An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

Liu, Xiao-Fang; Zhan, Zhi-Hui; Zhang, Mengjie

doi:10.3390/en10050609

Cited by 32 publications

(17 citation statements)

References 38 publications

(58 reference statements)

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“…Despite the numerous works in [22,81,95,96] which carried out on green cloud computing and provided potential solutions be shown as the adoption of software and hardware for decreasing energy consumption, power-saving using VM techniques, various energy-efficient resource allocation mechanisms and related tasks, and efficient methods for energy-saving systems. The authors in [82] explored the trade-off of the energy performance for consolidation, which resulted in the desired workload distribution across servers and saves energy.…”

Section: Smart Data Center For Smart Citiesmentioning

confidence: 99%

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

et al. 2021

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The development of the Internet of Things (IoT) technology and their integration in smart cities have changed the way we work and live, and enriched our society. However, IoT technologies present several challenges such as increases in energy consumption, and produces toxic pollution as well as E-waste in smart cities. Smart city applications must be environmentally-friendly, hence require a move towards green IoT. Green IoT leads to an eco-friendly environment, which is more sustainable for smart cities. Therefore, it is essential to address the techniques and strategies for reducing pollution hazards, traffic waste, resource usage, energy consumption, providing public safety, life quality, and sustaining the environment and cost management. This survey focuses on providing a comprehensive review of the techniques and strategies for making cities smarter, sustainable, and eco-friendly. Furthermore, the survey focuses on IoT and its capabilities to merge into aspects of potential to address the needs of smart cities. Finally, we discuss challenges and opportunities for future research in smart city applications.

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Section: Smart Data Center For Smart Citiesmentioning

confidence: 99%

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

et al. 2021

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“…It must also improve resource utilization while trying to satisfy service level agreements (SLAs). Genetic algorithm, Ant Colony algorithm, linear programming, adaptive heuristics, and utility-based approaches are proposed for resource scheduling and VM migration [24][25][26][27][28][29][30][31][32][33][34]. For typical air-cooled data centers, researchers propose thermal-aware workload allocation strategy with respect to the chip temperature constraint [35] and use computational fluid dynamics (CFD) to model and validate Airflow in a Data Center [36].…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Virtual Machine Scheduling in Data Centers

et al. 2019

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Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

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“…In the context of MOP, preferences are used as weights for conflicting objectives to obtain lexicographic order of objectives [31] or to favor one of the objectives [32]. This is a typical approach in many MOPs as the number of potential solutions increases dramatically with the size of the problem and the number of objectives [14].…”

Section: Related Workmentioning

confidence: 99%

Incorporating Ceteris Paribus Preferences in Multiobjective Virtual Machine Placement

Alashaikh

Alanazi

2019

IEEE Access

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Proper handling of preferences in multiobjective evolutionary algorithms is essential for the algorithms' success in real-life applications. While there has been a tremendous work addressing preferences in evolutionary algorithms, the issue of the exact interpretation of decision maker's (DM) preferences and how it affects the performance of evolutionary algorithms has received little attention. One interpretation of preferences that has received significant attention lately by the AI community and is believed to be exercised naturally by decision makers is the Ceteris Paribus (all else being equal) interpretation. In this paper, we adopt the notion of Ceteris Paribus as an interpretation for the DM preferences and incorporate it in a constrained multiobjective problem known as virtual machine placement (VMP). VMP is an essential multiobjective problem in the design and operation of cloud data centers concerned about placing each virtual machine to a physical machine (a server) in the data center. We propose a variant of the NSGA-II that promotes Ceteris Paribus preferred solutions and evaluate its applicability. Our experiment results show that this variant was able to return preferred solutions at almost no extra cost when compared to NSGA-II.

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An Energy Aware Unified Ant Colony System for Dynamic Virtual Machine Placement in Cloud Computing

Cited by 32 publications

References 38 publications

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

Energy Aware Virtual Machine Scheduling in Data Centers

Incorporating Ceteris Paribus Preferences in Multiobjective Virtual Machine Placement

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