SLA-based task scheduling algorithms for heterogeneous multi-cloud environment

Panda, Sanjaya Kumar; Jana, Prasanta K.

doi:10.1007/s11227-016-1952-z

Cited by 68 publications

(15 citation statements)

References 31 publications

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“…The outputs of the explained SLA-MCT are compared with three single-phase job scheduling algorithms and the following are CLS, Execution-MCT, and Profit-MCT, and the results of SLA-Min-Min are compared with two-phase scheduling algorithms, which are Execution-Min-Min and Profit-Min-Min in terms of four process metrics, namely manufacturing time, mean cloud usage, profit, and penalty cost of the services. The output proves that this algorithm effectively maintains a balance between manufacturing time and profit cost of the services [11].…”

Section: Types Of Load Balancing Algorithmsmentioning

confidence: 80%

OCSA: Task Scheduling Algorithm in Cloud Computing Environment

Pradeep

Jacob²

2018

IJIES

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Cloud computing offers dynamic allocation of resources on demand, the feature which makes it to stand apart providing great performance, scalability, cost efficient and less maintenance, thus making it an apt choice. Task scheduling becomes the essential factor in increasing the performance for the dynamic allocation of resources which is most essential in the cloud environment to increase performance and decrease the cost. In this work, a solution is proposed using makespan and cost, taking them as important constraints for the optimization problem. We have merged two algorithms namely, cuckoo search algorithm (CSA) and oppositional based learning (OBL) and created a new hybrid algorithm called oppositional cuckoo search algorithm (OCSA) to provide solution to the above stated issue. Our proposed OCSA algorithm showed noticeable improvement over the other task scheduling algorithms. The proposed work is simulated in cloudsim programming environment and the simulation results show the effectiveness of the proposed work by minimizing cost and makespan parameters. The obtained results are better in comparison to other existing algorithms like particle swarm optimization (PSO), Improved Differential Evolution Algorithm (IDEA) and genetic algorithm (GA).

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Section: Types Of Load Balancing Algorithmsmentioning

confidence: 80%

OCSA: Task Scheduling Algorithm in Cloud Computing Environment

Pradeep

Jacob²

2018

IJIES

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“…With virtualization technology, all physical resources in the cloud environment are represented as virtual machines (VMs) [70]. Hence, cloud providers must supply their customers with infinite virtualized resources in accordance with the service level agreement (SLA) [71] and must decide the best resource configuration to be utilized according to their profitability principles.…”

Section: Model and Problem Formulationmentioning

confidence: 99%

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Attiya

Elaziz

Xiong

2020

Computational Intelligence and Neuroscience

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In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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“…He has demonstrated the SLA violation level in a multi-cloud environment during service delivery and also noted various challenges. Panda and Jana proposed two SLA-based task scheduling algorithms to improve various parameters in a multi-cloud environment [15] [37]. The expected time to compute matrix is used to propagate the expected time for each task in all virtual machines [16] .…”

Section: Related Workmentioning

confidence: 99%

Energy-Aware Task Allocation for Multi-Cloud Networks

Mishra

Alsayat

et al. 2020

IEEE Access

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In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks (ETAMCN)) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization (CZSN) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLAviolation of ETAMCN for different scenarios is performed.

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SLA-based task scheduling algorithms for heterogeneous multi-cloud environment

Cited by 68 publications

References 31 publications

OCSA: Task Scheduling Algorithm in Cloud Computing Environment

OCSA: Task Scheduling Algorithm in Cloud Computing Environment

Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

Energy-Aware Task Allocation for Multi-Cloud Networks

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