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Task scheduling is a non-deterministic polynomial-time hard (NP-hard) optimisation problem, thus applying metaheuristics is important. In this paper, we employ glowworm swarm optimisation (GSO) to solve the task scheduling problem in cloud computing to minimise the total execution cost of tasks while keeping the total completion time within the dea...
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... This answer intended to limit the general execution value of jobs, at the same time as maintaining the whole of entirety time inside the deadline [15]. According to the findings of a simulation, the GSO primarily based mission scheduling (GSOTS) set of rules has higher consequences than the shortest mission first (STF), the most essential mission first (LTF), and the (PSO) algorithms in phrases of reducing the whole of entirety time and the value of executing tasks. ...
... These classes are based on the normal distribution N[x, y] and the uniform distribution U[x, y] [54]. The different values of the file-size sf j are given as follows: C1: U [15,150] Table 3. In total, there 1240 instances. ...
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... Experimental results and statistical analysis demonstrate that the HGSO algorithm outperforms previous heuristic algorithms for most tasks. GSO is also applied to address task scheduling issues in cloud computing [185]. The GSO-based Task Scheduling (GSOTS) method reduces the overall cost of job execution while ensuring timely task completion. ...
The advent of the cloud computing paradigm has enabled innumerable organizations to seamlessly migrate, compute, and host their applications within the cloud environment, affording them facile access to a broad spectrum of services with minimal exertion. A proficient and adaptable task scheduler is essential to manage simultaneous user requests for diverse cloud services using various heterogeneous and varied resources. Inadequate scheduling may result in issues related to either under-utilization or over-utilization of resources, potentially causing a waste of cloud resources or a decline in service performance. Swarm intelligence meta-heuristics optimization technique has evinced conspicuous efficacy in tackling the intricacies of scheduling difficulties. Thus, the present manuscript seeks to undertake an exhaustive review of swarm intelligence optimization techniques deployed in the task-scheduling domain within cloud computing. This paper examines various swarm-based algorithms, investigates their application to task scheduling in cloud environments, and provides a comparative analysis of the discussed algorithms based on various performance metrics. This study also compares different simulation tools for these algorithms, highlighting challenges and proposing potential future research directions in this field. This review paper aims to shed light on the state-of-the-art swarm-based algorithms for task scheduling in cloud computing, showing their potential to improve resource allocation, enhance system performance, and efficiently utilize cloud resources.
... The suggested HGSO speeds up convergence and makes it easier to escape from local optima by reducing unnecessary computation and dependence on GSO initialization. GSO is used in paper [243] to address the task scheduling issue in cloud computing in order to reduce the overall cost of job execution while maintaining on-time task completion. ...
... The suggested HGSO speeds up convergence and makes it easier to escape from local optima by reducing unnecessary computation and dependence on GSO initialization. GSO is used in paper [243] to address the task scheduling issue in cloud computing in order to reduce the overall cost of job execution while maintaining on-time task completion. ...
The advent of the cloud computing paradigm allowed multiple organizations to move, compute, and host their applications in the cloud environment, enabling seamless access to a wide range of services with minimal effort. An efficient and dynamic task scheduler is required to handle concurrent user requests for cloud services using various heterogeneous and diversified resources. Improper scheduling can lead to challenges with under or over-utilization of resources, which could waste cloud resources or degrade service performance. Nature-inspired optimization techniques have been proven effective at solving scheduling problems. This paper accomplishes a review of nature-inspired optimization techniques for scheduling tasks in cloud computing. A novel classification taxonomy and comparative review of these techniques in cloud computing are presented in this research. The taxonomy of nature-inspired scheduling techniques is categorized as per the scheduling algorithms, nature of the scheduling problem, type of tasks, the primary objective of scheduling, task-resource mapping scheme, scheduling constraint, and testing environment. Additionally, guidelines for future research issues are also provided, which should undoubtedly benefit researchers and practitioners as well as open the door for newcomers eager to pursue their glory in the field of cloud task scheduling.
... Then, the workload scheduler schedules the requested assignment on an appropriate VM on the basis of the accumulated information about the request (from the service supplier) and feedback data from RMs (point C)). Lastly, the workload scheduler assigns the selected VMs to PSs (point D) [37]. Two problems are actively being considered: (1) LB or efficient load scheduling among VMs, and (2) reducing the makespan of the workloads. ...
... Workload scheduling via VMs in the CC paradigm, adapted from[37]. ...
The Internet of things (IoT) extends the Internet space by allowing smart things to sense and/or interact with the physical environment and communicate with other physical objects (or things) around us. In IoT, sensors, actuators, smart devices, cameras, protocols, and cloud services are used to support many intelligent applications such as environmental monitoring, traffic monitoring, remote monitoring of patients, security surveillance, and smart home automation. To optimize the usage of an IoT network, certain challenges must be addressed such as energy constraints, scalability, reliability, heterogeneity, security, privacy, routing, quality of service (QoS), and congestion. To avoid congestion in IoT, efficient load balancing (LB) is needed for distributing traffic loads among different routes. To this end, this survey presents the IoT architectures and the networking paradigms (i.e., edge–fog–cloud paradigms) adopted in these architectures. Then, it analyzes and compares previous related surveys on LB in the IoT. It reviews and classifies dynamic LB techniques in the IoT for cloud and edge/fog networks. Lastly, it presents some lessons learned and open research issues.
... The glowworm swarm optimization (GSO) was used to solve the problem of task scheduling in cloud computing. The goal of this solution was to minimize the overall execution cost of jobs, while keeping the total completion time within the deadline [15]. According to the findings of a simulation, the GSO based task scheduling (GSOTS) algorithm achieved better results than the shortest task first (STF), the largest task first (LTF), and the particle swarm optimization (PSO) algorithms in terms of lowering the total completion time and the cost of executing tasks. ...
Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched.
... Strongest concentrations of the chemical present attracts glowworms that help in finding final optimization results. In [146] authors have employed GSO algorithm for task scheduling in the cloud. The aim is to minimize execution cost and keep total completion time under deadline. ...
... Response Time - Alboaneen et al. 2017 [146] Zhou et al. 2018 [147] Kim et al. 2014 [152] Abbasi-Tadi et al. 2016 [153] Tong et al. 2019 [154] Abdullahi et al. 2016 [155] Rahbari et al. 2017 [156] Abdullahi et al. 2019 [157] Ning et al. 2016 [158] Kaur et al. 2017 [159] Kumar et al. 2019 [160] Abdullahi et al. 2019 [161] Singh et al. 2019 [162] Chemistry-based Algorithms ...
Task scheduling is a critical issue in distributed computing environments like cloud and fog. The objective is to provide an optimal distribution of tasks among the resources. Several research initiatives to use metaheuristic techniques for finding near-optimal solutions to task scheduling problems are under way. This study presents a comprehensive taxonomic review and analysis of recent metaheuristic scheduling techniques using exhaustive evaluation criteria in the cloud and fog environment. A taxonomy of metaheuristic scheduling algorithms is presented. Besides, we have considered an extensive list of scheduling objectives along with their associated metrics. Rigorous evaluation of existing literature is performed, and limitations highlighted. We have also focused on hybrid algorithms as they tend to improve scheduling performance. We believe that this work will encourage researchers to conduct further research for removing the limitations in existing studies.
... In comparison, a Levy-flight strategy is applied to jumping out of local optima entrapment in the intensification phase. Similarly, the virtual machine (VM) assignment problem is addressed by Alboaneen, Tianfield, and Zhang (2016) and Ahmed, Tianfield, and Zhang (2017) using glowworm swarm optimization. Similarly, chicken swarm optimization uses a chaotic function to deal with multi-objective task scheduling in a cloud environment (Kiruthiga and Vennila 2020). ...
Virtual machine (VM) scheduling in a dynamic cloud environment is often bound with multiple quality of service parameters; therefore, it is classed as an NP-hard optimization problem. Swarm-based metaheuristics, such as the whale optimization algorithm (WOA), have gained a notable reputation for solving optimization problems. The unique bubble-net hunting behaviour and fast convergence of the algorithm led to the development of a hybrid multi-objective whale optimization algorithm-based differential evolution (M-WODE) technique to solve the VM scheduling problem. The differential evolution (DE) strategy is used to replace the randomly generated solution produced by the WOA to ensure diversity in the solution and to strengthen the local search of the M-WODE. In addition, the DE technique is applied to the Pareto front produced by the WOA to escape local optima entrapment problems. The experimental results showed that the proposed M-WODE outperformed previous algorithms in most cases on makespan and the cost trade-off.
... A. The First Experiment Using the NASA Dataset The dataset used for this experiment was the one from NASA [37,38], which has been used in various research such as in [39][40][41]. In this experiment, only the execution time was taken into account. ...
... The dataset used for this experiment was the one from NASA [37,38], which has been used in various research such as in [39][40][41]. In this experiment, only the execution time was taken into account. ...
Recently, there has been significant growth in the popularity of cloud computing systems. One of the main issues in building cloud computing systems is task scheduling. It plays a critical role in achieving high-level performance and outstanding throughput by having the greatest benefit from the resources. Therefore, enhancing task scheduling algorithms will enhance the QoS, thus leading to more sustainability of cloud computing systems. This paper introduces a novel technique called the dynamic round-robin heuristic algorithm (DRRHA) by utilizing the round-robin algorithm and tuning its time quantum in a dynamic manner based on the mean of the time quantum. Moreover, we applied the remaining burst time of the task as a factor to decide the continuity of executing the task during the current round. The experimental results obtained using the CloudSim Plus tool showed that the DRRHA significantly outperformed the competition in terms of the average waiting time, turnaround time, and response time compared with several studied algorithms, including IRRVQ, dynamic time slice round-robin, improved RR, and SRDQ algorithms.
... In our earlier works, GSO algorithm has been applied for dynamic VM placement [50] and for task scheduling [51], separately. In this article, the integration of task scheduling and VM placement problems, as one co-optimization problem is proposed in order to produce better optimization of joint task scheduling and VM placement in cloud data center. ...
The virtual machine (VM) allocation problem is one of the main issues in cloud data centers. This article proposes a new metaheuristic method to optimize joint task scheduling and VM placement (JTSVMP) in cloud data center. The JTSVMP problem, though composed of two parts, namely task scheduling and VM placement, is treated as a joint problem to be resolved by using metaheuristic optimization algorithms (MOAs). The proposed co-optimization process aims to schedule task into the VM which has the least execution cost within deadline constraint and then to place the selected VM on most utilized physical host (PH) within capacity constraint. To evaluate the performance of our proposed co-optimization process, we compare the performances of two different scenarios, i.e., task scheduling algorithms and integration co-optimization of task scheduling and VM placement using MOAs, namely
the basic glowworm swarm optimization (GSO), moth-flame glowworm swarm optimization (MFGSO) and genetic algorithm (GA). Simulation results show that optimizing joint task scheduling and VM placement leads to better overall results in terms of minimizing execution cost, makespan and degree of imbalance and maximizing PHs resource utilization.
