Heterogeneous Delay Requirements Research Articles

Task-Oriented Delay-Aware Multi-Tier Computing in Cell-Free Massive MIMO Systems

Multi-tier computing can enhance the task computation by multi-tier computing nodes. In this paper, we propose a cell-free massive multiple-input multiple-output (MIMO) aided computing system by deploying multi-tier computing nodes to improve the computation performance. At first, we investigate the computational latency and the total energy consumption for task computation, regarded as total cost. Then, we formulate a total cost minimization problem to design the bandwidth allocation and task allocation, while considering realistic heterogenous delay requirements of the computational tasks. Due to the binary task allocation variable, the formulated optimization problem is non-convex. Therefore, we solve the bandwidth allocation and task allocation problem by decoupling the original optimization problem into bandwidth allocation and task allocation subproblems. As the bandwidth allocation problem is a convex optimization problem, we first determine the bandwidth allocation for given task allocation strategy, followed by conceiving the traditional convex optimization strategy to obtain the bandwidth allocation solution. Based on the asymptotic property of received signal-to-interference-plus-noise ratio (SINR) under the cell-free massive MIMO setting and bandwidth allocation solution, we formulate a dual problem to solve the task allocation subproblem by relaxing the binary constraint with Lagrange partial relaxation for heterogenous task delay requirements. At last, simulation results are provided to demonstrate that our proposed task offloading scheme performs better than the benchmark schemes, where the minimum-cost optimal offloading strategy for heterogeneous delay requirements of the computational tasks may be controlled by the asymptotic property of the received SINR in our proposed cell-free massive MIMO-aided multi-tier computing systems.

Correction to: Optimizing computation offloading under heterogeneous delay requirements for wireless powered mobile edge computing

Correction to: Optimizing computation offloading under heterogeneous delay requirements for wireless powered mobile edge computing

Optimizing computation offloading under heterogeneous delay requirements for wireless powered mobile edge computing

Optimizing computation offloading under heterogeneous delay requirements for wireless powered mobile edge computing

Multi-agent reinforcement learning for edge information sharing in vehicular networks

To guarantee the heterogeneous delay requirements of the diverse vehicular services, it is necessary to design a full cooperative policy for both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) links. This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links. Specifically, a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user, respectively. A multi-agent reinforcement learning framework is designed to solve these two problems, where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework. Thereafter, a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward. The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.

Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems

This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.

Throughput Optimization With Delay Guarantee for Massive Random Access of M2M Communications in Industrial IoT

The machine-to-machine (M2M) communication is an emerging technology that is widely utilized in a vast number of industrial Internet-of-Things (IIoT) applications. Due to the diversity of IIoT applications, provisioning of heterogeneous delay requirements of delay-sensitive machine type devices (MTDs) while optimizing the access efficiency of delay-tolerate MTDs becomes a critical challenge for M2M communications. To address this issue, a multigroup analytical framework for massive random access of M2M communications in IIoT is proposed in this article. Specifically, we consider delay-sensitive MTDs and delay-tolerate MTDs coexist in the network, and those MTDs are divided into multiple groups according to their delay requirements. The access behavior of each MTD is characterized by a double-queue model. Based on this model, the throughput and the mean access delay of each group are characterized. It is found that for each group, the mean access delay decreases as the throughput increases and is minimized when the throughput is maximized. To achieve the maximum throughput of delay-tolerate MTDs under delay constraints of delay-sensitive MTDs, the backoff parameters of delay-sensitive MTDs should be tuned according to the delay constraints while that of delay-tolerate MTDs should be tuned further according to the aggregate packet arrival rate and the number of MTDs in each group. It is further demonstrated that the optimal tuning of backoff parameters is robust against the burstiness of input traffic. The analysis sheds important light on the access design of M2M communications in IIoT with delay constraints.

A Distributed and Context-Aware Task Assignment Mechanism for Collaborative Mobile Edge Computing.

Mobile edge computing (MEC) is an emerging technology that leverages computing, storage, and network resources deployed at the proximity of users to offload their delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since task offloading incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in such an environment are (i) should the workload be offloaded to the edge or computed in terminals? (ii) Which edge, among the available ones, should the task be offloaded to? In this paper, we formulate the task assignment problem as a one-to-many matching game which is a powerful tool for studying the formation of a mutual beneficial relationship between two sets of agents. The main goal of our task assignment mechanism design is to reduce overall energy consumption, while satisfying task owners’ heterogeneous delay requirements and supporting good scalability. An intensive simulation is conducted to evaluate the efficiency of our proposed mechanism.

Design and Analysis of EDF Scheduling Algorithm for a Chosen Network Traffic Problem: Implementation and Simulation Analysis

Abstract Workstations, CPU etc are in a period of transition, moving from a relatively slow network links and data oriented services to a high set fibre optic services and more diverse services. As such many of the hard and soft real time will not only demand high bandwidth but also a predictable and guarantee Quality of Service (QoS) along side, which is not offered in a best effort network (BE). The provision and terms of (QoS) to real-time applications is a key issue in promising broadband packet switched networks and network of other sorts. Applications such as voice and video typically require QoS guarantees in terms of end-to-end data transfer delays. Supporting the diverse and of course, heterogeneous delay requirements of applications with widely varying characteristics requires packet scheduling schemes more sophisticated than First-In-First-Out (FIFO) at each switch in the network(2) are one of most accepted examples of such scheduling schemes. This points to the convincing need for a QoS, which apportion a high network utilization to be realized. In this paper, we focus on the EDF scheduling scheme, since it is known to provide the optimal delay performance (2), in the deterministic environment, and can for this reason be expected to perform well in the real time setting as well. The large amount of bandwidth as promised by the future network also offer the integration of real time applications, hard or soft.

Interference-Aware Resource Control in Multi-Antenna Cognitive Ad Hoc Networks with Heterogeneous Delay Constraints

In this work, we consider a multi-antenna cognitive ad hoc network (CAHNet) with heterogeneous delay requirements. To fulfill the interference and delay constraints simultaneously, we propose to perform adaptive zero-forcing beamforming (ZFBF) at cognitive transmitters according to interference channel state information (CSI). To assist the CAHNet to obtain the interference CSI, we use a win-win inter-network cooperation strategy, namely quantized interference CSI feedback from the primary network to CAHNet through a feedback link, under the condition that the CAHNet pays a proper price for it. Considering the scarcity of feedback and power resources, we focus on the minimization of the overall resource cost subject to both interference and delay constraints. To solve the problem, we derive a joint feedback and power control algorithm amongst multiple links of CAHNet. Finally, simulation results validate the effectiveness of the proposed algorithm.

Combining explicit admission control and congestion control for predictable data transfers in grids

To improve the Grid infrastructure’s efficiency, the co-reservation of distributed resources is often required. Therefore, Grid applications need to move large amounts of data between these resources within deterministic time frames. In most cases it is possible to specify the volume and the deadline in advance. This paper proposes an approach for data-movement management and bandwidth reservation in Grid, which provides a high acceptance probability of flows in the network while maintaining efficient network-resource utilization. To achieve this, our proposal combines explicit admission control and high-speed transport protocols to enable an opportunistic sharing of the capacity by flows having heterogeneous bandwidth and delay requirements. We formulate the problem and discuss several objective functions. Then we present different heuristics and evaluate them according to the request’s acceptance rate and the network’s utilization metrics. Our simulations include all the communication and computation overheads which are involved in such data transfers.

Radio resource allocation in OFDM-based cooperative relaying networks for a mixture of elastic and streaming traffic

In this study, the authors formulate the optimal radio resource allocation for OFDM-based relay networks with heterogeneous delay requirements. The authors consider streaming traffic that requires a maximum guaranteed average delay and elastic traffic with flexible rate requirements. The main objective is to maximise the total transmission rate of elastic users, while average delay constraint for streaming traffic is satisfied. In the proposed formulation we also consider transmit power constraint for the base station as well as each relay. The authors then propose an algorithm based on dual approach to find the optimum subcarrier, relay assignment and power allocation. Using simulations, the authors evaluate the impact of streaming traffic on the total rate of elastic users for different number of relays and various power constraints.

Delay-Optimal User Scheduling and Inter-Cell Interference Management in Cellular Network via Distributive Stochastic Learning

In this paper, we propose a distributive queue-aware intra-cell user scheduling and inter-cell interference (ICI) management control design for a delay-optimal celluar downlink system with M base stations (BSs), and K users in each cell. Each BS has K downlink queues for K users respectively with heterogeneous arrivals and delay requirements. The ICI management control is adaptive to joint queue state information (QSI) over a slow time scale, while the user scheduling control is adaptive to both the joint QSI and the joint channel state information (CSI) over a faster time scale. We show that the problem can be modeled as an infinite horizon average cost Partially Observed Markov Decision Problem (POMDP), which is NP-hard in general. By exploiting the special structure of the problem, we shall derive an equivalent Bellman equation to solve the POMDP problem. To address the distributive requirement and the issue of dimensionality and computation complexity, we derive a distributive online stochastic learning algorithm, which only requires local QSI and local CSI at each of the M BSs. We show that the proposed learning algorithm converges almost-surely (with probability 1) and has significant gain compared with various baselines. The proposed solution only has linear complexity order O(MK).

Distributive Stochastic Learning for Delay-Optimal OFDMA Power and Subband Allocation

In this paper, we consider the distributive queue-aware power and subband allocation design for a delay-optimal OFDMA uplink system with one base station, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> users and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> independent subbands. Each mobile has an uplink queue with heterogeneous packet arrivals and delay requirements. We model the problem as an infinite horizon average reward Markov decision problem (MDP) where the control actions are functions of the instantaneous channel state information (CSI) as well as the joint queue state information (QSI). To address the distributive requirement and the issue of exponential memory requirement and computational complexity, we approximate the subband allocation <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -factor by the sum of the per-user subband allocation <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -factor and derive a distributive online stochastic learning algorithm to estimate the per-user <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -factor and the Lagrange multipliers (LM) simultaneously and determine the control actions using an auction mechanism. We show that under the proposed auction mechanism, the distributive online learning converges almost surely (with probability 1). For illustration, we apply the proposed distributive stochastic learning framework to an application example with exponential packet size distribution. We show that the delay-optimal power control has the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multilevel water-filling</i> structure where the CSI determines the instantaneous power allocation and the QSI determines the water-level. The proposed algorithm has linear signaling overhead and computational complexity <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KN</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> ), which is desirable from an implementation perspective.

Cross-Layer Scheduler for Wimax Networks with Outdated Channel State Information to Support Guaranteed QoS

The proposed Cross-Layer scheduling can boost the spectral efficiency of multi-user OFDMA wireless systems with heterogeneous delay requirements. The existing designs usually have two important assumptions that are the users are delay insensitive and Channel State Information at the Transmitter (CSIT) is perfect. In practice, users have heterogeneous delay requirements and CSIT usually becomes outdated in time varying channel which in turn leads to systematic packet errors and hence, results in significant degradation on the throughput. The Adaptive Modulation and Coding (AMC) is a promising tool for increasing the spectral efficiency of time varying channel while maintaining the target Bit Error Rate (BER) and the Packet Error Rate (PER). In this study, a novel design problem is formulated which combines AMC and CSI at the physical layer and scheduling using queuing theory at the Medium Access Control (MAC) layer, in order to maximize the throughput and spectral efficiency under the heterogeneous delay constraints. For the proposed research, transmissions on Rayleigh fading channel including Additive White Gaussian Noise (AWGN) are employed. Simulation results show that the proposed scheduler provides robust system performance enhancement over conventional cross-layer scheduler with perfect CSIT.

Delay-optimal power and subcarrier allocation for OFDMA systems via stochastic approximation

In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> subcarriers, K mobiles and one base station. There are K queues at the base station for the downlink traffic to the K mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a K-dimensional infinite horizon average reward Markov Decision Problem (MDP) where the control actions are assumed to be a function of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). We propose an online stochastic value iteration solution using stochastic approximation. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under two mild conditions in Theorem 1, the proposed solution converges to the optimal solution almost surely (with probability 1) and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics, we obtain a reduced complexity decomposed solution with linear O(KN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> ) complexity and O(K) memory requirement.

Design and Analysis of Delay-Sensitive Cross-Layer OFDMA Systems with Outdated CSIT

It is well known that cross-layer scheduling can boost the spectral efficiency of multi-user OFDMA systems through multi-user selection diversity but existing designs usually have two important assumptions - users are delay-insensitive and channel state information at the transmitter (CSIT) is perfect. In practice, users have heterogeneous delay requirements and CSIT usually becomes outdated in time varying channel, which in turns leads to systematic packet errors and hence results in significant degradation on the throughput and delay performance in the OFDMA systems. In this paper, a novel cross-layer design problem is formulated as a convex optimization problem, on which a delay-sensitive jointly optimal power, rate and subcarrier allocation scheme is proposed so as to maintain heterogeneous users' delay requirement as well as achieving a target packet outage probability through combining queueing theory and information theory. Furthermore, we obtain closed-form asymptotic performance of the proposed delay-sensitive scheduler. Unlike the well-known SNR gain of Theta(log - sigma2 DeltaH) in conventional cross-layer scheduler with perfect CSIT, we demonstrate a cross-layer SNR gain of Theta((1 - sigma2 DeltaH)log K)can still be achieved under heterogeneous delay constraints and outdated CSIT with error variance sigma2 DeltaH. Finally, simulation results show that our proposed delay-sensitive CSIT error considerate schemes provide robust system performance enhancement over conventional CSIT error inconsiderate opportunistic scheduler and naive queue length based MAX-Weight scheduler while satisfying heterogeneous delay requirements even at moderate to high CSIT errors.

Delay-optimal power and precoder adaptation for multi-stream MIMO systems

In this paper, we consider delay-optimal MIMO precoder and power allocation design for a MIMO link in wireless fading channels. There are L data streams spatially multiplexed onto the MIMO link with heterogeneous packet arrivals and delay requirements. The transmitter is assumed to have knowledge of the channel state information (CSI) as well as the joint queue state information (QSI) of the L buffers. Using L-dimensional Markov decision problem (MDP), we obtain optimal precoding and power allocation policies for general delay regime, which consists of an online solution and an offline solution. The online solution has negligible complexity but the offline solution has worst case complexity part((N + 1)L) where N is the buffer size. Using static sorting of the L eigenchannels, we decompose the MDP into L independent 1-dimensional subproblems and obtained low complexity offline solution with linear complexity order part(NL) and close-to-optimal performance.

Packet Utility Based Packet Scheduling for OFDMA Networks with Heterogeneous Delay Requirements

A delay-oriented packet scheduling scheme is proposed for downlink OFDMA networks with heterogeneous delay requirements. Using a novel packet utility concept, the proposed algorithm can exploit diversity from traffic characteristics and requirements to improve delay performance for delay sensitive traffics. Besides, the proposal also shows good ability in balancing fairness and efficiency. Simulation results show that our proposal outperforms existing delay-oriented scheduling schemes in terms of both delay performance and spectrum efficiency.

Design and analysis of delay-sensitive decentralized cross-layer OFDMA systems with efficient feedback algorithm

Abstract-Delay-sensitive cross-layer design has recently attracted increasing interests in providing delay performance guarantee and exploiting multi-user diversity throughput advantage for real-time traffic. Most of the existing solutions are centralized which are undesirable from the complexity scalability, signaling overhead scalability as well as the flexibility w.r.t. dynamic active sessions. In this work, we focus on delay-sensitive decentralized cross-layer design of OFDMA systems. Due to the stochastic system state, combinatorial nature of subcarrier allocation as well as the consideration for efficient feedback, conventional brute force decomposition technique failed to work. By using a novel two-level dual decomposition technique with comparison argument, we shall derive a feedback-efficient decentralized OFDMA cross-layer solution, with consideration of the stochastic system state, heterogeneous delay requirements, outdated CSIT as well as the combinatorial nature of subcarrier allocation. The proposed distributive design achieves optimal performance as the centralized solution scheduling with scalable complexity, signaling overhead as well as flexibility w.r.t. dynamic active sessions in the systems. In addition, using asymptotic analysis, we have derived the minimum feedback cost for ϵ-optimal system performance, wherein the average feedback cost per user is also shown to quickly approaches zero as systems scales.

Optimal Dynamic Resource Allocation for Multi-Antenna Broadcasting With Heterogeneous Delay-Constrained Traffic

This paper is concerned with dynamic resource allocation in a cellular wireless network with slow fading for support of data traffic having heterogeneous transmission delay requirements. The multiple-input single-output (MISO) fading broadcast channel (BC) is of interest where the base station (BS) employs multiple transmit antennas to realize simultaneous downlink transmission at the same frequency to multiple mobile users each having a single receive antenna. An information-theoretic approach is taken for characterizing capacity limits of the fading MISO-BC under various transmission delay considerations. First, this paper studies transmit optimization at the BS when some users have delay-tolerant ldquopacketrdquo data and the others have delay-sensitive ldquocircuitrdquo data for transmission at the same time. Based on the convex optimization framework, an online resource allocation algorithm is derived that is amenable to efficient cross-layer implementation of both physical (PHY) - layer multi-antenna transmission and media-access-control (MAC) - layer multiuser rate scheduling. Secondly, this paper investigates the fundamental throughput-delay tradeoff for transmission over the fading MISO-BC. By comparing the network throughput under completely relaxed versus strictly zero transmission delay constraint, this paper characterizes the limiting loss in sum capacity due to the vanishing delay tolerance, termed the delay penalty, under some prescribed user fairness for transmit rate allocation.