Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.

/pdf/workflow-scheduling-using-hybrid-ga-pso-algorithm-in-cloud-ay5qxrsuzm.pdf

Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing

Cloud computing is a recently looming-evoked paradigm, the aim of which is to provide on-demand, pay-as-you-go, internet-based access to shared computing resources (hardware and software) in a metered, self-service, dynamically scalable fashion. A related hot topic at the moment is task scheduling, which is well known for delivering critical cloud service performance. However, the dilemmas of resources being underutilized (underloaded) and overutilized (overloaded) may arise as a result of improper scheduling, which in turn leads to either wastage of cloud resources or degradation in service performance, respectively. Thus, the idea of incorporating meta-heuristic algorithms into task scheduling emerged in order to efficiently distribute complex and diverse incoming tasks (cloudlets) across available limited resources, within a reasonable time. Meta-heuristic techniques have proven very capable of solving scheduling problems, which is fulfilled herein from a cloud perspective by first providing a brief on traditional and heuristic scheduling methods before diving deeply into the most popular meta-heuristics for cloud task scheduling followed by a detailed systematic review featuring a novel taxonomy of those techniques, along with their advantages and limitations. More specifically, in this study, the basic concepts of cloud task scheduling are addressed smoothly, as well as diverse swarm, evolutionary, physical, emerging, and hybrid meta-heuristic scheduling techniques are categorized as per the nature of the scheduling problem (i.e., single- or multi-objective), the primary objective of scheduling, task-resource mapping scheme, and scheduling constraint. Armed with these methods, some of the most recent relevant literature are surveyed, and insights into the identification of existing challenges are presented, along with a trail to potential solutions. Furthermore, guidelines to future research directions drawn from recently emerging trends are outlined, which should definitely contribute to assisting current researchers and practitioners as well as pave the way for newbies excited about cloud task scheduling to pursue their own glory in the field.

Task Scheduling in Cloud Computing based on Meta-heuristics: Review, Taxonomy, Open Challenges, and Future Trends

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4?% in the first scenario and 60.0, 38.9, 31.5 and 31.2?% in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment.

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

To provide a better quality of service (QoS), cloud computing paradigm in Internet of Things (IoT) networks has shifted toward the edge. Fog-aided IoT networks deploy fog nodes, which are equipped with computing and storage resources, at the network edge to take over the deadline-driven computing tasks from IoT devices. In the fog node, where multiple virtual machines (VMs) can be rented to process the tasks, fog provisioning is to determine which VM should be rented and how to distribute different tasks to VMs in order to minimize the system cost (i.e., VM rentals). On the other hand, VMs may fail and lead to QoS degradation. Hence, reliability of VMs should also be considered when addressing the fog resource provisioning problem. To improve reliability, more VMs should be rented to satisfy the QoS requirement; this leads to higher system cost. Therefore, there is a tradeoff between reliability and the system cost. In this paper, we investigate the tradeoff of maximizing the reliability and minimizing the system cost for fog resource provisioning in IoT networks. An integer linear programming (ILP) problem is formulated but suffers from a high computational complexity. We then design an alternative algorithm to achieve suboptimal solutions with better time efficiency. The simulation results demonstrate the performances of our proposed algorithm.

Fog Resource Provisioning in Reliability-Aware IoT Networks

Efficient task and workflow scheduling are very important for improving resource management and reducing power consumption in cloud computing data centers (DCs). However, regarding numerous tasks, virtual machines, and several objectives which should be taken into account, scheduling is considered to be an NP-Hard problem. Multi-objective optimization is an interesting technique to deal with multiple conflicting goals which have been utilized by various schemes to solve the task and workflow scheduling problems. This paper focuses on the metaheuristic multi-objective optimization context and presents a comprehensive survey and overview of the multi-objective scheduling approaches designed for various cloud computing environments. It classifies the scheduling schemes regarding their applied multi-objective optimization algorithms and describes how they have adapted the optimization algorithms to solve scheduling problems. Furthermore, a comparison of the multi-objective scheduling schemes is provided, which illuminates future research directions, and finally concluding remarks are presented.

Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive Review

Workflow scheduling is one of the challenging problems in cloud computing, especially when service reliability is considered To improve cloud service reliability, fault tolerance techniques such as fault recovery can be employed Practically, fault recovery has impact on the performance of workflow scheduling Such impact deserves detailed research Only few research works on workflow scheduling consider fault recovery and its impact In this paper, we investigate the problem of workflow scheduling in clouds, considering the probability that cloud resources may fail during execution We formulate this problem as a multi-objective optimization model The first optimization objective is to minimize the overall completion time and the second one is to minimize the overall execution cost Based on the proposed optimization model, we develop a heuristic-based algorithm called Min-min based time and cost tradeoff (MTCT) We perform extensive simulations with four different real world scientific workflows to verify the validity of the proposed model and evaluate the performance of our algorithm The results show that, as expected, fault recovery has significant impact on the two performance criteria, and the proposed MTCT algorithm is useful for real life workflow scheduling when both of the two optimization objectives are considered

A Multi-objective Optimization Approach to Workflow Scheduling in Clouds Considering Fault Recovery

By live migration technology, multiple virtual machines (VMs) can be consolidated into a fewer physical servers and the idle ones can be shut down or switched to low-power mode, thus reducing the energy consumption of cloud data centers. However, live migration can result in performance degradation of migrated VMs, or even interrupting their services. At the same time, live migration can also aggravate the overheads of data transmissions and produce additional energy consumption in cloud data centers. All these negative influences belong to migration cost (MC) caused by VM migration, which becomes an important cost factor that can’t be ignored. Otherwise, another important concern, remaining runtime of the migrated VM, also has influence on the efficiency of VM consolidation, which is not well addressed as well. This paper investigates MC-aware VM consolidation problem and formulates the problem as a multi-constraint optimization model by considering migration cost and remaining runtime of VMs. Based on the proposed model, a heuristic algorithm, called MC-aware VM consolidation (MVC) algorithm, is developed. Finally, based on a real-world cloud trace, we conduct extensive experimental studies to verify the validity of the proposed model and algorithm. Experimental results show that, compared with some popular algorithms, MVC algorithm can effectively decrease the migration cost and, at the same time guarantee the energy consumption within a certain low level.

Migration Cost and Energy-Aware Virtual Machine Consolidation Under Cloud Environments Considering Remaining Runtime

Energy-aware Resource Management in Cloud Computing Considering Load Balance.

As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multi-objective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful execution rate, lower execution cost, smaller fairness deviation and most important, higher degree of user satisfaction in most cases.

/pdf/incentive-aware-virtual-machine-scheduling-in-cloud-2zojcu1d7e.pdf

Heyang Xu

Papers

A Multi-objective Optimization Approach to Workflow Scheduling in Clouds Considering Fault Recovery

Migration Cost and Energy-Aware Virtual Machine Consolidation Under Cloud Environments Considering Remaining Runtime

Energy-aware Resource Management in Cloud Computing Considering Load Balance.

Incentive-aware virtual machine scheduling in cloud computing

Efficient multi-resource scheduling algorithm for hybrid cloud-based large-scale media streaming