Real-time Resource Allocation Research Articles

Optimal Multi-stage Configuration Allocation with Applications to Video Advertising

In modern applications of real-time resource allocation, for example allocating ad opportunities created by users to relevant advertisers during video streaming, a prevalent scenario is when an arriving user can be served under different configurations''. For example, in video advertising, a configuration can be an arrangement of video-ads of various lengths and formats for a subset of advertisers, showing up at different time-stamps of the video. Each selected configuration assigns a certain number of impressions to each advertiser. Depending on their long-term contracts, each advertiser is willing to pay for a certain number of impressions during the decision making horizon at a per-impression price depending on the assigned configuration. While this application is real-time, there is an inherent allowance for latency in showing ads to users, as several of these ads show up in the middle (or at the end) of the video. This provides an opportunity for ad allocator to batch the users and to make more profitable allocation decisions. While designing algorithms for the fully online variant of this setting has been studied in the literature [Devanur et al., 2016], the multi-stage variant where the allocation decisions are made batch-by-batch is not studied. Motivated by the above application, we study designing optimal competitive multi-stage configuration allocation algorithms (with preemption) in an adversarial setting, where users arrive in batches in a stage-wise fashion. The allocator then decides on an irrevocable configuration for each of the users in an arriving batch at each stage so as to maximize the total revenue. We propose a novel convex-programming based multi-stage configuration allocation algorithm that is 1-(1-1/K)^K competitive, where K is the number of stages. We further show an intimate connection between the convex programs used in each stage and a recursive functional equation (with a sequence of polynomials of decreasing degrees as its solution). Using this connection, we develop a recursive primal-dual analysis for our algorithm and extend the previous analysis framework of multi-stage fractional matching [Feng and Niazadeh, 2021] to this more general setting. Interestingly, we show there is an intimate connection between convex Lagrangian dual of each stage of our algorithm and how the primal-dual analysis needs to be set up, which might be of independent interest.

Deep Learning–Based Energy Beamforming With Transmit Power Control in Wireless Powered Communication Networks

In this paper, we propose deep learning–based energy beamforming in a multi-antennae wireless powered communication network (WPCN). We consider a WPCN where a hybrid access point (HAP) equipped with multiple antennae broadcasts an energy-bearing signal to wireless devices using energy beamforming. We investigate the joint optimization of the time allocation for wireless energy transfer (WET) and wireless information transfer (WIT) with the design for energy beams while minimizing the transmit power at the HAP for efficient use of its available resources. However, this is a non-convex problem, and it is numerically intractable to solve it. In the literature, the traditional approach to solving this problem is based on an iterative algorithm that incurs high computational and time complexity, which is not feasible for real-time applications. We study and analyze a deep neural network (DNN)-based scheme and propose a faster and more efficient approach for the fair approximation of a near-optimal solution to this problem. To train the proposed DNN, we acquire training data samples from a sequential parametric convex approximation (SPCA)-based iterative algorithm. Instead of acquiring data samples and training the DNN, which is highly complex, we use offline training for the DNN to provide a faster solution to the real-time resource allocation optimization problem. Through the simulation results, we show the proposed DNN scheme provides a fair approximation of the traditional SPCA method with low computational and time complexity.

Real-Time Energy Harvesting Aided Scheduling in UAV-Assisted D2D Networks Relying on Deep Reinforcement Learning

Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported by UAVs, both the user experience and network performance can be substantially enhanced at public events. However, the continuous moving of D2D users, limited energy and flying time of UAVs are impediments to their applications in real-time. To tackle this issue, we propose a novel model based on deep reinforcement learning in order to find the optimal solution for the energy-harvesting time scheduling in UAV-assisted D2D communications. To make the system model more realistic, we assume that the UAV flies around a central point, the D2D users move continuously with random walk model and the channel state information encountered during each time slot is randomly time-variant. Our numerical results demonstrate that the proposed schemes outperform the existing solutions. The associated energy efficiency game can be solved in less than one millisecond by an off-the-shelf processor using trained neural networks. Hence our deep reinforcement learning techniques are capable of solving real-time resource allocation problems in UAV-assisted wireless networks.

SAFER model: Strategic allocation of fundamental epidemic resources.

To describe the Strategic Allocation of Fundamental Epidemic Resources (SAFER) model as a method to inform equitable community distribution of critical resources and testing infrastructure. The SAFER model incorporates a four-quadrant design to categorize a given community based on two scales: testing rate and positivity rate. Three models for stratifying testing rates and positivity rates were applied to census tracts in Milwaukee County, Wisconsin: using median values (MVs), cluster-based classification and goal-oriented values (GVs). Each of the three approaches had its strengths. MV stratification divided the categories most evenly across geography, aiding in assessing resource distribution in a fixed resource and testing capacity environment. The cluster-based stratification resulted in a less broad distribution but likely provides a truer distribution of communities. The GVs grouping displayed the least variation across communities, yet best highlighted our areas of need. The SAFER model allowed the distribution of census tracts into categories to aid in informing resource and testing allocation. The MV stratification was found to be of most utility in our community for near real time resource allocation based on even distribution of census tracts. The GVs approach was found to better demonstrate areas of need.

Profit-Based Algorithm of Joint Real-Time Task Scheduling and Resource Allocation in C-RANs

In cloud radio access networks (C-RANs), a local controller controls a set of lightweight remote radio heads (RRHs) for connecting users nearby, and creates virtual machines (VMs) for executing user tasks. In view of previous works, the strategies of minimizing system resource consumption or transmission delay are adopted in the issue of joint task scheduling and resource allocation. Different from them, this work puts forward the strategy of maximizing the profit of the joint issue. The problem is formulated as integer linear programming (ILP), and then an algorithm with bounded approximation ratio for solving the ILP is proposed. The simulation results show that the solution of the algorithm is very close to the optimal one of the ILP. In addition, another algorithm is proposed to control the tradeoff between performance and robustness of the solution under the assumption that the system resource can be expanded by a bounded factor.

Real-Time Resource Allocation for Wireless Powered Multiuser Mobile Edge Computing With Energy and Task Causality

This article considers a wireless powered multiuser mobile edge computing (MEC) system, in which a multi-antenna hybrid access point (AP) wirelessly charges multiple users, and each user relies on the harvested energy to execute computation tasks. We jointly optimize the energy beamforming and remote task execution at the AP, as well as the local computing and task offloading, aiming to minimize the total system energy consumption over a finite time horizon, subject to causality constraints for both energy harvesting and task arrival at the users. In particular, we consider a practical scenario with casual task state information (TSI) and channel state information (CSI), i.e., only the current and previous TSI and CSI are available, but the future TSI and CSI can only be predicted subject to certain errors. To solve this real-time resource allocation problem, we propose an offline-optimization inspired online design approach. First, we consider the offline optimization case by assuming that the TSI and CSI are perfectly known a-priori . In this case, the energy minimization problem corresponds to a convex problem, for which the semi-closed-form optimal solution is obtained via the Lagrange duality method. Next, inspired by the optimal offline solution, we propose a sliding-window based online resource allocation design in practical cases by integrating with the sequential optimization. Finally, numerical results show that the proposed joint wireless powered MEC designs significantly improve the system’s energy efficiency, as compared with the benchmark schemes that consider a sliding window of size one or without such joint optimization.

An adaptive dwell time scheduling model for phased array radar based on three-way decision

Real-time resource allocation is crucial for phased array radars to undertake multi-task with limited resources such as in the situation of multi-target tracking, in which targets need to be prioritized so that resources can be allocated accordingly and effectively. In this paper, a three-way decision-based model is proposed for adaptive scheduling of phased radar dwell time. Using the model, the threat posed by a target is measured by an evaluation function, and therefore, a target is assigned to one of the three possible decision regions, i.e., positive region, negative region, and boundary region. A different region has a various priority in terms of resource demand, and as such, a different radar resource allocation decision is applied to each region to satisfy different tracking accuracy of multi-target. In addition, the dwell time scheduling model can be further optimized by implementing a strategy for determining a proper threshold of three-way decision making to optimize the thresholds adaptively in real-time. The advantages and the performance of the proposed model has been verified by experimental simulations with comparison to the traditional two-way decision model and the three-way decision model without threshold optimization. The experiential results have demonstrated that the performance of the proposed model has a certain advantage in detecting high threat targets. 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Security-Aware Dynamic Scheduling for Real-Time Optimization in Cloud-Based Industrial Applications

Nowadays, large number of cloud-based techniques have been used in industrial control systems (ICS), which also brings many security threats. The emergence of security-aware industrial control has paved the way of security-aware scheduling in cloud-based industrial applications. Actually, most cloud-based industrial applications are time sensitive, which need real-time processing. Edge cloud computing paradigm extends the computing ability of traditional cloud model with low-latency local resources. Thus, heterogeneous clouds that consist of both centralized resources and edge resources may be a promising resource model to provide both scalable and low-latency resources for cloud-based industrial applications. In view of these challenges, in this article, we propose a security-aware dynamic scheduling method for real-time resource allocation in ICS. First, a three-level security model is designed for both tasks and cloud resources in ICS, and a two-tier heterogeneous cloud architecture is introduced. Accordingly, a security-aware scheduling method based on distributed particle swarm optimization is presented for resource allocation with security concerns. To deal with the dynamics of edge resources and the mobility of mobile industrial applications, a dynamic scheduling mechanism based on dynamic workflow model is proposed for real-time optimization. Experimental results validate that the scheduling control policy proposed in this article can achieve a good balance between scheduling performance and security performance.

Deep Neural Network for Resource Management in NOMA Networks

Resource management plays a crucial role in improving sum rate of non-orthogonal multiple access (NOMA) networks. However, the traditional resource management methods have considerable complexity, creating a huge challenge in computational efficiency. To handle this challenge, a resource management method is proposed based on a deep neural network (DNN). The key advantage of the method is that the DNN can perform in almost real-time resource allocation because it requires a very simple operation. In this paper, the resource management problem of the NOMA system adopting imperfect successive interference cancellation (SIC) technology at the receivers is studied, including the power allocation stage and the user scheduling stage. For power allocation stage, the generic fully-connected DNN is trained to approximate the power allocation of interior point method (IPM), which not only greatly improves the computational efficiency but also increases the sum rate of the system. Based on the allocated power, the user scheduling algorithm is performed to further increase the system sum rate. Finally, simulation results verify some performances of the proposed algorithms.

Traffic classification for connectionless services with incremental learning

The technological advancement in VoIP technology and P2P streaming led to the development of novel applications. Most of these applications use UDP traffic. The availability of UDP services for applications such as streaming, trivial file transfer, are denied to legitimate users due to malicious traffic, intentionally created by abnormal requesting behaviour of the botnets. Categorizing the traffic is required to discriminate the malicious traffic that occur due to attacks from normal traffic for better real time resource allocation. For this purpose, this paper proposes a two level hybrid classification model based on incremental learning to detect high and low rate attacks that deny the legitimate access to connectionless services. The simulation results show that the proposed incremental learning strategy improves the classification accuracy of the proposed hybrid classifier compared to existing traditional learning methods.

Synthesis of a Cyberphysical Hybrid Microfluidic Platform for Single-Cell Analysis

Single-cell genomics is used to advance our understanding of diseases, such as cancer. Microfluidic solutions have recently been developed to classify cell types or perform single-cell biochemical analysis on preisolated types of cells. However, new techniques are needed to efficiently classify cells and conduct biochemical experiments on multiple cell types concurrently. Nondeterministic cell-type identification, system integration, and design automation are major challenges in this context. To overcome these challenges, we present a hybrid microfluidic platform that enables complete single-cell analysis on a heterogeneous pool of cells. We combine this architecture with an associated design-automation and optimization framework, referred to as co-synthesis (CoSyn). The proposed framework employs real-time resource allocation to coordinate the progression of concurrent cell analysis. Besides this framework, a probabilistic model based on a discrete-time Markov chain is also deployed to investigate protocol settings, where experimental conditions, such as sonication time, vary probabilistically among cell types. Simulation results show that CoSyn efficiently utilizes platform resources and outperforms baseline techniques.

Real-Time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.

Strategic Timing and Pricing In On-Demand Platforms

This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing non- transferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogeneous price- and time-sensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand- capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers’ time-sensitivities. The optimal policy may feature strategic idleness (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers) and deliberate late service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand — high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared to dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is history-independent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes.

A Hierarchical Auction-Based Mechanism for Real-Time Resource Allocation in Cloud Robotic Systems.

Cloud computing enables users to share computing resources on-demand. The cloud computing framework cannot be directly mapped to cloud robotic systems with ad hoc networks since cloud robotic systems have additional constraints such as limited bandwidth and dynamic structure. However, most multirobotic applications with cooperative control adopt this decentralized approach to avoid a single point of failure. Robots need to continuously update intensive data to execute tasks in a coordinated manner, which implies real-time requirements. Thus, a resource allocation strategy is required, especially in such resource-constrained environments. This paper proposes a hierarchical auction-based mechanism, namely link quality matrix (LQM) auction, which is suitable for ad hoc networks by introducing a link quality indicator. The proposed algorithm produces a fast and robust method that is accurate and scalable. It reduces both global communication and unnecessary repeated computation. The proposed method is designed for firm real-time resource retrieval for physical multirobot systems. A joint surveillance scenario empirically validates the proposed mechanism by assessing several practical metrics. The results show that the proposed LQM auction outperforms state-of-the-art algorithms for resource allocation.

Real-time optimization and control mechanisms for collaborative demand and capacity sharing

This work presents a new mechanism for real-time resource allocation, order administration, and process monitoring in Collaborative Networked Enterprises (CNE). The motivation is to mitigate the uncertainties and risks caused by arbitrary nature of customer orders, dynamic changes in demand patterns, and unforeseen supply disruptions. Enterprise collaboration is enabled by Demand and Capacity Sharing (DCS) among enterprises considering four decision criteria: (a) Total cost; (b) Demand fulfillment; (c) Resource utilization; and (d) CNE stability. A Task Administration Protocol (TAP) is designed for priority-based allocation/re-allocation of resources and real-time monitoring of DCS processes. The TAP is enhanced with a Predictive Best Matching Protocol (PBMP), which optimizes allocation decisions in real-time while taking into account potential future events. The new Real-Time Optimization (RTO) mechanism is proven in experiments to be an advantageous solution for mitigating undesirable impacts of uncertainty and dynamicity on the CNE performance with respect to the four undertaken decision criteria.

The fundamental closed-form solution of control-related states of kth order S3PR system with left-side non-sharing resource places of Petri nets

ABSTRACTDue to the state explosion problem, it has been unimaginable to enumerate reachable states for Petri nets. Chao broke the barrier earlier by developing the very first closed-form solution of the number of reachable and other states for marked graphs and the kth order system. Instead of using first-met bad marking, we propose ‘the moment to launch resource allocation’ (MLR) as a partial deadlock avoidance policy for a large, real-time dynamic resource allocation system. Presently, we can use the future deadlock ratio of the current state as the indicator of MLR due to which the ratio can be obtained real-time by a closed-form formula. This paper progresses the application of an MLR concept one step further on Gen-Left kth order systems (one non-sharing resource place in any position of the left-side process), which is also the most fundamental asymmetric net structure, by the construction of the system's closed-form solution of the control-related states (reachable, forbidden, live and deadlock states) with a formula depending on the parameters of k and the location of the non-sharing resource. Here, we kick off a new era of real-time, dynamic resource allocation decisions by constructing a generalisation formula of kth order systems (Gen-Left) with r* on the left side but at arbitrary locations.

Priori Knowledge Guided Approach for Optimal Peer Selection in P2P VoD Systems

With the rise of Video-on-Demand (VoD) systems as a preferred way to distribute video content over IP networks, many research works and innovations have focused on improving the scalability of streaming systems by looking at distributed approaches such as peer-to-peer (P2P). One of the most critical aspects in P2P-assisted streaming system is the real-time resource allocation, which drives the performance of the system in terms of capacity utilization and VoD requests rejection rates. In this paper, we specifically focus on the problem of maximizing the P2P streaming system utilization by effectively alternating between different resource allocation strategies. Switching between different resource allocation strategies is guided by a run-time statistical analysis of performances against predicted content popularity pattern. A key contribution of this paper resides in effectively combining different, and potentially conflicting, performance objectives when deciding on which resource allocation strategy to use. Indeed, we use a Bayesian Fusion to select the most appropriate resource allocation strategy to deal with future content demand. With our P2P resource allocation framework, a VoD service operator can combine any number of resource allocation strategies and formulate different performance objectives that meet the requirements of its network and the content consumption behavior of its users.

Airline new customer tier level forecasting for real-time resource allocation of a miles program

This is a case study on an airline’s miles program resource optimization. The airline had a large miles loyalty program but was not taking advantage of recent data mining techniques. As an example, to predict whether in the coming month(s), a new passenger would become a privileged frequent flyer or not, a linear extrapolation of the miles earned during the past months was used. This information was then used in CRM interactions between the airline and the passenger. The correlation of extrapolation with whether a new user would attain a privileged miles status was 39% when one month of data was used to make a prediction. In contrast, when GBM and other blending techniques were used, a correlation of 70% was achieved. This corresponded to a prediction accuracy of 87% with less than 3% false positives. The accuracy reached 97% if three months of data instead of one were used. An application that ranks users according to their probability to become part of privileged miles-tier was proposed. The application performs real time allocation of limited resources such as available upgrades on a given flight. Moreover, the airline can assign now those resources to the passengers with the highest revenue potential thus increasing the perceived value of the program at no extra cost.

Real Time Resource Allocation Methodology and Quality based Framework in Cloud Computing

Cloud computing provides resources and services as per user’s demand. User can pay for these services as per their use. Many cloud users can request no. of resources or services simultaneously. So resource allocation should be cost effective, reliable and efficient. There are lots of criteria which should be considered while allocating resources to the users. This paper shows resource demand categories and requirements to be considered while allotting resources to the customer. A quality based framework is proposed in this paper.

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