A Dynamic Task Scheduling Algorithm Improved by Load Balancing in Cloud Computing

Ebadifard, Fatemeh; Babamir, Seyed Morteza; Barani, Sedighe

doi:10.1109/icwr49608.2020.9122287

Cited by 32 publications

(8 citation statements)

References 13 publications

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“…The proposed scheduling strategy is evaluated on GR-NET's cloud service okeanos-knossos, which provides a wide range of choices to develop, debug and evaluate an experimental system. We ran 3 experiments to compare strategy with 3 newly proposed schemes: the Load Balancing Modified Particle Swarm Optimization (LBMPSO) [44], the Honey bee foraging (HBF) with VM pre-selection [49], and the community-based Particle Swarm Optimization (CPSO) scheme introduced in [56]. These schemes are multiobjective; the first aims at enhancing the makespan and the resource utilization, the second aims at enhancing the makespan, the degree of LB, and the response time, while the third aims at reducing the processing costs and the response time.…”

Section: Resultsmentioning

confidence: 99%

“…An agent based strategy is proposed in [48]; the agents assigned to resources learn to select the best sequence of the tasks that can optimize the total makespan of the workflow, enhance utilization of resources, and improve load balancing between resources. In [49], the notion of fair distribution of the workload among the VMs is considered. In contrast to the single honey bee foraging behavior (which first uses round robin to assign tasks to virtual machines and then balance their workload), this scheme first makes the VM selection by checking its load.…”

Section: Related Workmentioning

confidence: 99%

“…Honey foraging [49] CPSO [56] FIGURE 7. Comparison of the response times between our scheme, the HBF [49], and the CPSO [56] strategies.…”

Section: Conclusion -Future Workmentioning

confidence: 99%

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A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

et al. 2022

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Load balancing techniques in cloud computing can be applied at three different levels: Virtual machine load balancing, task load balancing, and resource load balancing. At all levels, load balancing should also be implemented in an efficient manner, to increase system performance. In this paper, we propose a fair, in terms of added workload per VM, task load balancing strategy, that aims to improve the average response time and the makespan of the system in the cloud environment. The problem is formulated as an irreducible finite state Markov process, which is known to have a balance equation for each state. From the balance state probabilities we derive the expected utilizations for the virtual machines (VM), which play a vital role in our task allocation approach. In our model, the Load Balancer (LBer) acts as a central server, which uses our proposed fair task allocation scheme to distribute the incoming tasks in a fair, balanced manner among the virtual machines, taking into account their current state as well as their processing capabilities. Our scheme has been compared to recent algorithms that use the particle swarm optimization and the Honey bee foraging scheme to achieve load balancing. Our experimental results show that our proposed scheme outperforms other state of the art schemes in terms of makespan, average response time, and resource utilization and provides lower degree of imbalance.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

et al. 2022

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“…Cluster computing [64] 2020 Web research [65] 2020 Electrical and computer engineering innovations [66] 2020 Arab journal of information technology [67] 2021 Computing and digital system [69] 2015 Applied engineering research [70] 2017 Research journal of engineering and technology [71] 2020 Computers and applications [72] 2021 Concurrency and computation: practice and experience…”

Section: Researchmentioning

confidence: 99%

“…Generally, the proposed work aims to develop effective cloud architecture in order to tolerate faults, suggest appropriate solutions to maintain data, and make the system more reliable and flexible. A task scheduling approach based on the honeybee algorithm aiming to load balancing has been proposed in [64]. In order to minimize load redundancy, available tasks are sent to the most proper VMs.…”

Section: A Review Of Distributed Approachesmentioning

confidence: 99%

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

et al. 2022

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Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to detect under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, response time, reliability, availability, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.

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A Comparative Analytics for Dynamic Load Balancing Mechanisms Intended to Improve Task Scheduling in Cloud Computing

Patel,

Patel

et al. 2024

Communications in Computer and Information Science

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A Dynamic Task Scheduling Algorithm Improved by Load Balancing in Cloud Computing

Cited by 32 publications

References 13 publications

A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

A Comparative Analytics for Dynamic Load Balancing Mechanisms Intended to Improve Task Scheduling in Cloud Computing

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