Heuristic Workflow Scheduling Research Articles

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

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

Cost-effective Heuristic Workflow Scheduling Algorithm in Cloud Under Deadline Constraint

Background: Cloud computing is used to deliver IT resources over the internet. Due to the popularity of cloud computing, nowadays, most of the scientific workflows are shifted towards this environment. Many algorithms have been proposed in the literature to schedule scientific workflows in the cloud, but their execution cost is very high as they are not meeting the user-defined deadline constraint. Aim: This paper focuses on satisfying the user-defined deadline of a scientific workflow while minimizing the total execution cost. Methods: So, to achieve this, we proposed a Cost-Effective under Deadline (CEuD) constraint workflow scheduling algorithm. Results: The proposed CEuD algorithm considers all the essential features of the Cloud and resolves the major issues such as performance variation and acquisition delay. Conclusion: We compared the proposed CEuD algorithm with the existing literature algorithms for scientific workflows (i.e., Montage, Epigenomics, and CyberShake) and obtained better results for minimizing the overall execution cost of the workflow while satisfying the user-defined deadline.

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.

Fairness-based heuristic workflow scheduling and placement in cloud computing

Fairness-based heuristic workflow scheduling and placement in cloud computing

Fairness-based heuristic workflow scheduling and placement in cloud computing

Cloud computing has become a commercial business environment for small and large scale industries to avail the resources. Cloud application requests are processed dynamically to access resource utilisation based on the diverse demand for cloud resources. The expected allocation of resources to the application is inequitable. This paper aims to analyse the QoS metrics of cloud services based on user satisfaction, and proposes Fairness-based Heuristic Workflow Scheduling and Placement (FHWSP) algorithm with the new multi-objective function. It minimises the overall profit and time of execution with respect to characteristics of tasks and physical servers in the datacentre. The evaluation of the proposed FHWSP algorithm with the existing traditional algorithms is simulated in a CloudSim simulated environment. The experimental results significantly improve the QoS parameters in terms of 5% in makespan and 3% in total execution cost of the scheduling and placement algorithm on CloudSim environment.

CCA: a deadline-constrained workflow scheduling algorithm for multicore resources on the cloud

Workflows are adopted as a powerful modeling technique to represent diverse applications in different scientific fields as a number of loosely coupled tasks. Given the unique features of cloud technology, the issue of cloud workflow scheduling is a critical research topic. Users can utilize services on the cloud in a pay-as-you-go manner and meet their quality of service (QoS) requirements. In the context of the commercial cloud, execution time and especially execution expenses are considered as two of the most important QoS requirements. On the other hand, the remarkable growth of multicore processor technology has led to the use of these processors by Infrastructure as a Service cloud service providers. Therefore, considering the multicore processing resources on the cloud, in addition to time and cost constraints, makes cloud workflow scheduling even more challenging. In this research, a heuristic workflow scheduling algorithm is proposed that attempts to minimize the execution cost considering a user-defined deadline constraint. The proposed algorithm divides the workflow into a number of clusters and then an extendable and flexible scoring approach chooses the best cluster combinations to achieve the algorithm's goals. Experimental results demonstrate a great reduction in resource leasing costs while the workflow deadline is met.

Trust Based Meta-Heuristics Workflow Scheduling in Cloud Service Environment

Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. As an effective security services aggregation methodology, Trust Work-flow Technology (TWT) has been used to construct composite services. However, in cloud environment, the existing closed network services are maintained and functioned by third-party organizations or enterprises. Therefore service-oriented trust strategies must be considered in workflow scheduling. TWFS related algorithms consist of trust policies and strategies to overcome the threats of the application with heuristic workflow scheduling. As a significance of this work, trust based Meta heuristic workflow scheduling (TMWS) is proposed. The TMWS algorithm will improve the efficiency and reliability of the operation in the cloud system and the results show that the TMWS approach is effective and feasible.

Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud

The energy consumption of underlying cloud hardware has dramatically increased. The cloud service providers need to adopt some cost-effective and energy-aware job scheduler without compromising the quality of service QoS specified in the service level agreement SLA. Based on a rigorous mathematical model, we formulate an energy efficient problem to improve the resource utilisation for high system throughput. A multiple-procedure heuristic workflow scheduling and consolidation strategy is proposed with objectives to maximise the resource utilisation and minimise the power. Several techniques have been utilised including dynamic voltage and frequency scaling DVFS with task module migration for workload balance and task consolidation for virtual machine VM overhead reduction. The simulation results illustrate that our approach consistently achieves a lower power consumption and higher resource utilisation rate within the execution time bound compared with other similar scheduling algorithms as well as our previous algorithm without the task migration based on VM threshold.

Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud

The energy consumption of underlying cloud hardware has dramatically increased. The cloud service providers need to adopt some cost-effective and energy-aware job scheduler without compromising the quality of service (QoS) specified in the service level agreement (SLA). Based on a rigorous mathematical model, we formulate an energy efficient problem to improve the resource utilisation for high system throughput. A multiple-procedure heuristic workflow scheduling and consolidation strategy is proposed with objectives to maximise the resource utilisation and minimise the power. Several techniques have been utilised including dynamic voltage and frequency scaling (DVFS) with task module migration for workload balance and task consolidation for virtual machine (VM) overhead reduction. The simulation results illustrate that our approach consistently achieves a lower power consumption and higher resource utilisation rate within the execution time bound compared with other similar scheduling algorithms as well as our previous algorithm without the task migration based on VM threshold.

A hybrid heuristic workflow scheduling algorithm for cloud computing environments

Cloud computing is a relatively new concept in the distributed systems and is widely accepted as a new solution for high performance and distributed computing. Its dynamisms in providing virtual resources for organisations and laboratories and its pay-per-use policy make it very popular. A workflow models a process consisting of a series of steps that shape an application. Workflow scheduling is the method for assigning each workflow task to a processing resource in a way that specific workflow rules are satisfied. Some scheduling algorithms for workflows may assume some quality of service parameter such as cost and deadline. Some efforts have been done on workflow scheduling on cloud computing environments with different service level agreements. But most of them suffer from low speed. Here, we introduce a new hybrid heuristic algorithm based on particle swarm optimisation (PSO) and gravitation search algorithms. The proposed algorithm, in addition to processing cost and transfer cost, takes deadline limitations into account. The proposed workflow scheduling approach can be used by both end-users and utility providers. The CloudSim toolkit is used as a cloud environment simulator and the Amazon EC2 pricing is the reference pricing used. Our experimental result shows about 70% cost reduction, in comparison to non-heuristic implementations, 30% cost reduction in comparison to PSO, 30% cost reduction in comparison to gravitational search algorithm and 50% cost reduction in comparison to hybrid genetic-gravitational algorithm.