Network-aware Virtual Machine Migration Based on Gene Aggregation Genetic Algorithm

Jiang, Yi; Wang, Jinjin; Shi, Jieke; Zhu, Jun; Teng, Ling

doi:10.1007/s11036-019-01376-7

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Since the traditional particle swarm algorithm (PSO) is applicable to continuous problems, Ibrahim et al [15] used a discrete version of particle swarm optimization algorithm (PAPSO) based on decimal coding to map the migrated VMs to the most suitable physical machines, which can reduce energy consumption without violating SLAs. In [16], a gene aggregation genetic based VM migration algorithm VMM-GAGA was designed by improving the genetic algorithm (GA) encoding. The authors synthesized a set of chromosomes as genes in two virtual machines with small footprint but high communication and migrated them to the same host with low utilization.…”

Section: Related Workmentioning

confidence: 99%

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A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

Mao

Chen

Cheng

et al. 2023

Preprint

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With the continuous growth of cloud computing services, the high energy consumption of cloud data centers has become an urgent problem to be solved. Virtual machine consolidation (VMC) is an important way to optimize energy consumption, however excessive consolidation may lead to local hotspots and increase the risk of equipment failure. Thermal-aware scheduling can solve this problem, but it is difficult to strike a balance between SLA and energy consumption. To solve the above problems, we propose a method for scheduling cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hotspots from a global perspective. It includes four phases of the VM consolidation process, dynamically schedules VMs by detecting server temperature and utilization status in real time, and finds suitable target hosts based on an improved ant colony algorithm (UACO) for the VMs. We compare the TM-VMC approach with several existing mainstream VM consolidation algorithms under workloads from real-world data centers. Simulation experimental results show that the TM-VMC approach can proactively avoid data center hotspots and significantly reduce energy consumption while maintaining low SLA violation rates.

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Section: Related Workmentioning

confidence: 99%

“…The larger the 𝜌, the less pheromone remains. 𝜏 0 is the initial pheromone amount, in this paper, it is calculated according to formula (16). ACO is easy to fall into local optimum due to its positive feedback characteristics.…”

Section: Pheromone Update Rulementioning

confidence: 99%

A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

Mao

Chen

Cheng

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Many dynamic resource management solutions utilize live migration as a tool to achieve objectives, such as loadbalancing [2], [3], [4], [24], energy efficiency [13], [14], network delay [15], and communication cost [16]. Among these solutions, some resource management algorithms consider a linear model of the total migration overheads as the sum of individual migration overhead [2], [3], [4], [5], [12], [14], [24].…”

Section: Related Workmentioning

confidence: 99%

“…Figure 1 illustrates the general migration management workflow. Based on the various objectives, the resource management algorithms [2], [3], [4], [5], [12], [13], [14], [15], [16] find the optimal placement by generating multiple live migrations. With the generated multiple migration requests, the migration planning and scheduling algorithm [17], [18], [19], [20] optimizes the performance of multiple migrations, such as total and individual migration time and downtime, while minimizing the migration cost and overheads, such as migration impact on application QoS.…”

Section: Introductionmentioning

confidence: 99%

CAMIG: Concurrency-Aware Live Migration Management of Multiple Virtual Machines in SDN-enabled Clouds

He,

Toosi,

Buyya

2021

Preprint

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By integrating Software-Defined Networking and cloud computing, virtualized networking and computing resources can be dynamically reallocated through live migration of Virtual Machines (VMs). Dynamic resource management such as load balancing and energy-saving policies can request multiple migrations when the algorithms are triggered periodically. There exist notable research efforts in dynamic resource management that alleviate single migration overheads, such as single migration time and co-location interference while selecting the potential VMs and migration destinations. However, by neglecting the resource dependency among potential migration requests, the existing solutions of dynamic resource management can result in the Quality of Service (QoS) degradation and Service Level Agreement (SLA) violations during the migration schedule. Therefore, it is essential to integrate both single and multiple migration overheads into VM reallocation planning. In this paper, we propose a concurrency-aware multiple migration selector that operates based on the maximal cliques and independent sets of the resource dependency graph of multiple migration requests. Our proposed method can be integrated with existing dynamic resource management policies. The experimental results demonstrate that our solution efficiently minimizes migration interference and shortens the convergence time of reallocation by maximizing the multiple migration performance while achieving the objective of dynamic resource management.

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“…When the VMs runs too many tasks, the host will be overloaded and exception occurs. Regarding the issue, Jiang et al [3] consider the communication cost of virtual machine (VM) migration and proposes a VM Migration Algorithm based on Gene Aggregation Genetic Algorithm (VMM-GAGA). In [4], the authors propose an end-to-end infrared small target detection model (called CDAE) based on denoising autoencoder network and convolutional neural network, which treats small targets as "noise" in infrared images and transforms small target detection tasks into denoising problems.…”

mentioning

confidence: 99%

Cognitive Computing for Intelligence Systems

2020

Mobile Netw Appl

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Cognitive Computing breaks the boundary between two separate fields, neuroscience and computer science. It paves the way for machines to have reasoning abilities which is analogous to human. The research field of cognitive computing is interdisciplinary, and uses knowledge and methods from many areas such as psychology, biology, signal processing, physics, information theory, mathematics, and statistics. The development of cognitive computing will keep crossfertilizing these research areas. However, in multimedia applications there still remain many open problems for cognitive computing. Technologies like cloud computing and big data are essential to upgrade the web systems with near human intelligence by using new capabilities such as machine learning, cognitive sensing, data mining, pattern recognition and natural language processing.The objective of this special section is to provide a platform for researchers to share their thoughts and findings on various issues involved in artificial intelligence and networks. Authors of papers presented at the EAI International Conference on Robotic Sensor Networks (ROSENET -http://rosenets.org/) were invited to submit their substantially expanded papers. ROSENET conference had received over 30 papers from over 5 countries in the world. After a careful review process the 7 papers presented in this special section were selected based on their originality, significance, technical soundness and clarity of exposition.Virtual Reality (VR) sickness presents an important challenge in VR environments. The authors of "Virtual reality sickness and challenges behind different technology and content settings" [1] present the results of a study on of the effects of VR technology and VR video content type on VR sickness

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Network-aware Virtual Machine Migration Based on Gene Aggregation Genetic Algorithm

Cited by 7 publications

References 19 publications

A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

A Resource Scheduling Method for Cloud Data Centers Based on Thermal Management

CAMIG: Concurrency-Aware Live Migration Management of Multiple Virtual Machines in SDN-enabled Clouds

Cognitive Computing for Intelligence Systems

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