Dynamic Fault-Tolerant Workflow Scheduling with Hybrid Spatial-Temporal Re-Execution in Clouds

The workflow scheduling in the cloud computing environment is a well-known NP-complete problem, and metaheuristic algorithms are successfully adapted to solve this problem more efficiently. Grey wolf optimization (GWO) is a recently proposed interesting metaheuristic algorithm to deal with continuous optimization problems. In this paper, we proposed IGWO, an improved version of the GWO algorithm which uses the hill-climbing method and chaos theory to achieve better results. The proposed algorithm can increase the convergence speed of the GWO and prevents falling into the local optimum. Afterward, a binary version of the proposed IGWO algorithm, using various S functions and V functions, is introduced to deal with the workflow scheduling problem in cloud computing data centers, aiming to minimize their executions’ cost, makespan, and the power consumption. The proposed workflow scheduling scheme is simulated using the CloudSim simulator and the results show that our scheme can outperform other scheduling approaches in terms of metrics such as power consumption, cost, and makespan.

Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues

An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment

For those public-cloud-based application providers, workflow scheduling in clouds must not only meet traditional performance optimization goals, but also minimize financial costs. This paper focuses on budget-constrained workflow scheduling issue on heterogeneous cloud resources, and tries to minimize the end-to-end delay of the workflow while meeting the given budget. A heuristic algorithm is proposed, which introduces a resource leasing mechanism to maximize the usage of the budget. This paper also defines a scheduling factor parameter consisting of two factors (parallelism and urgency) to determine the scheduled tasks. A scheduling algorithm based on task duplication is designed, which reduces the finish time of workflow tasks by using the idle time slots in existing servers. Finally, compared with contrast algorithms, the experimental results show that the proposed algorithm is superior in both meeting budget constraints and minimizing end-to-end delay.

Cloud computing, a novel and promising model of Service-oriented computing, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. Workflow scheduling in cloud is challenging due to dynamic nature of the cloud, particularly, on demand provisioning, elasticity, heterogeneous resource types, static & dynamic pricing models and virtualization. An example of workflow scheduling is mapping workflow tasks to cloud computing resources. Additionally, these workflow applications have a runtime constraint—the most typical being the cost of the computation and the time that computation requires to complete. Therefore, the focus is on two criteria: makespan and cost. This paper presents an algorithm called NBWS (Normalization based Budget constraint Workflow Scheduling) which generates a workflow schedule which minimizes the schedule length while satisfying the budget constraint. The algorithm undergoes a process of min–max normalization tailed by computing expect reasonable budget $$ (erb) $$ for dispatching the workflow tasks into one of the virtual machines. To minimize the execution time, NBWS algorithm maps the workflow tasks to resources which are having the earliest finish time within the allocated budget. The experimental results demonstrate that NBWS outperforms current state-of-the-art heuristics with respect to budget constraint and minimizing the makespan.

Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

Cloud computing is the new generation of networks that uses remote servers hosted on the Internet for various uses such as data storage, data management, software usage etc. There are huge amount of resources provided and users can make use of the resources in any way they want to. Today, researchers attempt to find newer ways for Workflow scheduling which could work well in the cloud environment. Workflow scheduling is the most important task in cloud computing field and users have to pay for resources that were used based in a pay-per-usage scheme. Hence Workflow scheduling plays a vital role in getting maximum benefit from the resources that are provided. Another important element to be considered about cloud computing is Load balancing. This controlling of fill assures that every exclusive machine does the very same amount of labour at any immediate of time. To make sure this, we want to recommend on using the idea of fill controlling. Here in this document, we recommend heuristic criteria known as Firefly criteria for effective fill controlling in reasoning processing. This criterion is based on the travel behaviour of the fireflies which go looking for the closest possible maximum alternatives. We employ Firefly algorithm to schedule the jobs and thereby evenly distribute the load and in turn reduce the overall completion time (makespan).

HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment

Cloud computing has emerged as a dominant platform for hosting complex applications, offering scalable and flexible resources on demand. However, the dynamic and heterogeneous nature of cloud environments poses significant challenges for efficient workflow scheduling, particularly when aiming to minimize total execution time, energy consumption, and operational cost. In this research, we propose a novel hybrid approach that integrates the Heterogeneous Earliest Finish Time (HEFT) algorithm with an Improved Grey Wolf Optimizer (IGWO) enhanced by differential evolution strategies and survival-of-the-fittest mechanisms. These enhancements strengthen exploration and exploitation by adaptively mutating and refining task allocations while eliminating weaker solutions. The use of HEFT-based initialization provides a strong starting population, and the DE-driven IGWO refinement accelerates convergence and avoids premature stagnation. Together, these two-level optimization strategy ensures faster convergence and higher energy-efficient workflow scheduling compared to earlier HEFT metaheuristic approaches. To evaluate the effectiveness of the proposed hybrid method, extensive experiments were conducted on randomly generated workflows with varying task and dependency complexities. The performance analysis demonstrates that the hybrid HEFT-IGWO approach consistently outperforms standard HEFT, traditional GWO, and standalone metaheuristic techniques in terms of minimizing makespan, reducing energy consumption, and lowering cloud infrastructure costs. This study highlights the potential of combining heuristic initialization with evolutionary optimization to achieve energy-efficient, cost-effective workflow scheduling in cloud computing environments.

Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm for task scheduling in clouds, our proposed method is more efficient than other proposed methods due to the use of new genetic operators as well as modified genetic operators and the use of load balancing routine. Moreover, a solution obtained from a heuristic used as one of the initial population chromosomes and an efficient routine also used for generating the rest of the primary population chromosomes. An adaptive fitness function is used that takes into account both cost and makespan. The algorithm introduced in this paper utilizes a load balancing routine to maximize resources’ efficiency at execution time. The performance of the proposed algorithm is evaluated by comparing the results with state of the art algorithms of this field, and the results indicate that the proposed algorithm has remarkable superiority in comparison to other algorithms and performs task scheduling with the least makespan and cost.

Vehicular cloud as a kind of resource aggregation can provide cloud service flexibly for intelligent transportation systems and individual users. However, the dynamicity of vehicular cloud such as dynamic multihop transmission also brings new challenges to workflow scheduling. In this paper, a heuristic dynamic workflow scheduling (HDWS) strategy is proposed to solve the problem of workflow scheduling with dynamic multihop transmission in vehicular cloud. HDWS establishes a local scheduling based on the current resource status and task sets. The makespan of local scheduling is optimized by reassigning tasks based on task interdependency. At run time of an application, HDWS estimates the makespan in real time based on the current transmission rates and the unready tasks will be rescheduled once it detects the degradation of the makespan. Experimental results show that the proposed HDWS can improve the service success ratio of vehicular cloud and decrease the makespan compared to the existing approaches.

PurposeIn general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling.Design/methodology/approachTo make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling.FindingsThe proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively.Originality/valueThis paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.

In recent years, intelligent flow sensors have been applied to many fields. Cloud operation is a design method to further improve the intelligence of such sensors. However, the cloud workflows of intelligent flow sensors consume too much energy, making it imperative to schedule cloud workflows. With the growing awareness of energy conservation, it is a hot topic to design an energy-efficient workflow scheduling algorithm. Therefore, this paper puts forward the predict minimum energy consumption (PMEC) algorithm, a cloud workflow scheduling algorithm that strikes a balance between energy consumption and execution time. Firstly, the optimistic cost table (OCT) was adopted to rank the tasks by priority. Then, the resources, i.e. virtual machines, were assigned statically to the tasks, in the light of task priority and energy consumption. After that, the workflow was scheduled according to the assignments. Simulation results show that the PMEC is much more energy efficient than traditional listbased scheduling algorithms.

AI-based & heuristic workflow scheduling in cloud and fog computing: a systematic review

An improved Caledonian crow learning algorithm based on ring topology for security-aware workflow scheduling in cloud computing