Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey

In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers

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

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Maxmin, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Towards the Exploration of Task and Workflow Scheduling Methods and Mechanisms in Cloud Computing Environment

Cloud computing sets a domain and application-specific distributed environment to distribute the services and resources among users. There are numerous heterogeneous VMs available in the environment to handle user requests. The user requests are defined with a specific deadline. The scheduling methods are defined to set up the order of request execution in the cloud environment. The scheduling methods in a cloud environment are divided into two main categories called Task and Workflow Scheduling. This paper, is a study of work performed on task and workflow scheduling. Various feature processing, constraints-restricted, and priority-driven methods are discussed in this research. The paper also discussed various optimization methods to improve scheduling performance and reliability in the cloud environment. Various constraints and performance parameters are discussed in this research

Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions

Edge and Fog computing paradigms utilise distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of the fast-evolving IoT applications. Due to the fine-grained modularity of the microservices along with their independently deployable and scalable nature, MSA exhibits great potential in harnessing both Fog and Cloud resources to meet diverse QoS requirements of the IoT application services, thus giving rise to novel paradigms like Osmotic computing. However, efficient and scalable scheduling algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. To this end, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications scheduling in Edge and Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the scheduling problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey

Alts : An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing

Abstract: According to the research, many task scheduling approaches have been proposed like GA, ACO, etc., which have improved the performance of the cloud data centers concerning various scheduling parameters. The task scheduling problem is NP-hard, as the key reason is the number of solutions/combinations grows exponentially with the problem size, e.g., the number of tasks and the number of computing resources. Thus, it is always challenging to have complete optimal scheduling of the user tasks. In this research, we proposed an adaptive load-balanced task scheduling (ALTS) approach for cloud computing. The proposed task scheduling algorithm maps all incoming tasks to the available VMs in a load-balanced way to reduce the makespan, maximize resource utilization, and adaptively minimize the SLA violation. The performance of the proposed task scheduling algorithm is evaluated and compared with the state-of-the-art task scheduling ACO, GA, and GAACO approaches concerning average resource utilization (ARUR), Makespan, and SLA violation. The proposed approach has revealed significant improvements concerning the makespan, SLA violation, and resource utilization against the compared approaches

Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing

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