed Computing Environments such as Peer-Peer, Queue systems, Clusters, Grids, Virtual machine organizations and Cloud systems and therefore in software engineering point of view, the new code can be written to integrate their forecasting services on the Serverless (Edge) Clouds platforms. In this chapter the taxonomy for describing the PFS architecture is discussed. The taxonomy is used to classify and identify approaches which are followed in the implementation of the existing PFSs in the Distributed Computing Environments and to realise their adaptation in the Serverless (Edge) Cloud Computing.

Particle swarm optimization (PSO) is a stochastic population-based algorithm based on the collaborative swarm behaviors of insects, birds, fish, and animals. The algorithms use swarm emulation to seek for solutions to optimization problems and are often used for the optimization of continuous nonlinear systems. This approach has recently succeeded in addressing many significant real-world optimization problems. In particular, this work integrates studies of PSOs and proportional–integral–derivative (PID) controllers, which are widely used for industrial control, in order to optimize PID controllers using PSO in a wide variety of multidisciplinary fields. This chapter introduces the PSO approach and algorithm and the theory of PID control, the use of PSO in optimizing the parameters of various controllers, and illustrates the findings by exploring several real-time control applications.

Increasing numbers of metastatic melanoma (MM) patients are receiving immunotherapy treatment, including pembrolizumab, and the impact on their well-being is underexplored.

Objective Little is known about the lived experiences of patients with metastatic melanoma undergoing checkpoint inhibitor treatment. We conducted a feasibility study of a supportive care intervention for melanoma patients being treated with pembrolizumab. Here, we report a secondary objective of the study, which was to explore the lived experience of being on pembrolizumab treatment for advanced melanoma. Methods Twenty-eight participants with metastatic melanoma were recruited across two cohorts, all receiving 3-weekly immunotherapy treatment. Semi-structured interviews were conducted with 26 participants once at 9 weeks. Thematic analysis using interpretative phenomenological analysis (IPA) was performed with multiple iterations of data review to achieve consensus. Results Three overarching themes were identified; here, we report the first and most dominant theme: how metastatic melanoma patients live within uncertain spaces. Although immunotherapy increases overall survival, metastatic melanoma patients live within an uncertain spectrum. They confront uncertainty related to immunotherapy treatment, their disease trajectory, family relationships, and decision-making. Melanoma patients attempt to normalize their lives, engaging in their usual activities. Uncertainty increases prior to active treatment and intensifies during investigation phases. Conclusions Despite progress in melanoma patient treatment and outcomes, these patients face sustained uncertainty about their disease trajectory.

Since chaos systems generally have the intrinsic properties of sensitivity to initial conditions, topological mixing and density of periodic orbits, they may tactfully use the chaotic ergodic orbits to achieve the global optimum or their better approximation to given cost functions with high probability. During the past decade, they have increasingly received much attention from academic community and industry society throughout the world. To improve the performance of particle swarm optimization (PSO), we herein propose a chaotic proportional integral derivative (PID) controlling PSO algorithm by the hybridization of chaotic logistic dynamics and hierarchical inertia weight. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles’ search space. Moreover, the chaotic logistic map is not only used in the substitution of the two random parameters affecting the convergence behavior, but also used in the chaotic local search for the global best position so as to easily avoid the particles’ premature behaviors via the whole search space. Thereafter, the convergent analysis of chaotic PID controlling PSO is under deep investigation. Empirical simulation results demonstrate that compared with other several chaotic PSO algorithms like chaotic PSO with the logistic map, chaotic PSO with the tent map and chaotic catfish PSO with the logistic map, chaotic PID controlling PSO exhibits much better search efficiency and quality when solving the optimization problems. Additionally, the parameter estimation of a nonlinear dynamic system also further clarifies its superiority to chaotic catfish PSO, genetic algorithm (GA) and PSO.

As a method of estimating the parameters of statistical models in statistics, maximum likelihood (ML) method has gained much attention and been popularly applied to a variety of fields. In the subjects of information, control, as well as system science and engineering, it is usually exploited to es timate the parameters of diverse linear dynamic systems. The tough task of estimating ML parameters of diverse linear dynamic systems can be attributed to a constrained optimization problem. In an effort to solve the constrained optimization problem effectively, we first use the ML method to address the statistical modelling of ML parameter estimation of general linear dynamic systems. Then we present a novel heuristic particle swarm search algorithm called sliding mode controlling particle swarm optimization (SMCPSO) algorithm. In SMCPSO, we introduce a sliding mode controller (SMC) into standard particle swarm optimization (SPSO). The SMC is intentionally placed between the particle position and the global best particle position so as to improve the particles' position information. It ameliorates the exploration and exploitation search abilities of the particles. Thereafter, SMCPSO is exploited to solve the problem of the ML parameter estimation of a given linear dynamic system together with SPSO, recursive ML generalized least squares (RMLGLS) method, and recursive ML least squares (RMLLS) algorithm. The simulation results demonstrate that SMCPSO is an effective approach and superior to other three approaches in estimating the ML parameters of the linear dynamic system.

PURPOSE:Demand for complementary and alternative medicine (CAM) is high among cancer patients. This, alongside growing evidence for the efficacy of some CAM therapies, is driving change within cancer centres, where evidence-based CAM therapies are increasingly provided alongside standard cancer treatments. In Australia, commitment to equitable access to healthcare is strong, and some cancer centres are now providing integrative services at no cost to the patient. This represents a significant shift in healthcare provision. This study aimed to examine health professional and patient dynamics in an integrated cancer service where CAM is provided at no cost to patients alongside standard cancer treatments. It specifically sought to understand what might drive or hinder further integration of CAM with standard treatment in the cancer context.METHODS:Qualitative interviews were undertaken with twenty key stakeholders-cancer patients, cancer nurses, and oncologists-who were delivering or receiving care in an Australian public hospital where acupuncture services are provided at no cost to patients alongside standard chemotherapy and radiation treatments.RESULTS:Findings point to key areas where the concerns and priorities of cancer patients, cancer nurses, and oncologists converge and diverge in ways that reflect core personal and professional interests regarding patient care needs, the evidence base for CAM efficacy and safety, and rising healthcare costs.CONCLUSIONS:Understanding points of convergence and divergence could assist clinicians and service providers in negotiating ways forward for integrative cancer services.

Proportional integral derivative (PID) controllers are widely used in industrial control processes since they are simple and easy to implement and act as an effective measure to manipulate the dynamic properties of industry systems. Carrying out the optimal design of the PID controllers is an indispensable constituent of the premises of highly precious control of these systems. In order to solve the problem of designing the parameters of the PID controllers more effectively, we bring forward a chaotic particle swarm optimization (CPSO) approach which we call CPIDSO. In this approach, we introduce the combination of chaotic logistic dynamics, hierarchical inertia weight, enhancement learning strategy, mutation mechanism and a proportional integral derivative (PID) controller. The chaotic logistic map is used in the substitution of the two random parameters affecting the convergence behavior. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles' search space. The PID controller and enhancement learning strategy are simultaneously incorporated into standard PSO (SPSO) to efficiently enhance the particles' local and global search exploration and exploitation abilities. For performance validation of CPIDSO, CPIDSO, together with other algorithms like chaotic catfish PSO (CCPSO), genetic algorithm (GA) and PSO, is exploited to design the parameters of a PID controller in a Kalman filter based cybernetic system. The simulation results illustrate that CPIDSO exhibits better performance than other algorithms and yields the best result in the parameter optimization design of the system.

This letter illustrates the effect of timing drift using a short Ada program as an educational tool.

As an indispensable constituent of the premises of highly precious control of vertical takeoff and landing (VTOL) aircrafts, parameter identification has received an increasingly considerable attention from academic community and practitioners. In an effort to tackle the matter better, we herewith put forward a PID controlling particle swarm optimizer (PSO) which we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (P idSO). It uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles' local and global best positions information. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO features better search effectiveness and efficiency in solving most of the multimodal optimization problems when compared with other recent variants of PSOs, and its performance can be upgraded by adopting proper control law based controllers. Moreover, PidSO, together with least squares (LS) method and genetic algorithm (GA), is applied to the parameter estimation of the VTOL aircraft. In comparison with LS method and GA, PidSO is a more effective tool in estimating the parameters of the VTOL aircraft.

Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.

In the business domain requirements are typically expressed in natural language. Any formal specifications based on high level abstractions are generally imprecise, incomplete and inconsistent. As a result the implementation of business systems often fails to deliver solutions as expected as consequence of the discontinuity between models at different levels of abstraction. Situation theory combines linguistics and logic to form a framework for capturing and manipulating information. In this work we apply situation theory to process specifications defined using natural language and create formal specification artefacts which are amenable to processing using model based software engineering methods.

Complex software systems often suffer from flaws arising either during the design or the development stages. In many cases due to the lack of formal underpinning it is difficult to assuredly define the model from which the system is generated. In our work, we investigated the suitability of mathematical situation theory as a formal foundation for a computer modelling language. This paper describes our empirical evaluation of this thesis in the real-time domain using Infon Lab, a tool based upon situation theory.

Parameter estimation of static friction torques in servo control systems is of great significance to their robust control. Many researchers are devoted to pursuing the solutions to estimating the coefficients of the static friction torques. In order to tackle the troublesome matter more effectively, in this paper, we address a neural dynamic programming inspired particle swarm search algorithm. We call the algorithm direct BP neural dynamic programming inspired PSO (NDPSO) since we incorporate direct back propagation (BP) and neural dynamic programming (NDP) into particle swarm optimization (PSO). In NDPSO, critic BP neural network is trained to balance the Bellman equation while action BP neural network is used to train the inertia weight, the cognitive coefficient, and the social coefficient of the PSO algorithm. The training target is to enable the critic BP neural network output to approach the ultimately successful objective. Successively, NDPSO, together with standard PSO (SPSO) and genetic algorithm (GA), is applied to the parameter identification of the static friction torque in a servo control system with single input and single output (SISO). The experimental results clearly demonstrate that NDPSO is effective and outperforms SPSO and GA in identifying the parameters of the static friction torque in the servo control system.

In this paper, we introduce direct back propagation (BP) neural dynamic programming (NDP) into particle swarm optimisation (PSO). Thus, a direct BP NDP inspired PSO algorithm, which we call NDPSO, is proposed. In NDPSO, since direct BP NDP belongs to the class of heuristic dynamic programming algorithms based on model-based adaptive critic designs and often serves as an online learning control paradigm, critic BP neural network is trained to optimise a total reward-to-go objective, namely to balance Bellman's equation, while action BP neural network is used to train the inertia weight, cognitive, and social coefficients so that the critic BP network output can approach an ultimate reward-to-go objective of success. With the collective aid of action-critic BP neural networks, inertia weight, cognitive, and social coefficients become more adaptive. Besides, the NDPSO's mutation mechanism also has greatly improved the dynamic performance of the standard PSO. Empirical experiments are conducted on both unimodal and multimodal benchmark functions. The experimental results demonstrate NDPSO's effectiveness and superiority to many other PSO variants on solving most multimodal problems.

Cloud computing offers new computing paradigms, capacity, and flexibility to high performance computing (HPC) applications with provisioning of a large number of Virtual Machines (VMs) for computation-intensive applications using the Hardware as a Service (HaaS) model. Due, however, to the large number of VMs and electronic components in HPC systems in the cloud, any fault during the execution would result in re-running the application, which will cost time, money and energy. In this paper we present a proactive Fault Tolerance (FT) approach to HPC systems in the cloud to reduce the wall clock execution time in the presence of faults. We develop a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We analyze the dollar cost of provisioning spare nodes to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall clock execution time of the computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of check pointing of computation-intensive applications can be reduced to 50% with our fault tolerance approach for HPC in the cloud, compared to current FT approaches.

This paper presents a novel variant of particle swarm optimizers (PSOs) that we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (PidSO), which uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles' local and global best positions information. This strategy enables PidSO to improve the diversity of swarm in a bid to discourage premature convergence and to perform a global search over the entire search space more efficiently. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO enhances the diversity of the swarm and features better search effectiveness and efficiency in solving most multimodal optimization problems when compared with other recent variants of PSOs and evolutionary optimization algorithms such as integral-controlled PSO (ICPSO), PID-controlled PSO (PIDCPSO), comprehensive learning PSO (CLPSO), I-population covariance matrix adaptation evolution strategy (IPOP-CMA-ES), and a multi algorithm genetically adaptive method for single objective optimization (AMALGAM-SO), and so on. Additionally, it has been observed that PidSO is able to achieve comparatively better success rates and success performances though it is more complex, and that the performance of PidSO is promoted by selecting proper law based controllers. Consequently, PidSO offers a new solution to real engineering optimization designs of industrial systems.

In recent years, High Performance Computing (HPC) systems have been shifting from expensive massively parallel architectures to clusters of commodity PCs to take advantage of cost and performance benefits. Fault tolerance in such systems is a growing concern for long-running applications. In this paper, we briefly review the failure rates of HPC systems and also survey the fault tolerance approaches for HPC systems and issues with these approaches. Rollback-recovery techniques which are most often used for long-running applications on HPC clusters are discussed because they are widely used for long-running applications on HPC systems. Specifically, the feature requirements of rollback-recovery are discussed and a taxonomy is developed for over twenty popular checkpoint/restart solutions. The intent of this paper is to aid researchers in the domain as well as to facilitate development of new checkpointing solutions.