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On dynamic bin packing for resource allocation in the cloud

Published: 21 June 2014 Publication History

Abstract

Dynamic Bin Packing (DBP) is a variant of classical bin packing, which assumes that items may arrive and depart at arbitrary times. Existing works on DBP generally aim to minimize the maximum number of bins ever used in the packing. In this paper, we consider a new version of the DBP problem, namely, the MinTotal DBP problem which targets at minimizing the total cost of the bins used over time. It is motivated by the request dispatching problem arising in cloud gaming systems. We analyze the competitive ratios of the commonly used First Fit, Best Fit, and Any Fit packing (the family of packing algorithms that open a new bin only when no currently opened bin can accommodate the item to be packed) algorithms for the MinTotal DBP problem. We show that the competitive ratio of Any Fit packing cannot be better than the max/min item interval length ratio μ. The competitive ratio of Best Fit packing is not bounded for any given μ. For First Fit packing, if all the item sizes are smaller than W⁄k (W is the bin capacity and k≥1 is a constant), it has a competitive ratio of k⁄k-1μ + 6k⁄k-1 + 1. For the general case, First Fit packing has a competitive ratio of 2μ + 13. We also propose a Modified First Fit packing algorithm that can achieve a competitive ratio of 8⁄7μ + 55⁄7 when μ is not known and can achieve a competitive ratio of μ + 8 when μ is known.

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cover image ACM Conferences
SPAA '14: Proceedings of the 26th ACM symposium on Parallelism in algorithms and architectures
June 2014
356 pages
ISBN:9781450328210
DOI:10.1145/2612669
  • General Chair:
  • Guy Blelloch,
  • Program Chair:
  • Peter Sanders
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 June 2014

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Author Tags

  1. approximation algorithms
  2. cloud gaming
  3. dynamic bin packing
  4. request dispatching
  5. worst case bounds

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SPAA '14 Paper Acceptance Rate 30 of 122 submissions, 25%;
Overall Acceptance Rate 447 of 1,461 submissions, 31%

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  • (2025)Renting servers in the cloud: The case of equal duration jobsDiscrete Applied Mathematics10.1016/j.dam.2024.11.015362(82-99)Online publication date: Feb-2025
  • (2024)Tight Bounds for Dynamic Bin Packing with PredictionsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004378:3(1-28)Online publication date: 10-Dec-2024
  • (2024)The Power of Migrations in Dynamic Bin PackingProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004358:3(1-28)Online publication date: 10-Dec-2024
  • (2024)Renting Servers in the Cloud: Parameterized Analysis of FirstFitProceedings of the 25th International Conference on Distributed Computing and Networking10.1145/3631461.3631557(199-208)Online publication date: 4-Jan-2024
  • (2024)Brief Announcement: Tight bounds for Dynamic Bin Packing with PredictionsProceedings of the 36th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3626183.3660271(297-299)Online publication date: 17-Jun-2024
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  • (2024)Deep reinforcement learning based resource allocation in edge-cloud gamingMultimedia Tools and Applications10.1007/s11042-024-18337-283:26(67903-67926)Online publication date: 29-Jan-2024
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