Filling Two Needs With One Deed: Combo Pricing Plans for Computing-Intensive Multimedia Applications

Zang, Shizhe; Bao, Wei; Yeoh, Phee Lep; Vucetic, Branka; Li, Yonghui

doi:10.1109/jsac.2019.2916451

Cited by 13 publications

(8 citation statements)

References 37 publications

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“…The longer the plan reserved, the greater the discount. (Zang et al 2019) studied a variant of ski-rental problem offering three purchase options including two single purchases and one combo purchase. (Zhang and Conitzer 2020) considers the ski-rental problem with multiple desired resources.…”

Section: Related Workmentioning

confidence: 99%

Competitive Analysis for Two-Level Ski-Rental Problem

Bao

Yuan

2021

AAAI

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In this paper, we study a two-level ski-rental problem. There are multiple commodities, each one can be “rented” (paying for on-demand usage) or “purchased” (paying for life-time usage). There is also a combo purchase available so that all commodities can be purchased as a combo. Since the usages of the commodities in future are not known in advance, to minimize the overall cost, we design an online algorithm to decide if we rent a commodity, purchase a commodity, or make a combo purchase. We first propose a deterministic online algorithm. It can achieve 3 competitive ratio, which is optimal and tight. Next, we further propose a randomized online algorithm, leading to a e^σ/(e^σ-1) competitive ratio, where σ is the ratio between the price of a single commodity and the price of combo purchase. Finally, we apply simulation to verify the theoretical competitive ratios and evaluate the actual performance against benchmarks.

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Section: Related Workmentioning

confidence: 99%

Competitive Analysis for Two-Level Ski-Rental Problem

Bao

Yuan

2021

AAAI

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“…(Dinh et al 2020) studied how edge nodes rent or buy remote resources to meet the capacity requirement. (Zang et al 2019) developed an online ski-rental algorithm considering both computation instance reservation and communication channel reservation. A range of caching problems can also be formulated as ski-rental problems.…”

Section: Ski-rental and Applicationsmentioning

confidence: 99%

“…A sequence from 0 to arbitrary T max can be divided in to sub-sequences in [iT, (i+1)T ) and each sub-sequence follows the same analysis. Please note that such setting follows a range of ski-rental problems, such as (Wang, Liang, and Li 2015;Zang et al 2019). Therefore, we focus on typical sequences, which is defined as follows.…”

Section: Analysis For Non-extension Plansmentioning

confidence: 99%

“…Given the available directional plans and the all-in-one plan, the objective is how to reserve them to minimize the overall cost. However, the user's future data usage is not known in advance (Rosenfelder 2018;Zang et al 2019;Unterbrunner et al 2009), so that we focus on the online solution with competitive analysis (Komm 2016). Nevertheless, the challenge is three-fold: (1) There are M directional plans and an all-in-one plan to choose, which create a large decision space.…”

Section: Introductionmentioning

confidence: 99%

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Online Hedge Reservation for Diverse Plans and Competitive Analysis

Bao

Yuan

et al. 2021

ICAPS

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In this paper, we investigate the plan reservation problem with diverse plans in mobile networks. The pricing scheme includes: 1) Pay-as-you-go (PAYG) payment; 2) All-in-one plan: an upfront fee is charged to cover data volume of a period of time; and 3) Directional plan: an upfront fee is charged to cover data volume of a specific app for a period of time. We investigate online plan reservation with competitive analysis, as the data volume is not known until an app is used. The problem is challenging as there are multiple directional plans and one all-in-one plan, creating a large decision space and complicated correlations among the decisions. We propose the Online Hedge Reservation (OHR) Algorithm to solve the problem and prove that it achieves e^beta/(e^beta-1) competitive ratio when each plan is valid till the end of each calendar month and 2e^beta/(e^beta-1) competitive ratio when each plan is valid for a full month, where beta is the ratio of prices of the directional plans and the all-in-one plan. This is an exciting neat extension of the competitive ratio e/(e-1) of the classic ski-rental problem. Finally, trace-driven simulation is conducted to further verify the advantages of the OHR Algorithm.

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“…However, this approach provides probabilistic rather than deterministic guarantees for individual vehicles accessing network bandwidth. Vehicle-side and individual bandwidth reservation has proven to be a more efficient solution, where vehicles reserve the necessary resources in advance and make reservations based on their specific requirements and resource costs rather than relying on the MNO to allocate resources on their behalf [9], [10], [11], [12], [13]. Another focus of this approach is the economical aspect, which is being minimized by the expenditure for guaranteed access to the network upon reservation.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Bandwidth Reservation Decision Time in Vehicular Networks using Batched LSTM

Al-khatib,

Moessner,

Timinger

2024

IJACSA

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Time-sensitive and safety-critical networked vehicular applications, such as autonomous driving, require deterministic guaranteed resources. This is achieved through advanced individual bandwidth reservations. The efficient timing of a vehicle decision to place a cost-efficient reservation request is crucial, as vehicles typically lack sufficient information about future bandwidth resource availability and costs. Predicting bandwidth costs often using time-series machine learning models like Long Short-Term Memory (LSTM). However, standard LSTM models typically require longer durations of multiple input data sets to achieve high accuracy. In certain scenarios, quick decisions must be made, even if the vehicle means sacrificing some accuracy. We propose a batched LSTM model to assist vehicles in placing bandwidth reservation requests within a limited data for an upcoming driving path. The model divides data during training to enhance computational efficiency and model performance. We validated our model using historical Amazon price data, providing a real-world scenario for experiment. The results demonstrate that the batched LSTM model not only achieves higher accuracy within a short input data duration but also significantly reduces bandwidth costs by up to 27% compared to traditional time-series machine learning models.

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Filling Two Needs With One Deed: Combo Pricing Plans for Computing-Intensive Multimedia Applications

Cited by 13 publications

References 37 publications

Competitive Analysis for Two-Level Ski-Rental Problem

Competitive Analysis for Two-Level Ski-Rental Problem

Online Hedge Reservation for Diverse Plans and Competitive Analysis

Optimizing Bandwidth Reservation Decision Time in Vehicular Networks using Batched LSTM

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