scholarly journals Optimal Analysis of Best Fit Bin Packing

2014 ◽  
pp. 429-441 ◽  
Author(s):  
György Dósa ◽  
Jiří Sgall
Keyword(s):  
Bin Packing ◽  
Optimal Analysis ◽  
Best Fit

COMPUTING CALL ADMISSION CAPACITIES IN LINEAR NETWORKS

1999 ◽  
Vol 13 (4) ◽  
pp. 387-406 ◽  
Author(s):  
E. G. Coffman ◽  
Anja Feldmann ◽  
Nabil Kahale ◽  
Bjorn Poonen
Keyword(s):  
Bin Packing ◽  
Arrival Time ◽  
Necessary Condition ◽  
Time Duration ◽  
Call Admission ◽  
Admission Rates ◽  
Linear Networks ◽  
Bandwidth Requirement ◽  
Bandwidth Capacity ◽  
Best Fit

We study call admission rates in a linear communication network with each call identified by an arrival time, duration, bandwidth requirement, and origin-destination pair. Network links all have the same bandwidth capacity, and a call can be admitted only if there is sufficient bandwidth available on every link along the call's path. Calls not admitted are held in a queue, in contrast to the protocol of loss networks. We determine maximum admission rates (capacities) under greedy call allocation rules such as First Fit and Best Fit for several baseline models and prove that the natural necessary condition for stability is sufficient. We establish the close connections between our new problems and the classic problems of bin packing and interval packing. In view of these connections, it is surprising to find that Best Fit allocation policies are inferior to First Fit policies in the models studied.

Which Algorithm for Scheduling Add-on Elective Cases Maximizes Operating Room Utilization? 

1999 ◽  
Vol 91 (5) ◽  
pp. 1491-1491 ◽  
Author(s):  
Franklin Dexter ◽  
Alex Macario ◽  
Rodney D. Traub
Keyword(s):  
Information System ◽  
Operating Room ◽  
Bin Packing ◽  
Space Variable ◽  
Fuzzy Constraints ◽  
Surgical Services ◽  
Open Time ◽  
Bounded Space ◽  
Utilization Time ◽  
Best Fit

Background The algorithm to schedule add-on elective cases that maximizes operating room (OR) suite utilization is unknown. The goal of this study was to use computer simulation to evaluate 10 scheduling algorithms described in the management sciences literature to determine their relative performance at scheduling as many hours of add-on elective cases as possible into open OR time. Methods From a surgical services information system for two separate surgical suites, the authors collected these data: (1) hours of open OR time available for add-on cases in each OR each day and (2) duration of each add-on case. These empirical data were used in computer simulations of case scheduling to compare algorithms appropriate for "variable-sized bin packing with bounded space." "Variable size" refers to differing amounts of open time in each "bin," or OR. The end point of the simulations was OR utilization (time an OR was used divided by the time the OR was available). Results Each day there were 0.24 +/- 0.11 and 0.28 +/- 0.23 simulated cases (mean +/- SD) scheduled to each OR in each of the two surgical suites. The algorithm that maximized OR utilization, Best Fit Descending with fuzzy constraints, achieved OR utilizations 4% larger than the algorithm with poorest performance. Conclusions We identified the algorithm for scheduling add-on elective cases that maximizes OR utilization for surgical suites that usually have zero or one add-on elective case in each OR. The ease of implementation of the algorithm, either manually or in an OR information system, needs to be studied.

BIN-PACKING ALGORITHMS FOR PERIODIC TASK SCHEDULING

2011 ◽  
Vol 25 (07) ◽  
pp. 1147-1160 ◽  
Author(s):  
ZHILIN ZHU ◽  
JINXUE SUI ◽  
LI YANG
Keyword(s):  
Control System ◽  
Real Time ◽  
Task Scheduling ◽  
Bin Packing ◽  
Performance Ratio ◽  
Electronic Control ◽  
Worst Case ◽  
Periodic Task ◽  
Electronic Control System ◽  
Best Fit

CAN (Controller Area Network) operation observes event triggering standards, it is widely applied in real-time distributional control system of the automobile electronic control system, satellite control system, medical equipment's electronic control system as well as the textile equipment and so on. As the extension of offline bin-packing problem, periodic task scheduling has many important applications in real-time distributed systems. For time triggered CAN control systems, two strategies to determine the basic cycle (BC) of TTCAN are presented. Next-fit algorithm, next-fit descending algorithm, best-fit algorithm and best-fit descending algorithm are proposed to construct periodic task scheduling tables. Time complexity and worst-case asymptotic performance ratio of these algorithms are analyzed, and the experimental results indicate that NFA algorithm is not worse than NFDA algorithm, BFA is also not worse than BFDA algorithm, four algorithms have the advantage over the typical one-dimensional bin-packing algorithm.

scholarly journals Biased Random Walks, Lyapunov Functions, and Stochastic Analysis of Best Fit Bin Packing

1998 ◽  
Vol 27 (2) ◽  
pp. 218-235 ◽  
Author(s):  
Claire Kenyon ◽  
Yuval Rabani ◽  
Alistair Sinclair
Keyword(s):  
Random Walks ◽  
Stochastic Analysis ◽  
Lyapunov Functions ◽  
Bin Packing ◽  
Best Fit ◽  
Biased Random Walks

Distributed cloud resources allocation for fair utilization using multi-objective bin packing algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Senthil Kumar Angappan ◽  
Tezera Robe ◽  
Sisay Muleta ◽  
Bekele Worku M
Keyword(s):  
Cloud Storage ◽  
Bin Packing ◽  
Virtual Machines ◽  
Utilization Efficiency ◽  
Processing Unit ◽  
Content Type ◽  
Multi Objective ◽  
Physical Machine ◽  
Packing Algorithm ◽  
Best Fit

PurposeCloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources.Design/methodology/approachThis thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins.FindingsThe proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms.Originality/valueThe proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.

scholarly journals Online Bandwidth packing with symmetric distribution

2007 ◽  
Vol DMTCS Proceedings vol. AH,... (Proceedings) ◽  
Author(s):  
Marc Lelarge
Keyword(s):  
Bin Packing ◽  
Queueing System ◽  
Packing Problem ◽  
Symmetric Distribution ◽  
Heavy Load ◽  
Average Case ◽  
Matching Problems ◽  
Model Average ◽  
Monotonicity Properties ◽  
Best Fit

International audience We consider the following stochastic bin packing process: the items arrive continuously over time to a server and are packed into bins of unit size according to an online algorithm. The unpacked items form a queue. The items have random sizes with symmetric distribution. Our first contribution identifies some monotonicity properties of the queueing system that allow to derive bounds on the queue size for First Fit and Best Fit algorithms. As a direct application, we show how to compute the stability region under very general conditions on the input process. Our second contribution is a study of the queueing system under heavy load. We show how the monotonicity properties allow one to derive bounds for the speed at which the stationary queue length tends to infinity when the load approaches one. In the case of Best Fit, these bounds are tight. Our analysis shows connections between our dynamic model, average-case results on the classical bin packing problem and planar matching problems.

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