Bin packing algorithms for virtual machine placement in cloud computing: a review

Kumaraswamy, Subra; Nair, Mydhili K.

doi:10.11591/ijece.v9i1.pp512-524

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…This decision depends on the desired objective function, which may vary according to the optimization problem at hand. A primary objective in data center optimization is to reduce the total number of active servers and minimize energy consumption [29]. In some instances, the focus might be solely on minimizing energy consumption, with the chosen server having the lowest energy usage in the data center [30].…”

Section: Selection Of Destination Server For Vm Migrationmentioning

confidence: 99%

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Kushchazli,

Safargalieva,

Kochetkova

et al. 2024

Mathematics

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The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.

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Section: Selection Of Destination Server For Vm Migrationmentioning

confidence: 99%

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Kushchazli,

Safargalieva,

Kochetkova

et al. 2024

Mathematics

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“…Substantial research has been conducted on the container packing problem, resulting in the development of various algorithms, each offering unique strengths and limitations [14][15][16]. This section reviews some of these algorithms, as shown in Table 1, and their applications to the container packing problem, setting the stage for the current study's objectives and methods.…”

Section: Related Workmentioning

confidence: 99%

“…This section reviews some of these algorithms, as shown in Table 1, and their applications to the container packing problem, setting the stage for the current study's objectives and methods. Next-Fit Decreasing Algorithm Simple and efficient to implement [14][15][16] Does not always produce optimal results…”

Section: Related Workmentioning

confidence: 99%

Optimizing Space Utilization In Container Packing

Kamath

2023

J. Comput. Mech. Manag

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Container packing presents a complex optimization problem that seeks to efficiently pack diverse items into a fixed-size container. This study provides a comparative analysis of four algorithms -- Greedy Shelf, Shelfing with Rotation, Shelfing with Search, and Guillotine Paper Cutting -- investigating their proficiency in solving the container packing problem. Utilizing a set of four packages with differing dimensions and IDs, The study evaluated the performance of each algorithm. The results demonstrated that the Shelfing with Search algorithm outperformed its counterparts by yielding a stack height of 3019610 L units. Conversely, the Guillotine Paper Cutting algorithm performed poorly, with a stack height of 7537295 units. This research also explored the impact of different sorting methods on packing efficiency, revealing that sorting packages in descending order of height yields superior results. Consequently, this study provides an extensive evaluation of the various algorithms used for container packing, while suggesting promising directions for future research to enhance packing efficiency.

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“…The process is inclined towards minimizing number of active bins to reduce energy consumption. Following heuristic based bin-packing algorithms are prevalent in the cloud paradigm [6]:…”

Section: Introductionmentioning

confidence: 99%

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

Gupta

Rani

et al. 2021

Symmetry

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Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy consumption without compromising upon makespan, resource utilization and Quality of Service (QoS) parameters. The novelty of the proposed dynamic approaches in comparison to the existing static approaches is that the proposed approach dynamically creates and dissolves virtual machines as per incoming and completed requests which is a dire need of present computing paradigms via attachment of time-frame with each virtual machine. The simulations have been performed on JAVA platform and dynamic energy utilized-best fit decreasing bin packing technique has produced better results in maximum runs.

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Bin packing algorithms for virtual machine placement in cloud computing: a review

Cited by 10 publications

References 21 publications

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

Optimizing Space Utilization In Container Packing

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

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