Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Yuan, Chunmiao; Sun, Xi

doi:10.3390/s19122724

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Sayadnavard et al [31] proposed a server consolidation method based on multiple resource constraints, but the optimization goal is to minimize the number of hosts used by the VM placement, and it ignores other types of costs. Yuan et al [32] used the culture multiple-ant-colony algorithm to solve the server consolidation problem without SLAV constraints.…”

Section: Server Consolidation Solutionsmentioning

confidence: 99%

More than Meets One Core: An Energy-Aware Cost Optimization in Dynamic Multi-Core Processor Server Consolidation for Cloud Data Center

Wen

Liu

et al. 2022

Electronics

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The massive number of users has brought severe challenges in managing cloud data centers (CDCs) composed of multi-core processor that host cloud service providers. Guaranteeing the quality of service (QoS) of multiple users as well as reducing the operating costs of CDCs are major problems that need to be solved. To solve these problems, this paper establishes a cost model based on multi-core hosts in CDCs, which comprehensively consider the hosts’ energy costs, virtual machine (VM) migration costs, and service level agreement violation (SLAV) penalty costs. To optimize the goal, we design the following solution. We employ a DAE-based filter to preprocess the VM historical workload and use an SRU-based method to predict the computing resource usage of the VMs in future periods. Based on the predicted results, we trigger VM migrations before the hosts move into the overloaded state to reduce the occurrence of SLAV. A multi-core-aware heuristic algorithm is proposed to solve the placement problem. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. Compared with the existing baseline methods, our proposed method reduces the total operating cost by 20.9~34.4%.

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Section: Server Consolidation Solutionsmentioning

confidence: 99%

More than Meets One Core: An Energy-Aware Cost Optimization in Dynamic Multi-Core Processor Server Consolidation for Cloud Data Center

Wen

Liu

et al. 2022

Electronics

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“…But the objective of minimizing VMMs easily causes host overloading risk, further increasing SLA violations. Yuan and Sun [22] proposed a VM consolidation algorithm based on the culture multiple-ant-colony algorithm. In this algorithm, VM of the low utilization physical machine consolidates into the more running physical machine according to the current resource situation, so as to reduce the number of working PMs.…”

Section: Vm Consolidationmentioning

confidence: 99%

A Strategy Game System for QoS-Efficient Dynamic Virtual Machine Consolidation in Data Centers

Zhao

2019

IEEE Access

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In this paper, we develop a model for the competitive relationship between cloud service providers and cloud brokers during the virtual machine (VM) placement. The model adopts the idea of a strategy game, in which the cloud service provider and all VMs are participants. First, we define the strategy for the participants in the strategy game model according to the difference between the number of physical resources provided by the provider, and that requested for the VMs by the brokers. Next, payment values between the providers and brokers are obtained from each strategy in the game matrix, which corresponds to the strategy set of VM placement. In addition, the Nash equilibrium solution of the current strategy game demonstrates the trade-off between reducing energy consumption and guaranteeing QoS. On the basis of this, the optimal VM placement scheme is obtained, and the ongoing VM consolidation is examined. Finally, a strategy-game-based VM consolidation (SG-VMC) method is proposed. The extensive experimental results show that the proposed SG-VMC method can significantly reduce energy consumption while guaranteeing QoS.INDEX TERMS Game theory, strategy game model, VM placement, VM consolidation.

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“…In this paper, the particle swarm optimization (PSO) algorithm is used to find the optimal number of hidden layer nodes. Common intelligent search algorithms include the genetic algorithm [36,37], ant colony algorithm [38,39], simulated annealing algorithm [40], and particle swarm optimization. The genetic algorithm cannot effectively converge in a limited time.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO

Tan

Zuo

et al. 2019

Sensors

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With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.

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Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Cited by 9 publications

References 24 publications

More than Meets One Core: An Energy-Aware Cost Optimization in Dynamic Multi-Core Processor Server Consolidation for Cloud Data Center

More than Meets One Core: An Energy-Aware Cost Optimization in Dynamic Multi-Core Processor Server Consolidation for Cloud Data Center

A Strategy Game System for QoS-Efficient Dynamic Virtual Machine Consolidation in Data Centers

Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO

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