A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computing

Jain, Richa; Sharma, Neelam

doi:10.1007/s10586-022-03740-x

Cited by 20 publications

(7 citation statements)

References 49 publications

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“…The designed DSA is also can be used to optimize the hyper-parameters of the deep learning models for the image classification. The feature selection problem and node location of the WSN also can be applied by the proposed inspired algorithm, which is considered the quantum theory [74].…”

Section: Discussionmentioning

confidence: 99%

Duck swarm algorithm: theory, numerical optimization, and applications

Zhang,

Wen

2024

Cluster Comput

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A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study. This algorithm is inspired by the searching for food sources and foraging behaviors of the duck swarm. The performance of the DSA is verified by using eighteen benchmark functions, where its statistical (best, mean, standard deviation, and average runningtime) results are compared with seven well-known algorithms like Particle swarm optimization (PSO), Firefly algorithm (FA), Chicken swarm optimization (CSO), Grey wolf optimizer (GWO), Sine cosine algorithm (SCA), and Marine-predators algorithm (MPA), and Archimedes optimization algorithm (AOA). Moreover, the Wilcoxon rank-sum test, Friedman test, and convergence curves of the comparison results are used to prove the superiority of the DSA against other algorithms. The results demonstrate that DSA is a high-performance optimization method in terms of convergence speed and exploration-exploitation balance for solving high-dimension optimization functions. Also, DSA is applied for the optimal design of six engineering constraint problems and the node optimization deployment task of the Wireless Sensor Network (WSN). Overall, the comparison results revealed that the DSA is a promising and very competitive algorithm for solving different optimization problems.

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Section: Discussionmentioning

confidence: 99%

Duck swarm algorithm: theory, numerical optimization, and applications

Zhang,

Wen

2024

Cluster Comput

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“…GSAGA [28] combined GA with Gravitational Search Algorithm (GSA) to improve the resource cost for processing tasks in a cloud. QSSGWA [29] used quantum operator to improve the population initialization, and combined Salp Swarm Algorithm (SSA) with Grey Wolf Optimizer (GWO), which applied position update strategies of SSA and GWO for the first and second half of the population, respectively. To optimize makespan, Cheikh et al [30] proposed a hybrid heuristic scheduling algorithm by combining PSO and Extremal Optimization (EO), which used PSO to provide the initial solution for EO.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Combination of Genetic Algorithm and Particle Swarm Optimization for Scheduling Data-Intensive Tasks in Heterogeneous Cloud Computing

Shao

Wang

2023

Electronics

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Task scheduling is still an open issue for improving the performance of cloud services. Focusing on addressing the issue, we first formulate the task-scheduling problem of heterogeneous cloud computing into a binary non-linear programming. There are two optimization objectives including the number of accepted tasks and the overall resource utilizations. To solve the problem in polynomial time complexity, we provide a hybrid heuristic algorithm by combing both benefits of genetic algorithm (GA) and particle swarm optimization (PSO), named PGSAO. Specifically, PGSAO integrates the evolution strategy of GA into PSO to overcome the shortcoming of easily trapping into local optimization of PSO, and applies the self-cognition and social cognition of PSO to ensure the exploitation power. Extensive simulated experiments are conducted for evaluating the performance of PGSAO, and the results show that PGSAO has 23.0–33.2% more accepted tasks and 27.9–43.7% higher resource utilization than eight other meta-heuristic and hybrid heuristic algorithms, on average.

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“…Technique Used Addressed Parameters [7] APSO Makespan, throughput [8] LJ-PSO, M-PSO makespan, total execution time, degree of imbalance [9] GAGELS makespan, resource utilization [10] MPSO Makespan, resource utilization [11] IT2FCM Data movements, data placement, makespan [12] PSO-RDAL Response time, task deadline, penalty cost [13] EPSOCHO Makespan, processing cost, resource utilization [14] GSOS Makespan, cost [15] AINN-BPSO makespan, cost, degree of imbalance [16] QPSO Scheduling efficiency [17] MVO-GA Task transfer time [18] NSGAIII runtime, cost, power consumption [19] Hybrid Lion-GA Load balancing [20] GSAGA Makespan [21] GBO Makespan, accuracy of scheduling [22] HWOA-MBA Makespan, cost [23] IWHOLF-TSC Makespan, cost [24] HWACOA Makespan, cost ELHHO Schedule length, execution cost, resource utilization [28] RATSA Failure rate [29] SOATS Cost, energy consumption [30] HunterPlus Energy consumption, job completion rate [31] IQSSA QOS parameters [32] RAO Makespan [33] HFSGA Makespan, cost [34] DRL Makespan, throughput [35] IMOMVO Execution time, throughput [36] HBSFD Task processing time, turnaround time [37] Wale Disk space [38] Docker Containers Disk space…”

Section: Authorsmentioning

confidence: 99%

“…In the above equation, Y, Z are calculated in Equations ( 26) and ( 27) respectively. For hard besiege with constructive or incremental steps, updation is performed by using the following Equation (31):…”

Section: Fault-tolerant Trust-aware Task Scheduler (Fttats) Using Har...mentioning

confidence: 99%

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Mangalampalli,

Karri,

Gupta

et al. 2023

Sensors

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Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.

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A quantum inspired hybrid SSA–GWO algorithm for SLA based task scheduling to improve QoS parameter in cloud computing

Cited by 20 publications

References 49 publications

Duck swarm algorithm: theory, numerical optimization, and applications

Duck swarm algorithm: theory, numerical optimization, and applications

An Efficient Combination of Genetic Algorithm and Particle Swarm Optimization for Scheduling Data-Intensive Tasks in Heterogeneous Cloud Computing

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

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