A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts

Effective Workflow Scheduling in Cloud using Constriction Factor based Inertia Weight Particle Swarm Optimization

Cloud computing allows rapid provision of resources based on the need. This enables users to execute the independent tasks and dependent tasks called workflows on the cloud system. Workflow scheduling is a crucial problem that is NP Hard and is still a challenging problem. Particle Swarm Optimization (PSO) is one of the commonly used metaheuristic algorithms for solving task scheduling problems, but it has issues with premature convergence and lack of diversity. In recent years, chaotic maps have been employed in PSO to enhance its performance. This study proposes a Constriction factor-based inertia weight in PSO for workflow scheduling (CFPSO). The proposed algorithm utilizes a constriction factor for updating the inertia weight, which enhances the exploration ability of the algorithm thereby avoid local optima. The algorithm considers a fitness function with an aim to minimize makespan, service cost, and maximize load balance. The proposed algorithm is evaluated using a set of benchmark workflows, and the obtained results are compared with the standard PSO algorithm, Grey Wolf Optimizer (GWO) algorithm and Chaotic PSO algorithm. The extensive experimentation performed show that the proposed algorithm outperforms the other algorithms in terms of makespan, service cost, and load balance. The proposed CFPSO shows reduction of 20% of makespan, 2% of the service cost and 18% load balance rate compared to the conventional algorithms on Montage workflow with 1000 tasks. The use of constriction factor enhances the performance of the algorithm and makes it suitable for solving complex problems with multiple objectives. The proposed algorithm can be used in real-world applications to optimize workflow scheduling in cloud computing environments

Responsive Multi-objective Load Balancing Transformation Using Particle Swarm Optimization in Cloud Environment

Cloud computing is an emerging computing paradigm with a large collection of heterogeneous autonomous systems with flexible computational architecture which provides the customers with computing resources as a service over a network on their demand. A multi-objective nature is inherent in cloud resource scheduling, as the objectives of cloud providers, cloud users, and other stakeholders can be independent. Resource allocation among multiple clients has to be ensured as per service level agreements. Several techniques have been invented and tested by research community for generation of optimal schedules in cloud computing. To accomplish these goals and achieve high performance, it is important to design and develop a Responsive multi-objective load balancing Transformation algorithm (RMOLBT) based on abstraction in multi cloud environment. It is most challenging to schedule the tasks along with satisfying the user’s Quality of Service requirements. This paper proposes a  wide variety of task scheduling and resource utilization using Particle swarm Optimization (PSO) in cloud environment. The result obtained by RMOLBT was simulated by an open source cloudsim configured with test case specification. Finally, the results  demonstrate the suitability of the proposed scheme that will increase throughput, reduce waiting time, reduction in missed process considerably and balances load among the physical machines in a Data centre in multi cloud environment

Optimized task scheduling based on hybrid symbiotic organisms search algorithms for cloud computing environment

In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Task scheduling algorithms are responsible for specifying adequate set of resources to execute user applications in the form of tasks, and schedule decisions of task scheduling algorithms are based on QoS requirements defined by the user. Task scheduling problem is an NP-Complete problem, due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms incur high computational complexity and cannot effectively obtain global optimum solutions. Recently, Symbiotic Organisms Search (SOS) has been applied to various optimization problems and results obtained were found to be competitive with state-of-the-art metaheuristic algorithms. However, similar to the case other metaheuristic optimization algorithms, the efficiency of SOS algorithm deteriorates as the size of the search space increases. Moreover, SOS suffers from local optima entrapment and its static control parameters cannot maintain a balance between local and global search. In this study, Cooperative Coevolutionary Constrained Multiobjective Symbiotic Organisms Search (CC-CMSOS), Cooperative Coevolutionary Constrained Multi-objective Memetic Symbiotic Organisms Search (CC-CMMSOS), and Cooperative Coevolutionary Constrained Multi-objective Adaptive Benefit Factor Symbiotic Organisms Search (CC-CMABFSOS) algorithms are proposed to solve constrained multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. To address the issue of scalability, the concept of Cooperative Coevolutionary for enhancing SOS named CC-CMSOS make SOS more efficient for solving large scale task scheduling problems. CC-CMMSOS algorithm further improves the performance of SOS algorithm by hybridizing with Simulated Annealing (SA) to avoid entrapment in local optima for global convergence. Finally, CC-CMABFSOS algorithm adaptively turn SOS control parameters to balance the local and global search procedure for faster convergence speed. The performance of the proposed CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms are evaluated on CloudSim simulator, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms are compared with multi-objective optimization algorithms, namely, EMS-C, ECMSMOO, and BOGA. The CC-CMSOS, CC-CMMSOS, and CC-CMABFSOS algorithms obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) while meeting deadline constraints with no computational overhead. The performance improvements obtained by the proposed algorithms in terms of hypervolume ranges from 8.72% to 37.95% across the workloads. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery

Hybrid heuristic algorithm for better energy optimization and resource utilization in cloud computing

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a Hybrid Heuristic algorithm based Energy-efficient cloud Computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the Chaotic based Particle Swarm Optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the Hybrid Heuristic Workflow Scheduling algorithm (HHWS) and Distributed Dynamic VM Management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods

Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date:September 30th 2022

Random Adjustment - Based Chaotic Metaheuristic Algorithms for Image Contrast Enhancement

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem\u27s characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment

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

Task scheduling for application integration: A strategy for large volumes of data

Enterprise Application Integration is the research field, which provides methodologies, techniques and tools for modelling and implementing integration processes. An integration process performs the orchestration of a set of applications to keep them synchronised or to allow the creation of new features. It can be represented by a workflow composed of tasks and communication channels. Integration platforms are tools for the design and execution of integration processes in which, the runtime system is the component responsible for execution time of the tasks and the allocation of computational resources that perform them. The processing of a large volume of data, corresponding to execution of millions of tasks, can cause situations of overload, characterised by the accumulation of tasks in internal queues awaiting computational resources in the runtime systems, resulting in unacceptable response time for the external applications and users. Our research hypothesis is that the runtime systems of the integration platforms use simplistic heuristics for scheduling tasks, which does not allow them to maintain acceptable levels of performance when there are overload situations. In this research work, we developed (i) a representation for integration processes, (ii) a characterisation for your task schedules, (iii) a heuristic to deal with situations of overload, (iv) a mathematical model for a performance metric of the execution of integration processes and (v) a simulation tool for task scheduling heuristics. Our research results indicate that, in situations of overload, our heuristic promotes a balanced workload distribution and an increase in the performance of the execution of the integration processes.Integração de Aplicações Empresariais é o campo de pesquisa, que fornece metodologias, técnicas e ferramentas para modelar e implementar processos de integração. Um processo de integração executa a orquestração de um conjunto de aplicações para mantê-las sincronizadas ou para permitir a criação de novas funcionalidades. Ele pode ser representado por um fluxo de trabalho composto por tarefas e canais de comunicação. Plataformas de integração são ferramentas para projetar e executar processos de integração, nas quais o motor de execução é o componente responsável pelo tempo de execução das tarefas e pela alocação de recursos computacionais que as executam. O processamento de um grande volume de dados, correspondendo a execução de milhões de tarefas, pode causar situações de sobrecarga, caracterizadas pelo acúmulo de tarefas em filas internas que aguardam recursos computacionais nos motores de execução, resultando em tempos de resposta inaceitáveis para aplicações e usuários externos. Nossa hipótese de pesquisa é que os motores de execução das plataformas de integração usam heurísticas simplistas para agendar tarefas, o que não lhes permitem manter níveis aceitáveis de desempenho em situações de sobrecarga. Neste trabalho de pesquisa, desenvolvemos (i) uma representação para processos de integração, (ii) uma caracterização para seus agendamentos de tarefas, (iii) uma heurística para lidar com situações de sobrecarga, (iv) um modelo matemático para uma métrica de desempenho da execução de processos de integração e (v) uma ferramenta de simulação para heurísticas de agendamento de tarefas. Nossos resultados de pesquisa indicam que, em situações de sobrecarga, nossa heurística promove uma distribuição equilibrada da carga de trabalho e um aumento no desempenho da execução dos processos de integração