RETRACTED ARTICLE: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

Ramamoorthy, S.; Ravikumar, G.; Balakrishnan, S.; Venkatachalam, K.

doi:10.1007/s12652-020-02138-0

Cited by 21 publications

(14 citation statements)

References 22 publications

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“…Based on the "Deep Q-network (DQN) algorithm", Zhiping Peng et al [13] suggested an online resource scheduling system. By modifying the proportion of the benefit of two optimization targets, the framework may make a trade-off between the two optimization goals of energy usage and task make-span.…”

Section: Related Workmentioning

confidence: 99%

Machine Intelligence by Central Pivot Ranges (MICPR): An Optimal Resource Scheduling Strategy for Cloud Services

Rao

Sastry

2022

Preprint

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Alongside the increasing demand for cloud computing solutions, the other critical factor in focusing on cloud computing solutions is effective cloud resource sensing and scheduling. In the real-time scenario, there are numerous resource scheduling models available to support effective managing resource scheduling. However, resource sensing and scheduling challenges remains in the increasing complexities of background processing in the cloud servers. Thus, in this manuscript, a machine intelligence model that provides significant insights into how the load balance can be sensed and handled using the sever performance's critical metrics is proposed. The model is an automated machine intelligence (MI) approach that uses pivot range (CPR), support, and resistance models for estimating if the load of the system as per the metrics chosen is running heavy load or normal. Accordingly, the decision can be triggered automatically by MICPR to choose the fundamental resource sensing and scheduling algorithms. The experimental study of the model refers to potential ways in which the system can be feasible, and if implemented on a large scale, it can help reduce the backend load on the cloud servers.

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Section: Related Workmentioning

confidence: 99%

Machine Intelligence by Central Pivot Ranges (MICPR): An Optimal Resource Scheduling Strategy for Cloud Services

Rao

Sastry

2022

Preprint

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“…For a VM or physical node, CPU and memory are two important resources that are often used to measure its load or utilization in the construction of scheduling models, such as the literatures [14,22,[25][26][27][28][29][30][31][32][33][34][35][36][37]. Because…”

Section: Load Of a Vm Or Physical Nodementioning

confidence: 99%

“…In [22], memory and network bandwidth were used to measure the load of a physical node. [36] leveraged RAM, storage and bandwidth to evaluate the resource utilization of a physical node. CPU, memory and storage are three factors to measure the capacity of each physical server when building a non-preemptive VM scheduling model in [37].…”

Section: Load Of a Vm Or Physical Nodementioning

confidence: 99%

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

Zhang

Yan

2021

Preprint

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Efficient resource scheduling is one of the most critical issues for big datacenters in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

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“…Therefore, the cloud edge hybrid network model can be simplified to a multi-edge node model that can communicate with each other in a certain area. 20,21 Suppose that there are n edge nodes in the cloud edge hybrid network in a certain area, and the set is N = 1, 2, … , n. Edge nodes can communicate with each other, and their undirected links can be expressed as (i, j), ∀i, j ∈ N. Because all the edge nodes in the region can communicate with each other, there are n(n − 1)∕2 undirected links, which can be expressed as G = (i, j), ∀i, j ∈ N.…”

Section: Resource Scheduling Optimization Problem With Constraintsmentioning

confidence: 99%

An effective resource scheduling model for edge cloud oriented AIoT

Liang

Cui

Cao

et al. 2021

Concurrency and Computation

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Artificial Intelligence of Things (AIoT) is a new research area in AI and IoT. For the massive data and connections at the edge of AIoT, how to schedule the resource load is a key problem to be solved. For the multi-objective resource scheduling problem in a cross-domain environment, this article proposes a resource scheduling method based on edge computing and multi-objective algorithms. First, a cloud-edge hybrid AIoT hierarchical network resource management architecture is constructed. Then, the resource scheduling problem is modeled and a linear weighting strategy is designed for search space "pruning", which is then optimized by three multi-objective algorithms. Finally, we conducted a series of experiments based on the iFogSim platform. The simulation results show that the proposed resource scheduling strategy can effectively reduce the processing delay of AIoT and effectively improve the energy utilization efficiency of devices in the network.

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RETRACTED ARTICLE: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

Cited by 21 publications

References 22 publications

Machine Intelligence by Central Pivot Ranges (MICPR): An Optimal Resource Scheduling Strategy for Cloud Services

Machine Intelligence by Central Pivot Ranges (MICPR): An Optimal Resource Scheduling Strategy for Cloud Services

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

An effective resource scheduling model for edge cloud oriented AIoT

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