Dynamic vector bin packing for virtual machine placement in cloud

Virtual machine placement is a crucial challenge in cloud computing in utilizing physical machine resources in data centers. Virtual Machine placement is formulated as the MinUsageTime Dynamic Vector Bin Packing Problem (DVBP), aiming to minimize the total usage time of the physical machines. This r...

Full description

Saved in:
Bibliographic Details
Main Author: Lee, Zong Yu
Other Authors: Tang Xueyan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/174990
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-174990
record_format dspace
spelling sg-ntu-dr.10356-1749902024-04-19T15:45:10Z Dynamic vector bin packing for virtual machine placement in cloud Lee, Zong Yu Tang Xueyan School of Computer Science and Engineering ASXYTang@ntu.edu.sg Computer and Information Science Virtual machine placement MinUsageTime DVBP Dynamic vector bin packing problem Cloud computing Optimization Competitive ratio Online clairvoyant Offline clairvoyant Non clairvoyant Virtual machine placement is a crucial challenge in cloud computing in utilizing physical machine resources in data centers. Virtual Machine placement is formulated as the MinUsageTime Dynamic Vector Bin Packing Problem (DVBP), aiming to minimize the total usage time of the physical machines. This report provides inclusive proof that DVBP is an NP-hard problem. This report also evaluates state-of-the-art algorithms in non-clairvoyant and clairvoyant scenarios, where future information about the items is known. For non-clairvoyant scenarios, algorithms such as First Fit and Next Fit are implemented, and new algorithms such as Resource Max and Round-Robin Next Fit are proposed for replacement. Similarly, clairvoyant scenarios' algorithms like Classify By Departure Time and Hybrid Algorithm are evaluated, and a new algorithm such as Closest Remaining Time is proposed as an alternative. Overall, 22 algorithms are evaluated (9 of which are original to this project). This report provides the pseudocode of the algorithm during the implementation. It optimizes the implementation of the best-fit and worst-fit algorithms by proving the theorem to prune the search space effectively. The evaluation includes theoretical competitiveness analysis and empirical analysis of a simulation of real-life datasets by Microsoft Azure. Theoretically, this report proves that the competitive ratio of any algorithm is bounded by the number of items. Empirically, the insights from the total usage time of the physical machines by various algorithms are discussed through the simulation results. Bachelor's degree 2024-04-19T02:17:33Z 2024-04-19T02:17:33Z 2024 Final Year Project (FYP) Lee, Z. Y. (2024). Dynamic vector bin packing for virtual machine placement in cloud. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/174990 https://hdl.handle.net/10356/174990 en SCSE23-0324 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Virtual machine placement
MinUsageTime DVBP
Dynamic vector bin packing problem
Cloud computing
Optimization
Competitive ratio
Online clairvoyant
Offline clairvoyant
Non clairvoyant
spellingShingle Computer and Information Science
Virtual machine placement
MinUsageTime DVBP
Dynamic vector bin packing problem
Cloud computing
Optimization
Competitive ratio
Online clairvoyant
Offline clairvoyant
Non clairvoyant
Lee, Zong Yu
Dynamic vector bin packing for virtual machine placement in cloud
description Virtual machine placement is a crucial challenge in cloud computing in utilizing physical machine resources in data centers. Virtual Machine placement is formulated as the MinUsageTime Dynamic Vector Bin Packing Problem (DVBP), aiming to minimize the total usage time of the physical machines. This report provides inclusive proof that DVBP is an NP-hard problem. This report also evaluates state-of-the-art algorithms in non-clairvoyant and clairvoyant scenarios, where future information about the items is known. For non-clairvoyant scenarios, algorithms such as First Fit and Next Fit are implemented, and new algorithms such as Resource Max and Round-Robin Next Fit are proposed for replacement. Similarly, clairvoyant scenarios' algorithms like Classify By Departure Time and Hybrid Algorithm are evaluated, and a new algorithm such as Closest Remaining Time is proposed as an alternative. Overall, 22 algorithms are evaluated (9 of which are original to this project). This report provides the pseudocode of the algorithm during the implementation. It optimizes the implementation of the best-fit and worst-fit algorithms by proving the theorem to prune the search space effectively. The evaluation includes theoretical competitiveness analysis and empirical analysis of a simulation of real-life datasets by Microsoft Azure. Theoretically, this report proves that the competitive ratio of any algorithm is bounded by the number of items. Empirically, the insights from the total usage time of the physical machines by various algorithms are discussed through the simulation results.
author2 Tang Xueyan
author_facet Tang Xueyan
Lee, Zong Yu
format Final Year Project
author Lee, Zong Yu
author_sort Lee, Zong Yu
title Dynamic vector bin packing for virtual machine placement in cloud
title_short Dynamic vector bin packing for virtual machine placement in cloud
title_full Dynamic vector bin packing for virtual machine placement in cloud
title_fullStr Dynamic vector bin packing for virtual machine placement in cloud
title_full_unstemmed Dynamic vector bin packing for virtual machine placement in cloud
title_sort dynamic vector bin packing for virtual machine placement in cloud
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/174990
_version_ 1806059740462055424