The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

Pourghebleh, Behrouz; Anvigh, Amir Aghaei; Ramtin, Amir Reza; Mohammadi, Behnaz

doi:10.1007/s10586-021-03294-4

Cited by 32 publications

(11 citation statements)

References 73 publications

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“…In recent years, swarm intelligence algorithms have been widely used in virtual machine scheduling problems [13] . For example, Kansal et al [14] first applied the Firefly algorithm (FA) to the energy-aware data center VM scheduling problem.…”

Section: Related Workmentioning

confidence: 99%

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%

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

Mao

Chen

Cheng

et al. 2023

Preprint

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“…It limits the systematic faults, increases the validity of data investigation, and offers reliable results. 51 In recent years, we have used this method to review and examine the available techniques in various fields such as data aggregation in the IoT, 3 data collection in vehicular ad hoc network (VANET), 52 load balancing in the IoT, 4 virtual machine consolidation in cloud environments, 53 and service composition in cloud manufacturing 54,55 that have been published in high-quality journals. A complete search of the literature for the related studies is the main step in conducting a systematic study.…”

Section: Article Selection Processmentioning

confidence: 99%

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Pourghebleh

Hekmati

Davoudnia

et al. 2022

Concurrency and Computation

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Nowadays, with the rapid progress of Internet-based and distributed systems such as cloud computing, peer-to-peer networking, and Internet of Things (IoT), significant improvements in almost every engineering and commercial field have been made. On the basis of IoT, smart cities are formed utilizing intelligent information processing, universal connectivity, ubiquitous sensing, and real-time monitoring. Energy conservation is one of the significant issues in current IoT development due to the poor battery endurance of IoT objects. Over the last years, with smart cities' explosive growth, a huge range of studies regarding energy efficiency have been done. The diversity of sparse data and multi-sourcing is utilized in developing IoT scenarios. In order to use efficiently of these data to improve the IoT services, data fusion plays an important role. It saves network resources, improves data transmission efficiency, and extracts useful information from raw data. To the best of our knowledge, there is still a lack of a comprehensive and systematic study about surveying and analyzing the available energy-efficient data fusion techniques in the IoT. Thus, this article aims to address this gap using a systematic method.

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“…Over recent years, many researchers have used GA to solve VM placement/ consolidation problems [46]. GA belongs to the class of evolutionary algorithms [47].…”

Section: Agaff Algorithmmentioning

confidence: 99%

Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

Seyyedsalehi

Khansari

2022

IEEE Access

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Big data and cloud computing are two advanced technologies that have overcome many computing and analytical challenges in recent years. With the rise in the applications of these technologies, the necessity of efficiency and optimization in the utilization of related resources has made sense. The procedure of locating virtual machines (VM) in physical machines (PM) affects the performance, speed, and costs of cloud computing services. VM placement in cloud computing is an NP-hard problem. Indeed, the problem is more complicated in big data tasks due to the need for transferring high volumes of traffic between VMs. This paper proposes a new approach for VM placement in a multi data center (DC) cloud environment. The aware genetic algorithm first fit (AGAFF) is a context-aware algorithm that distinguishes big data tasks with an input tag and uses a structure to minimize the traffic between MapReduce nodes. This multi-objective algorithm is based on the genetic algorithm, which is incorporated with the first fit methodology. The algorithm minimizes energy usage by minimizing the number of used servers, intra-DC traffic of big data tasks, and VMs' live migration while maximizing relevant usage of CPU and RAM in every server. Furthermore, it improves job execution time, especially in big data processing, and reduces service level agreement (SLA) violations. A comparison between the results of AGAFF and four other algorithms shows by about 61% energy consumption reduction on average on different scales and approves a decrease in the number of needed PMs, intra-DC traffic of big data processing, and the number of live migrations.

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The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

Cited by 32 publications

References 73 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

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

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