Algorithm NextFit for the Bin Packing Problem

Fujiwara, Hiroshi; Adachi, Ryota; Yamamoto, Hiroaki

doi:10.2478/forma-2021-0014

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…We use the nextfit heuristic [24] to solve the workload estimation problem as follows: we first choose a random VFN and assign tasks to it until all the CPU and GPU resources have been allocated. We repeat this process of choosing random VFNs and assigning tasks until we have assigned all tasks or all VFNs are fully booked.…”

Section: Workload Estimationmentioning

confidence: 99%

“…The heuristic method could offer two benefits: low complexity and bounded solutions. First, since the complexity of the nextfit heuristic is O(n), the method can solve the problem in polynomial time [24]. Second, while the solution based on the heuristic suffers from the sub-optimal issue, the approximated solution could act as an upper bounding for the workload.…”

Section: Workload Estimationmentioning

confidence: 99%

“…Then, we evaluate the scalability of the capacity planning model. The nextfit heuristic can solve the workload estimation problem in polynomial time [24]. To measure the scalability of the global and regional planning models, we use random inputs with various sizes of data and compare the execution time.…”

Section: A Time Complexitymentioning

confidence: 99%

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On-Demand Vehicular Fog Computing for Beyond 5G Networks

Mao

Akgül

Cho

et al. 2023

IEEE Trans. Veh. Technol.

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Emerging compute-intensive and latency-sensitive vehicular applications are expected to be deployed at the edge instead of the cloud to shorten the network latency. Mobile fog nodes carried by moving vehicles, namely vehicular fog nodes (VFNs), have been proposed to complement the stationary fog nodes co-located with base stations to handle the spatiotemporal variations of demand in a cost-efficient way. Existing works on capacity planning for such vehicular fog computing (VFC) scenarios are built on the assumption of certain spatiotemporal patterns of vehicular traffic. They consider long-term capacity planning (e.g., updated every season) but leave the adaptation to temporary changes or unexpected variations out of scope. These solutions typically result in high computational costs and thus are not suitable for short-term capacity planning, which requires low-latency responses. To reduce time complexity, we propose an integer linear programming (ILP)-based framework called on-demand capacity planning (ODCP) to implement two-phase planning through optimizing the routing strategies of VFNs, with the aim of maximizing the profit and quality of service (QoS). More specifically, ODCP first predicts the traffic flow and resource demand using seasonal autoregressive integrated moving average (SARIMA) and estimates the revenue using an economic model defined by service level agreement (SLA). With the estimated workload and revenue, the first phase (i.e., global planning) decides the ratio of tasks that can be served at the city scale and assigns VFNs to each region. The second phase (i.e., regional planning) assigns the VFNs to users within the same region and schedules the routes of VFNs based on the mobility of users. Experimental results show that the proposed solution achieves a higher performance in terms of profit and QoS than the existing single-phase capacity planning solutions. We also find that a large number of VFNs, a small region size, high penalty costs, and low travel and rental costs lead to high service rates, whereas a large region size and low travel, rental, and penalty costs lead to high profits.Index Terms-Capacity planning, vehicular fog computing (VFC), quality of service (QoS), integer linear programming (ILP), and techno-economic analysis.

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Section: Workload Estimationmentioning

confidence: 99%

Section: Workload Estimationmentioning

confidence: 99%

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On-Demand Vehicular Fog Computing for Beyond 5G Networks

Mao

Akgül

Cho

et al. 2023

IEEE Trans. Veh. Technol.

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“…However, as far as we know, there is no work in the literature that deals with this problem from the QC perspective. On the contrary, there are few publications mentioning the BPP and quantum algorithms, but most of them discuss it from quantum-inspired classical algorithms perspective [29,42,68]. Furthermore, slightly related with the 1dBPP, there are quantum algorithms solving the subset-sum problem [5,13,17].…”

Section: Bpp State Of the Artmentioning

confidence: 99%

Hybrid quantum-classical heuristic for the bin packing problem

Andoin

Osaba

Oregi

et al. 2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Optimization problems is one of the most challenging applications of quantum computers, as well as one of the most relevants. As a consequence, it has attracted huge efforts to obtain a speedup over classical algorithms using quantum resources. Up to now, many problems of different nature have been addressed through the perspective of this revolutionary computation paradigm, but there are still many open questions. In this work, a hybrid classical-quantum approach is presented for dealing with the one-dimensional Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one designed for being executed in different computational ecosystems. First, a quantum subroutine seeks a set of feasible bin configurations of the problem at hand. Secondly, a classical computation subroutine builds complete solutions to the problem from the subsets given by the quantum subroutine. Being a hybrid solver, we have called our method H-BPP. To test our algorithm, we have built 18 different 1dBPP instances as a benchmarking set, in which we analyse the fitness, the number of solutions and the performance of the QC subroutine. Based on these figures of merit we verify that H-BPP is a valid technique to address the 1dBPP.

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Algorithm NextFit for the Bin Packing Problem

Cited by 2 publications

References 5 publications

On-Demand Vehicular Fog Computing for Beyond 5G Networks

On-Demand Vehicular Fog Computing for Beyond 5G Networks

Hybrid quantum-classical heuristic for the bin packing problem

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