Online and offline based load balance algorithm in cloud computing

Tang, Linlin; Li, Zuohua; Ren, Pingfei; Pan, Jeng‐Shyang; Lu, Zhe‐Ming; Su, Jingyong; Meng, Zhenyu

doi:10.1016/j.knosys.2017.09.040

Cited by 20 publications

(7 citation statements)

References 18 publications

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“…Optimization algorithms play a crucial role in enhancing resource management in cloud computing, particularly in the context of task scheduling, virtual machine allocation, and migration [180], [186]- [191]. The objective is to optimize a designated objective evaluation function that guides the decision-making process [127], [192]- [194].…”

Section: Optimization Algorithm-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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Section: Optimization Algorithm-based Managementmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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“…According to the relationship between individuals, they can be further classified as evolutionary and swarm intelligence algorithms. Evolutionary algorithms, such as bacterial foraging optimization (BFO) [28,29], genetic algorithms (GA) [30], and their variants, are inspired by evolution theory and the candidate solutions in each iteration are updated based on selection, crossover, and mutation operations. In swarm intelligence algorithms, however, the candidate solutions survive in the whole searching process and their fitness values are optimized by exchanging information with each other.…”

Section: Scheduling Algorithmsmentioning

confidence: 99%

Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments

et al. 2022

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Cloud computing has been studied and used extensively in many scenarios for its nearly unlimited resources and X as a service model. To reduce the latency for accessing the remote cloud data centers, small data centers or cloudlets are deployed near end-users, which is also called edge computing. In this paper, we mainly focus on the efficient scheduling of distributed simulation tasks in collaborative cloud and edge environments. Since simulation tasks are usually tightly coupled with each other by sending many messages and the status of tasks and hosts may also change frequently, it is essentially a dynamic bin-packing problem. Unfortunately, popular methods, such as meta-heuristics, and accurate algorithms are time-consuming and cannot deal with the dynamic changes of tasks and hosts efficiently. In this paper, we present Pool, an incremental flow-based scheduler, to minimize the overall communication cost of all tasks in a reasonable time span with the consideration of migration cost of task. After formulating such a scheduling problem as a min-cost max-flow (MCMF) problem, incremental MCMF algorithms are adopted to accelerate the procedure of calculating an optimal flow and heuristic scheduling algorithm, with the awareness of task migration cost, designed to assign tasks. Simulation experiments on Alibaba cluster trace show that Pool can schedule all of the tasks efficiently and is almost 5.8 times faster than the baseline method when few tasks and hosts change in the small problem scale.

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“…Since the algorithm ○ 1 and algorithm ○ 2 cannot be applied to the integrated scheduling problem with the same equipment characteristics, this paper needs to make appropriate modifications to the algorithm ○ 1 and algorithm ○ 2 under not changing the characteristics of the algorithm itself. On the basis of the original algorithm strategy, the early processing strategy and equipment balancing strategy [23][24][25] are introduced into the algorithm ○ 1 and algorithm ○ 2 . Among which, the early processing strategy is to load the process with multiple machinable equipment to the equipment with the earliest processing time; The equipment balancing strategy is that when a process with multiple machinable equipment has the same earliest start processing time on these equipment, the process is selected to be processed on the equipment with the smallest sum of the processing time.…”

Section: ) Design Of the Root-subtree Vertical And Horizontal Advanta...mentioning

confidence: 99%

A Flexible Integrated Scheduling Algorithm for Dynamic Determination Processing Equipment Considering the Root-Subtree Vertical and Horizontal Pre-Scheduling

et al. 2022

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Aiming at the existing flexible integrated scheduling algorithms that use unified rules to solve the conflicts between processes and equipment, and ignore the vertical and horizontal characteristics of the process relative positions in the process tree, a flexible integrated scheduling algorithm for dynamic determination processing equipment considering the Root-Subtree vertical and horizontal pre-scheduling (FISA-DEVH) is proposed. The process sorting strategy of the Root-Subtree vertical and horizontal prescheduling is proposed to sort the Root-Subtree processes of the same planned processing equipment in groups and load the Root-Subtree processes into the corresponding equipment queues, so as to fully explore the vertical and horizontal characteristics of the process. In order to give full play to the characteristics of the flexible equipment and realize the effective parallel processing between the processes, the flexible equipment optimal scheduling strategy of dynamically determining process is proposed to determine the final processing equipment of the process at each equipment driving moment and complete the production scheduling process from the Root-Subtree static pre-scheduling to dynamic fusion; The strategy uses the scheduling relationship between the unplanned processes and unplanned equipment to reduce the search scope of the combination scheme and improve the efficiency of the algorithm. The example tests show that FISA-DEVH has a better scheduling effect than the flexible integrated scheduling algorithm that processes the process tree with unified rules, and has universal significance for solving the flexible integrated scheduling problem.INDEX TERMS Flexible integrated scheduling, Root-Subtree vertical and horizontal pre-scheduling, the same planned processing equipment, the unplanned processes, the unplanned equipment

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Online and offline based load balance algorithm in cloud computing

Cited by 20 publications

References 18 publications

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments

A Flexible Integrated Scheduling Algorithm for Dynamic Determination Processing Equipment Considering the Root-Subtree Vertical and Horizontal Pre-Scheduling

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