Online Algorithm Research Articles

Maximum Efficiency Point Tracking Algorithm for Resonant Full-Bridge Converter in Charging Applications

In this paper, a novel maximum efficiency point tracking algorithm is proposed for a resonant DC-DC power converter in charging applications. Based on a total harmonic analysis (THA), a method for minimizing the harmonic content of the input AC current is investigated. An online algorithm is proposed for achieving the maximum efficiency at the whole charging spectrum, by controlling the angle under which zero-voltage switching occurs. The suggested control algorithm is simple to implement with very low computational complexity. Additionally, an effective winding method is proposed for increasing the leakage inductance without greatly increasing the air gap, thus eliminating the need for additional resonant inductors. A prototype converter of 2kW|20A is implemented for verifying the proposed method and 98% maximum efficiency is achieved.

Efficient Throughput Maximization in Dynamic Rechargeable Networks

In the Dynamic Rechargeable Networks (DRNs), to maximize the throughput by efficient energy allocation, the existing studies usually consider the spatio-temporal dynamic factors of the harvested energy, and seldom the network dynamic factors simultaneously, such as the time variable network resources and wireless interference. To take the network dynamic factors together, this paper studies the quite challenging problem, the network throughput maximization in the DRNs. We introduce the Time-Expanded Graph (TEG) to describe the above dynamic factors in an obvious way and design the Single Pair Throughput maximization (SPT) algorithm based on TEG. In the case of multiple pairs of source-targets, this paper introduces the Garg and Könemann's framework and then designs the Multiple Pairs Throughput (MPT) algorithm to maximize the overall throughput of all pairs. To reduce the time complexity, this paper proposes a Distributed and Parallel Throughput algorithm (DPT). In real applications, the network dynamic factors may not be known in advance. This paper proposes an Online Time-Span Algorithm (OTA) with Markov approximation and Lyapunov optimization, and conducts the extensive numerical evaluation based on the simulated data and the data collected by our real system. The numerical simulation results demonstrate the throughput improvement of our algorithms.

Enhancing multi-cloud service deployment with [formula omitted]: A loss-aware coordinator in sky computing

Internet of Things (IoT) services deployed across different Cloud Service Providers (CSPs) require seamless migration across heterogeneous clouds. Sky Computing (SC) facilitates the requirement by implementing a coordinator with communication APIs among clouds. However, security concerns of CSPs prevent the coordinator from accessing detailed cloud hardware information, thereby hindering the assessment of Serving Ability (SA) and exacerbating delays in service delivery. To ensure delay experience, we designed a loss-aware coordinator SkyCap for the multi-CSP collaboration in SC. First, to assess the SA of clouds with available information, we design a loss-aware SA assessment mechanism AdaSA that collects delay experience and Packet Loss Ratio (PLR) information for SA assessment. Since the uncontrollable PLR can increase service delay, we incorporate PLR as a disturbing factor in SA assessment to ensure a more precise evaluation. Second, to maximize the admission profit of the collaboration, we design two approximation algorithms within SkyCap to serve requests. Since the coordinator may deploy services in batches, we design a delay-aware offline algorithm AdaOff to serve requests simultaneously, while ensuring the computing capacity of clouds and the delay requirement of requests. Meanwhile, for asynchronously arriving requests, we design an ability-aware online algorithm AdaOn to serve requests in real-time, while guaranteeing the computing capacity of clouds, SA of clouds, and delay requirement of requests. Finally, extensive theoretical analysis and experimental validation confirm the performance of the coordinator SkyCap. The SA assessment mechanism AdaSA outperforms existing solutions in SA assessment by ensuring at most 1% deviation, especially in high-PLR scenarios. Meanwhile, the algorithm AdaOff exhibits a remarkable 20% improvement over the state-of-the-art in admission profit while ensuring stringent service delay. Concurrently, the algorithm AdaOn surpasses existing solutions, achieving a nearly 10% enhancement in both admission profit and service delay. In summary, the coordinator SkyCap stands out in theoretical groundwork and practical implementation, offering advancements in the SA assessment, admission profit, and service delay.

Online Convex Optimization with Switching Cost and Delayed Gradients

We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions f T , T < t is available. For L-smooth and µ-strongly convex objective functions, we propose an algorithm (OMGD) with a competitive ratio of at most 4(L+5)+ 16(L+5)/µ for the quadratic switching cost, and also show the bound to be order-wise tight in terms of L, µ. In addition, we show that the competitive ratio of any online algorithm is at least maxΩ(L),Ω( L/√µ ) when the switching cost is quadratic. For the linear switching cost, the competitive ratio of the OMGD algorithm is shown to depend on both the path length and the squared path length of the problem instance, in addition to L, µ, and is shown to be order-wise, the best competitive ratio any online algorithm can achieve.

Optimizing the Journey: Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport

Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies’ results’ plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications.

Online Allocation with Replenishable Budgets: Worst Case and Beyond

This paper studies online resource allocation with replenishable budgets, where budgets can be replenished on top of the initial budget and an agent sequentially chooses online allocation decisions without violating the available budget constraint at each round. We propose a novel online algorithm, called OACP (Opportunistic Allocation with Conservative Pricing), that conservatively adjusts dual variables while opportunistically utilizing available resources. OACP achieves a bounded asymptotic competitive ratio in adversarial settings as the number of decision rounds T gets large. Importantly, the asymptotic competitive ratio of OACP is optimal in the absence of additional assumptions on budget replenishment. To further improve the competitive ratio, we make a mild assumption that there is budget replenishment every T* ≥ 1 decision rounds and propose OACP+ to dynamically adjust the total budget assignment for online allocation. Next, we move beyond the worst-case and propose LA-OACP (Learning-Augmented OACP/OACP+), a novel learning-augmented algorithm for online allocation with replenishable budgets. We prove that LA-OACP can improve the average utility compared to OACP/OACP+ when the ML predictor is properly trained, while still offering worst-case utility guarantees when the ML predictions are arbitrarily wrong. Finally, we run simulation studies of sustainable AI inference powered by renewables, validating our analysis and demonstrating the empirical benefits of LA-OACP.

Integrally Bladed Rotor Modal Identification Under Traveling Wave Excitation With High Density Measurement Points

Abstract Vibration testing of an integrally bladed rotor (IBR) is often completed through traveling wave excitation (TWE) bench tests composed of multiple, simultaneously excited inputs. Often, each blade has many output locations. For IBRs with many blades, as often found in the high pressure compressor, the total number of outputs can be several orders of magnitude. Formulation of a full output spectral density matrix from all measurements will then contain an exponential number of values at each frequency bin that can be detrimental to computational resources during the spectral density matrix formulation as well as down-stream system identification algorithms. An online algorithm is proposed for collecting and analyzing TWE data to reduce the large, computationally burdensome datasets into a manageable number of subsets for subsequent system identification. Furthermore, a frequency domain decomposition technique is also proposed for system identification that also attempts to reduce the data size through singular value decomposition. Identified system poles can be averaged from each subset, but mode shapes require stitching each subset together to identify the full mode shape at all output locations. The developed approaches are tested on synthetic TWE data and compared to baseline system identification results obtained using the full spectral density matrix. Results indicate the data subsets accurately compare to the baseline without much loss in accuracy.

Flow-Time Minimization for Timely Data Stream Processing in UAV-Aided Mobile Edge Computing

Unmanned Aerial Vehicle (UAV) has gained increasing attentions by both academic and industrial communities, due to its flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this paper, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow-time, where the flow-time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrated that the proposed algorithms outperform their comparison counterparts, by reducing the flow-time no less than 19% on average.

On Supervised Online Rolling-Horizon Control for Infinite-Horizon Discounted Markov Decision Processes

This note re-visits the rolling-horizon control approach to the problem of Markov decision process (MDP) with infinite-horizon discounted expected reward criterion. Distinguished from the classical value-iteration approaches, we develop an asynchronous on-line algorithm based on policy iteration integrated with a multi-policy improvement method of policy switching. A sequence of monotonically improving solutions to the forecast-horizon sub-MDP is generated by updating the current solution only at the currently visited state, building in effect a rolling-horizon control policy for the MDP over infinite horizon. Feedbacks from “supervisors,” if available, can be also incorporated while updating. We focus on the convergence issue with a relation to the transition structure of the MDP. Either a global convergence to an optimal forecast-horizon policy or a local convergence to a “locally-optimal” fixed-policy in a finite time is achieved by the algorithm depending on the structure.

An Online Algorithm Based on Replication for Using Spot Instances in IaaS Clouds

An Online Algorithm Based on Replication for Using Spot Instances in IaaS Clouds

Comparison of clinician preference and HIV-ASSIST recommendations in antiretroviral therapy decision-making: A single center experience from Turkiye.

Background: HIV-ASSIST is a free, continuously updated, clinically validated online algorithm tool that synthesizes participant- and virus-specific characteristics and provides ART decision support based on the goals of maximizing viral suppression and tolerability. The aim of this study was to analyze the concordance of clinicians' ART preferences with HIV-ASSIST recommendations and the influencing factors. Methods: We conducted a cross-sectional retrospective cohort study using electronic medical records of people with HIV (PWH) followed in the Infectious Diseases and Clinical Microbiology Department of Health Sciences University Izmir Tepecik Training and Research Hospital. The concordance between prescribed ART and HIV-ASSIST recommendations was evaluated. Results: The study included 101 participants (92 male, 91.1%), median age was 35 (20-67), and 24.8% of participants were treatment-experienced. The concordance between prescribed ART and HIV-ASSIST recommendations was 90.1% (absolute concordance 60.4%). The concordance rate was 89.5% (absolute concordance rate was 64.5%) in treatment-naive participants; 92% (absolute concordance rate was 48%) in treatment-experienced participants. Factors that were associated with discordance in the multivariate analysis were co-trimoxazole prophylaxis and hyperlipidemia. Conclusion: The recommendations of the HIV-ASSIST algorithm were found to be highly concordant with the ART prescriptions of our clinicians.

QoE-aware budgeted edge data caching online: A primal–dual approach

Data caching has garnered significant attention in the field of edge computing due to the low-latency services offered by nearby edge servers. To efficiently utilize the data cache budget and ensure minimal data transmission latency, selecting appropriate edge servers for data caching is crucial. While the users’ Quality of Experience (QoE) plays a vital role in the service providers’ benefits, it has not been adequately considered in dynamic edge computing environments. This is primarily due to the non-linear correlation between QoE and Quality-of-Service (QoS), making cost-effective edge data caching challenging within the service provider’s limited budget. In this paper, we formally define the QoE-aware budgeted online edge data caching (QoE-BOEDC) problem, aiming to maximize the average user QoE while adhering to the service provider’s budget in a dynamic edge computing environment. Here, we formulate a formal optimization model for QoE-BOEDC, which can be proven to be NP-complete. Subsequently, we propose an online algorithm, PDOQ (Primal–Dual Optimization for QoE), based on the primal–dual technique, and theoretically establish its comparative ratio. Additionally, we conduct both small-scale and large-scale experimental tests, and the experimental results demonstrate that PDOQ significantly outperforms other representative algorithms.

Online early work scheduling on parallel machines

We consider non-preemptive online parallel-machine scheduling with a common due date to maximize the total early work of all the jobs, i.e., the total processing time of the jobs (or parts) completed before the common due date. For the general case of m machines, we provide a parameter lower bound with respect to m. For the online algorithm, we first show that the tight competitive ratio of the classical list scheduling (LS) algorithm is 43. We then improve the upper bound on the competitive ratio for the previous algorithm, EFFm, to 1.2956. Additionally, we present a formula to compute the upper bound on the competitive ratio for any given m. For the case of three machines, we improve the lower bound to 1.1878 and propose an improved online algorithm with a tight competitive ratio of 1.2483.

Data-driven zonotopic approximation for n-dimensional probabilistic geofencing

Advanced air mobility is a promising way of metropolitan air transportation. One critical concern that arises is how to ensure operational safety in high-dense, dynamic, and uncertain airspace environments in real time. To address this challenge, we seek a probabilistic geofence that bounds system states with high confidence. To identify the n-dimensional probabilistic geofence for arbitrary unknown uncertainties not limited to Gaussian ones, we present an online algorithm based on a data-driven approach of kernel density estimator. Considering the irregular shape of the probabilistic geofence, we formulate an optimization framework of integer linear programming whose solution determines a zonotope which provides a convex approximation for the probabilistic geofence. Leveraging this formulation, a heuristic algorithm is developed to find its solution efficiently without losing notable accuracy. This heuristic algorithm is tested on case studies that demonstrate it enjoys efficiency, accuracy, near-optimality, and robustness simultaneously.

An Online Fairness-Aware Task Planning Approach for Spatial Crowdsourcing

Spatial crowdsourcing, a human-centric paradigm for performing spatial tasks, has drawn rising attention. Task assignment and worker scheduling are basic issues in spatial crowdsourcing, which has a high demand on the timeliness of tasks and fairness among workers. In this paper, we propose Utility-Fairness Index to evaluate the performance of the crowdsourcing platform and introduce the Fairness-Aware Task Planning problem that maximizes the utility while well-maintaining fairness during task assignment. We prove that its offline version is NP-hard, and clarify that there is no deterministic online algorithm with a constant competitive ratio for it. Two greedy-based online algorithms, Traversal Search and Advanced Nearest Neighbor are designed to solve the problem. We make optimization on running time with recurrence method to reach linear complexity, and use Gaussian Mixture Model to reveal the distribution regularity of tasks in these algorithms. Experiments on both synthetic and real datasets validate the efficiency and effectiveness of our algorithms.

New Scalable and Efficient Online Pairwise Learning Algorithm.

Pairwise learning is an important machine-learning topic with many practical applications. An online algorithm is the first choice for processing streaming data and is preferred for handling large-scale pairwise learning problems. However, existing online pairwise learning algorithms are not scalable and efficient enough for large-scale high-dimensional data, because they were designed based on singly stochastic gradients. To address this challenging problem, in this article, we propose a dynamic doubly stochastic gradient algorithm (D2SG) for online pairwise learning. Especially, only the time and space complexities of O (d) are needed for incorporating a new sample, where d is the dimensionality of data. This means that our D2SG is much faster and more scalable than the existing online pairwise learning algorithms while the statistical accuracy can be guaranteed through our rigorous theoretical analysis under standard assumptions. The experimental results on a variety of real-world datasets not only confirm the theoretical result of our new D2SG algorithm, but also show that D2SG has better efficiency and scalability than the existing online pairwise learning algorithms.

Efficient approximation and privacy preservation algorithms for real time online evolving data streams

Efficient approximation and privacy preservation algorithms for real time online evolving data streams

Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming

In this paper we describe a new conceptual framework that connects approximate Dynamic Programming (DP), Model Predictive Control (MPC), and Reinforcement Learning (RL). This framework centers around two algorithms, which are designed largely independently of each other and operate in synergy through the powerful mechanism of Newton's method. We call them the off-line training and the on-line play algorithms. The names are borrowed from some of the major successes of RL involving games; primary examples are the recent (2017) AlphaZero program (which plays chess, [SHS17], [SSS17]), and the similarly structured and earlier (1990s) TD-Gammon program (which plays backgammon, [Tes94], [Tes95], [TeG96]). In these game contexts, the off-line training algorithm is the method used to teach the program how to evaluate positions and to generate good moves at any given position, while the on-line play algorithm is the method used to play in real time against human or computer opponents. Significantly, the synergy between off-line training and on-line play also underlies MPC (as well as other major classes of sequential decision problems), and indeed the MPC design architecture is very similar to the one of AlphaZero and TD-Gammon. This conceptual insight provides a vehicle for bridging the cultural gap between RL and MPC, and sheds new light on some fundamental issues in MPC. These include the enhancement of stability properties through rollout, the treatment of uncertainty through the use of certainty equivalence, the resilience of MPC in adaptive control settings that involve changing system parameters, and the insights provided by the superlinear performance bounds implied by Newton's method.

Base on -Learning Pareto Optimality for Linear Itô Stochastic Systems With Markovian Jumps

This article investigate the cooperative differential game (CDG) for continuous-time linear Itô stochastic systems with markovian jumps (SSMJ) to obtain the Pareto solutions. Different from most existing works studying nonzero-sum games, this article studies the CDG on the quadratic infinite horizon for the Itô-type SSMJ with unknown system matrix and transition probability. A novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning online algorithm is developed, which consists of that (i) the optimal control problem is equivalent to solving a stochastic algebraic Riccatic equation (ARE); (ii) the joint cost function is approximated by a critic neural network (NN) and Pareto efficient is approximated by two actor NNs. The rigorous stability analysis shows that the system state for SSMJ and the NN weight errors are uniformly ultimately bounded (UUB). Finally, the theory analysis is validated by a numerical example with detailed discussions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In practical applications, many systems are often affected by the change of external environment or the failure of internal components, which leads to the random jump of system parameters. Markovian jump system can effectively describe the above problems. And when the system model is disturbed by the internal parameters, the system control input and external environment, the random errors of state measurement, and other random factors, the deterministic model can no longer accurately describe the controlled system. Therefore, the SSMJ can describe practical problems more accurately. By cooperation, in general, the cost one specific player incurs is not uniquely determined anymore. If all players decide, for example, to use their control variables to reduce the cost of player 1 as much as possible, a different minimum is attained for player 1 compared with that in the case where all players agree collectively to help a different player in minimizing his cost. So, depending on how the players choose to ‘divide’ their control efforts, a player incurs different ‘minima’. Therefore, we will design an online learning algorithm to obtain pareto solutions with different weights. On the other hand, in practice, it is difficult to obtain an accurate system model. In order to solve this problem, a novel scheme is designed by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning technology, which does not need system matrix.

Efficient Framework for Real-Time Color Cast Correction and Dehazing Using Online Algorithms to Approximate Image Statistics

Efficient Framework for Real-Time Color Cast Correction and Dehazing Using Online Algorithms to Approximate Image Statistics