Lyapunov Optimization Research Articles

Elastic Resource Allocation for Coded Distributed Computing Over Heterogeneous Wireless Edge Networks

Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

Stochastic Digital-Twin Service Demand With Edge Response: An Incentive-Based Congestion Control Approach

The emergence of Digital Twin Edge Networks (DTENs) achieves the mapping of real physical entities to digital models of cyberspace. By offloading real-time mobile data to Mobile Edge Computing (MEC) servers for processing and modeling, communication-efficient Digital Twin (DT) services could be achieved. However, the spatio-temporal dynamic DT service demand stochastically generated by mobile users easily causes service congestion, which challenges the long-term DT service stability. Meanwhile, current DT services still lack long-term effective incentive designs for participants. To solve these issues, we design an incentive-based congestion control scheme for stochastic demand response in DTENs. First, we adopt the Lyapunov optimization theory to decompose the long-term congestion control decision into a sequence of online edge association decisions, with no need for future system information. We then present a contract-based incentive design to optimize the long-term profit of the DT service provider, comprehensively considering the delay sensitivity, incentive compatibility, and individual rationality. Finally, experimental simulations are carried out to verify the superiority of the proposed scheme with the base station dataset of Shanghai Telecom. Theoretical and simulation analysis demonstrates that compared with benchmarks, our scheme could effectively avoid long-term service congestion with an arbitrarily near-optimal profit.

Efficient Consensus and Elastic Resource Allocation Empowered Blockchain for Vehicular Networks

With the advantages of decentralization and high reliability, the blockchain is widely used in vehicular networks, which has attracted widespread attention from academia and industries. However, when combining blockchain into vehicular networks, there exist three challenges: 1) how to access pre-deployed blockchain when vehicles are disconnected from network infrastructures, 2) how to reduce the communication overhead caused by an increasing network scale while maintaining decentralization of blockchains, 3) how to balance limited vehicle's resources and high throughput of blockchain. To address these challenges, we propose an efficient Device-to-Device (D2D) Network-empowered blockchain framework to receive and process transactions from D2D vehicles. We then propose a grouped and leaderless consensus mechanism to reduce communication overhead as the network scale increases. We also propose an elastic resource allocation scheme using the Lyapunov optimization theory to achieve high throughput given limited resources. Finally, numerical results show that the proposed mechanisms achieve D2D interaction under different network conditions and better performance than existing approaches.

DRL-Based Computation Offloading With Queue Stability for Vehicular-Cloud-Assisted Mobile Edge Computing Systems

Computation offloading is considered as a promising method to improve computation performance of Intelligent Vehicles (IVs), where IVs can offload resource-hungry applications to Mobile Edge Computing (MEC) servers. However, due to the dynamic environment of Intelligent Transportation Systems, it's challenging to develop an effective offloading scheme that ensures stable operation of the system. Furthermore, IVs only offload tasks to MEC servers or parked vehicles, how to take full advantage of resources in a random traffic flow is a main challenge. In this paper, we propose a vehicular-cloud-assisted MEC network in urban cities to fully explore the idle resources and ensure the stability of task queue. Specifically, we develop a novel virtual platform based on deep neural network that predicts vehicles trajectory and creates vehicular cloud to integrate computation resources. We then design a vehicular-cloud-assisted offloading scheme that aims to maximize task throughput in continuous time slots subject to multi-constraint. To address the coupling among multiple variables and different time slots, we put forward a lightweight framework to apply the Lyapunov optimization in combination with deep reinforcement learning. In particular, we apply Lyapunov drift-plus-penalty approach to decompose the initial problem into per-slot subproblems and control the long-term task queue stability. To further reduce the computation complexity, we decouple the subproblem into three processes, and the deep Q-network is put forward to solve it. Extensive simulations demonstrate that under different task arrivals, our proposed scheme can achieve the task queue stability within 10 time slots with a low transmission power.

An Online Resource Management for Obscured Sensors in Agriculture using UAV

Agriculture-based Internet of Things (Ag-IoT) can generate near-real-time quantitative data to analyze crop growth. Unmanned Aerial Vehicle (UAV) based mobile edge computing systems is widely used in Ag-IoT due to the low cost, fast movement and ease of operation of UAVs. Nevertheless, sensors in agriculture may be under a tall and dense crop canopy or under the soil where they have difficulty harvesting a steady of energy and have too worse a channel condition to transmit more data. This paper considers a UAV-Sensor Collaboration Wireless Network (USCWN) in which each UAV carries a computation module and a wireless power transfer module to provide service and energy to sensors. Then, we propose an online resource management framework with two sub-algorithms to maximize the network throughput of USCWN. The first is the sensor fuzzy selection algorithm, which determines which UAV the sensor transmits data to, considering the different resource properties between sensors and UAVs without prior knowledge. The other is a semi-distributed dynamic resource allocation algorithm in which the sensors compete with UAV’s energy, computation, and communication resources in a stochastic game. The UAV acts as a game manager to manage sensor resources. The stochastic game is solved using Lyapunov optimization, which yields a coarse correlated equilibrium that is better than the nash equilibrium. Performance evaluation shows that our proposed framework has a higher network throughput than other agricultural works and maintains stable sensor energy.

A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing

Satellite edge computing (SEC) has garnered significant attention for its potential to deliver services directly to users. However, the uneven distribution of receiving tasks among satellites in the constellation can lead to uneven utilization of computing resources. This paper proposes a task offloading strategy for SEC that aims to minimize the average delay and energy consumption of tasks by assigning them to appropriate satellite nodes. The approach uses Lyapunov optimization to convert the long-term optimization problem with task queue length constraints into an assignment problem within a single time slot and solve it based on the Hungarian algorithm. Experimental simulations have shown that the proposed algorithm performs better than other baseline algorithms.

Jointly optimized resource allocation for SDN control and forwarding planes in edge-cloud SDN-based networks

Today’s resource allocation strategies for edge-cloud network based on Software-Defined Network (SDN) and Network Function Virtualization (NFV) architectures often make a decision of placing resource based on network locations, i.e. edge or core cloud. Unfortunately, these strategies are not optimally tailored for a growing class of Internet-of-Things (IoT) services whose traffic is designated to go through a Virtual Network Functions (VNF) service chain. Without considering the communication between SDN control plane and forwarding plane, inappropriate SDN controllers could be selected for switches, which may result in a significant setup delay of the whole service chain. In addition, the fluctuate demand of IoT services requires the entire system, that is composed of controllers, forwarding elements, i.e. switches, and physical machines, being stable during service provisioning. In this paper, we jointly address two issues, i.e. SDN controller-switch assignment and VNF placement, both are high-complexity problems. We formulate the joint optimization problem of dynamically provisioning resource for SDN controllers and VNFs in a long run. We then apply the Lyapunov optimization framework to transform a long-term optimization problem into a series of real-time problems and employ exponentiated gradient ascent (EGA) method to find a near-optimal solution. Extensive simulation and experiments show that our approach helps to optimize the system cost up to 11 ∼ 40% depending on service demand and network size.

Self-adaptive end-to-end resource management for real-time monitoring in cyber–physical systems

This paper proposes a novel self-adaptive resource management framework that preserves a low end-to-end monitoring delay in large-scale cyber–physical systems (CPS) while providing high monitoring resolution. According to the tradeoff relationship between the end-to-end delay and monitoring resolution, our proposed algorithm is designed inspired by Lyapunov optimization framework that is mathematically time-average utility optimal under delay/stability constraints. Our proposed Lyapunov optimization-based framework dynamically controls both sensing period on sensor nodes and analysis period on back-end server nodes based on the workload of network and CPU resources. Moreover, our framework does not require additional messages between sensor and server nodes. In our framework, each node autonomously recognizes the underuse or overuse of network and CPU resources with only local state information and self-adapts periods of sensing and analysis under Lyapunov optimization theory. Furthermore, our framework provides different priorities for classes of sensing data and adaptively limits periods of sensing and analysis of low-priority classes. Our proposed framework is designed and implemented on a well-known publish/subscribe (pub/sub) network protocol, Message Queuing Telemetry Transport (MQTT), and finally we can confirm that our proposed framework achieves low end-to-end monitoring delays and high monitoring resolutions.

Energy-optimal DNN model placement in UAV-enabled edge computing networks

Unmanned aerial vehicle (UAV)-enabled edge computing is emerging as a potential enabler for Artificial Intelligence of Things (AIoT) in the forthcoming sixth-generation (6G) communication networks. With the use of flexible UAVs, massive sensing data is gathered and processed promptly without considering geographical locations. Deep neural networks (DNNs) are becoming a driving force to extract valuable information from sensing data. However, the lightweight servers installed on UAVs are not able to meet the extremely high requirements of inference tasks due to the limited battery capacities of UAVs. In this work, we investigate a DNN model placement problem for AIoT applications, where the trained DNN models are selected and placed on UAVs to execute inference tasks locally. It is impractical to obtain future DNN model request profiles and system operation states in UAV-enabled edge computing. The Lyapunov optimization technique is leveraged for the proposed DNN model placement problem. Based on the observed system overview, an advanced online placement (AOP) algorithm is developed to solve the transformed problem in each time slot, which can reduce DNN model transmission delay and disk I/O energy cost simultaneously while keeping the input data queues stable. Finally, extensive simulations are provided to depict the effectiveness of the AOP algorithm. The numerical results demonstrate that the AOP algorithm can reduce 18.14% of the model placement cost and 29.89% of the input data queue backlog on average by comparing it with benchmark algorithms.

Age of Processed Information Minimization Over Fading Multiple Access Channels

In many real-time applications, timely accessibility to actionable insights is an important requirement. We consider users equipped with sensors and actuators, where the sensors generate updates which need to be processed for driving the actuators. The processing task can be carried out locally or at an edge server located at a common base station (BS). The users access the BS via a fading multiple access channel (MAC) using a non-orthogonal multiple access technique. To measure the timeliness of processed information, we adopt a metric called age of processed information (AoPI), defined as the time elapsed since the generation of the last successfully processed packet available to the user. In this setting, we formulate an average AoPI minimization problem, when users are subjected to average power constraints. The BS has to decide when the users should generate status updates and whether to process them locally or at the edge server. We cast the problem as a constrained Markov decision process (CMDP) and obtain its solution via Lagrangian relaxation. We then obtain a simpler policy via Lyapunov optimization and bound its performance. Via numerical simulations, we finally illustrate properties of the CMDP solution and study behavior of average AoPIs under different policies when problem parameters are varied.

Lyapunov-guided optimal service placement in vehicular edge computing

Vehicular Edge Computing (VEC) brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles. However, the limited computing capability of VEC cannot simultaneously respond to large amounts of offloading requests, thus restricting the performance of VEC system. Besides, a mass of traffic data can incur tremendous pressure on the front-haul links between vehicles and the edge server. To strengthen the performance of VEC, in this paper we propose to place services beforehand at the edge server, e.g., by deploying the services/tasks-oriented data (e.g., related libraries and databases) in advance at the network edge, instead of downloading them from the remote data center or offloading them from vehicles during the runtime. In this paper, we formulate the service placement problem in VEC to minimize the average response latency for all requested services along the slotted timeline. Specifically, the time slot spanned optimization problem is converted into per-slot optimization problems based on the Lyapunov optimization. Then a greedy heuristic is introduced to the drift-plus-penalty-based algorithm for seeking the approximate solution. The simulation results reveal its advantages over others in terms of optimal values and our strategy can satisfy the long-term energy constraint.

Energy-Constrained Indoor Air Quality Optimization for HVAC System in Smart Building

The heating, ventilation, air conditioning (HVAC) system is a common ventilation system applied to an indoor environment based on Internet of Things (IoT) technology. In HVAC, providing a comfortable temperature under the constraint of energy is a major challenge. In this article, we propose an air quality optimization strategy to control the air supply and energy consumption, and build several dynamic models to capture the stochastic processes in HVAC. Besides, a system utility maximization problem is formulated. We provide a solution framework based on the Lyapunov optimization method. Based on this framework, we propose a utility-optimal air quality optimization algorithm to solve the subproblem, and theoretically prove that it can achieve the near-optimal system utility. Additionally, the upper bound of the indoor temperature is derived, and the optimality of the algorithm is analyzed. Simulation results show the impact of the system parameter on the HVAC system and the indoor temperature, and verify that the proposed strategy and methods can maintain the comfortable temperature range and supply more fresh air effectively under the constraints of energy consumption.

Energy-Efficient Joint Computation Offloading and Resource Allocation Strategy for ISAC-Aided 6G V2X Networks

Integrated sensing and communications (ISAC) is a pillar technology of 6G to enable intelligent environment awareness and hardware cost efficiencies. In ISAC-aided 6G vehicle-to-everything (V2X) networks, the perception data fusion from multiple sources can make a reliable and efficient data sharing to guarantee driving safety. However, the contradiction between task delay requirement and energy consumption becomes more and more prominent with the growth of computing task amount. Therefore, this paper proposes a joint computation offloading and resource allocation strategy to build greener V2X networks with mobile-edge computing (MEC) and ISAC technologies. A data fusion architecture for cooperative perception is first introduced to support fusing massive perception data from wireless infrastructures and vehicles. Then, a minimization problem of queuing latency is formulated with long-term latency and energy consumption constraints for data fusion computing tasks. The problem is further reformulated by the Lyapunov optimization method to transfer delay and energy constraints into queue stability problems. Finally, a joint computation offloading and resource allocation (JCORA) scheme is proposed to obtain the optimal computation offloading and resource allocation decision, achieving the balance between task delay and energy consumption. Extensive simulations validate the effectiveness of the proposed strategy compared with other baseline schemes.

A Distributed Online Algorithm for Promoting Energy Sharing Between EV Charging Stations

In recent years, electric vehicle (EV) charging stations have experienced an increasing supply-demand mismatch due to their fluctuating renewable generation and unpredictable charging demands. To reduce their operating costs, this paper proposes a distributed online algorithm to promote energy sharing between charging stations. We begin with the offline and centralized version of the EV charging stations operation problem, whose objective is to minimize the long-term time-average total cost. Then, we develop an online implementation approach based on the Lyapunov optimization framework. Although the proposed online algorithm runs in a prediction-free manner, we prove that by properly choosing the parameters, the time-coupling constraints remain satisfied. We also provide a theoretical bound for the optimality gap between the offline and online optimums. Furthermore, an improved alternating direction method of multipliers (ADMM) algorithm with an iteration truncation is proposed to enable distributed computation. The proposed algorithm can protect privacy while being suitable for online implementation. Case studies validate the effectiveness of the theoretical results. Performance comparisons are carried out to demonstrate the advantages of the proposed method.

Online Energy Consumption Optimization in WPCNs With Time-Varying Energy Storage Efficiency

This work considers a wireless powered communication network (WPCN), in which wireless nodes store the energy from an energy access point in their batteries for subsequent data transmission. An online energy consumption optimization strategy is proposed for adaptively determining the beamforming vector, data routing, network operation mode and transmitted power based only on the current state of WPCN. In most existing results, the energy/data transmission of WPCNs is based on the ideal battery models and the energy storage efficiencies therein are always assumed to be non-zero constants. Since the energy storage efficiency of batteries may be affected by the ambient environment or aging in real-time, this work considers a WPCN with a time-varying energy storage efficiencies sequence and correspondingly develops an improved Lyapunov optimization strategy to offset the impact of the time-varying energy storage efficiencies. More importantly, a distributed strategy is proposed to optimize the cooperation of wireless nodes over unrestricted numbers of hops, and thus the energy access point does not require channel state information of all data links and the data backlog queues of all nodes during the solving process. Accordingly, the computational burden at the energy access point is greatly reduced due to the use of this distributed strategy. Under this strategy, the time-averaged expected energy consumption of WPCN can be within a bounded gap of the minimum energy required to maintain stability of the network. Finally, the theoretical analysis is further corroborated by simulation results.

Joint DNN Partition and Resource Allocation Optimization for Energy-Constrained Hierarchical Edge-Cloud Systems

Hierarchical edge-cloud systems collaboratively utilize the resources of both the edge server and central cloud, enabling deep neural network (DNN) partition between the edge and cloud to accelerate the inference. However, the limited energy budgets of both the edge server and central cloud restrict them from providing optimal DNN inference services. Moreover, considering the high dynamics in stochastic environments, the long-term system performance should be optimized under long-term energy constraints. How to improve the long-term DNN inference performance in such energy-constrained hierarchical edge-cloud systems is less studied by existing related works. In this paper, we aim to jointly optimize DNN partition and computing resource allocation to minimize the long-term average end-to-end delay of multiple types of deep learning (DL) tasks while guaranteeing the energy consumption of the edge server and central cloud within their energy budgets. Based on the Lyapunov optimization technique and reinforcement learning, we design a novel deep deterministic policy gradient based DNN partition and resource allocation (DDPRA) algorithm to train policy to decide DNN partition dynamically by observing the environment. Moreover, the DDPRA algorithm is embedded with a heuristic computing resource allocation (HCRA) algorithm, which effectively reduces the complexity of policy training by decoupling and optimizing the computing resource allocation separately. We analyze the complexity of our algorithms and conduct extensive simulations. The numerical results demonstrate the superiority of our algorithm in comparison with 5 other schemes in multiple scenarios.

Cross-Layer Resource Allocation in HetNet NOMA Systems With Dynamic Traffic Arrivals

Non-orthogonal multiple access (NOMA) has become a promising candidate aiming to enhance the system performance in the fifth generation of wireless communication. However, most existing studies on the resource allocation of NOMA systems only consider the short-term performance improvement but ignore the problems caused by the imperfect time-varying channels and the dynamic traffic arrivals, which will result in unsuccessful transmission issues. Different from the existing approaches, we propose a long-term cross-layer resource allocation model with dynamic traffic arrivals and limited channel information. With the low-overhead one-bit feedback, the optimal decoding order, user scheduling, and power allocation are analyzed. Specifically, the problem is formulated as a stochastic optimization framework to minimize long-term power consumption. By applying the Lyapunov optimization framework, the problem can be transformed into a joint traffic rate control and mixed-integer programming resource allocation problem. This NP-hard problem is difficult to be solved directly. Therefore, we devise an efficient sub-optimal algorithm with the dynamic penalty factor. In our theoretical analysis, we prove that the delay-power tradeoff can be achieved by tuning a control parameter. The simulation results confirm that our proposed algorithms can efficiently reduce the power consumption compared with the baseline algorithms while satisfying the QoS requirements.

Joint Task Offloading and Resource Allocation for Accuracy-Aware Machine-Learning-Based IIoT Applications

Machine learning (ML) plays a key role in Intelligent Industrial Internet of Things (IIoT) applications. Processing of the computation-intensive ML tasks can be largely enhanced by applying edge computing (EC) to traditional cloud-based schemes. System optimizations in the existing works always ignore the inference accuracy of ML models with different complexities, and their impacts on error task inference. In this article, we propose a joint task offloading and resource allocation scheme for accuracy-aware machine-learning-based IIoT applications in an edge–cloud-based network architecture. We aim at minimizing the long-term average system cost affected by the task offloading, computing resource allocation, and inference accuracy of the ML models deployed on the sensors, edge server, and cloud server. The Lyapunov optimization technique is applied to convert the long-term stochastic optimization problem into a short-term deterministic problem. An optimal algorithm based on the general Benders decomposition (GBD) technology and a heuristic algorithm based on proportional computing resource allocation and task offloading strategy comparison are proposed to efficiently solve the problem, respectively. The performance of our scheme is proved by theoretical analysis and evaluated by extensive simulations conducted in multiple scenarios. Simulation results demonstrate the effectiveness and superiority of our two algorithms in comparison with several other schemes proposed by the existing works.

Real-time energy optimization and scheduling of buildings integrated with renewable microgrid

Real-time energy optimization is essential for effective load scheduling, cost reduction, maintaining demand and supply balance, and ensuring reliable power system operations. However, real-time energy optimization is challenging due to the unpredictable nature of renewable energy sources (RES) and the behavior of electric loads. On this note, a rigid model is required that can deal with this dilemma. Thus, the Lyapunov optimization technique (LOT) emerged as a solution for the real-time energy optimization problem. This work investigates a smart home equipped with inflexible loads (TV, computer, light, etc.), flexible loads (EVs, HVAC, water heaters, etc.), and RES (photovoltaic and wind energy) in a grid-connected mode that ensures energy trading (purchasing and selling of energy). The aim is to optimize total cost, thermal discomfort cost, and batteries and EVs charging/discharging using LOT by real-time energy optimization, which does not require any system parameters to be anticipated. The proposed algorithm employs LOT for four queues to solve the real-time energy optimization problem. Simulations are conducted for different scenarios and varying weather conditions to endorse the effectiveness of the developed real-time energy optimization solution in various aspects of the performance metrics.

Energy Efficiency and Delay Optimization of Virtual Slicing of Fog Radio Access Network

To develop the energy efficient of 6G networks, the fog radio access network (F-RAN) is expected to meet various use cases of high-performance mobile services. Although network virtualization greatly enhances the flexibility to accommodate various services, the interaction and optimization between delay and energy efficiency (EE) in virtual slicing (VS)-based F-RAN have not been well studied. To accomplish the EE and delay optimization of VS of F-RAN. The key technical challenges lie in the construction of new network architecture, the integration and optimization of radio, caching, and computing 3-D resources, reducing the algorithm’s complexity, and simulation verification. We first design a novel network architecture based on VS and fog computing. The VS method in F-RAN to embed VS assembles virtual radio, caching, and computing resources into physical substrates, transforming nonconvex problems into convex optimization problems by transforming constraints and using Lyapunov optimization methods. We further propose a virtual resource allocation optimization algorithm based on EE. To reduce the complexity of the algorithm, a low-complexity EE optimization algorithm is further proposed for virtual resource allocation. The simulation results show that the low-complexity virtual resource allocation EE optimization algorithm proposed in this article has better performance than the existing fog access network resource allocation methods. The EE is further improved by about 30% under the condition that the guaranteed delay threshold is two slots (i.e., 1 ms).