Benchmark Algorithms In Terms Research Articles

Energy-performance aware virtual machines migration in cloud network by using prediction and fuzzy approaches

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration.

GAN-powered heterogeneous multi-agent reinforcement learning for UAV-assisted task offloading

The flexible and highly mobile unmanned aerial vehicle (UAV) with computing capabilities can improve the quality of experience (QoE) of ground users (GUs) according to real-time service requirements by performing flight maneuvers. In this study, we investigate a task offloading scheme and trajectory optimization in a multi-UAV-assisted system, where UAVs offload a portion of multiple GUs’ computational tasks. The optimization problem is formulated to jointly minimize the energy consumption of UAVs and the task latency of GUs by optimizing trajectory, task allocation and offloading proportion. This paper proposes a heterogeneous multi-agent reinforcement learning (MARL)-based approach to solve the issue in high dimensions and limited states, where UAV and GU are treated separately as two different types of agents. Due to the high cost and low sample efficiency of online training of RL algorithms, a generative adversarial network (GAN)-powered auxiliary training mechanism is proposed, which reduces the overhead of interacting with the real world and makes the agent’s policy appropriate for real-world execution environment via offline training with generated environment states. Numerical evaluation results demonstrate that the proposed algorithm outperforms other benchmark algorithms in terms of energy consumption and task latency.

Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach

To cope with climate change and other environmental problems, countries and regions around the world have begun to pay attention to the development of renewable energy under the drive of achieving the global carbon emission peak and carbon neutrality goal. The distributed photovoltaic (PV) power grid is an effective solution that can utilize solar energy resources to provide clean a energy supply. However, with the continuous grid connection of distributed energy, it poses great challenges to the power supply stability and security of the grid. Therefore, it is particularly important to promote the local consumption of distributed energy and the construction of the energy internet. This paper aims to study the cooperative operation and energy optimization scheduling problem among distributed PV power grids, and proposes a new scheme to reduce the electricity cost under the constraint of power supply and demand balance. The optimization problem is modeled as a Markov decision process (MDP), and a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used to solve the MDP problem. Simulation results show that the proposed algorithm outperforms other benchmark algorithms in terms of reducing electricity cost, convergence and stability, and verifies its effectiveness.

Device Access, Subchannel Division, and Transmission Power Allocation for NOMA-Enabled IoT Systems

In the era of the Internet of Things (IoT), it is a promising way to improve system energy utility and better meet users’ requirements for quality of service (QoS) via integrating non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) technologies. In light of this idea, we investigate device access, sub-channel division, and transmission power allocation for NOMA-enabled IoT systems. To maximize the energy utility of IoT systems while satisfying the minimum demands of IoT devices (IoTDs) on achievable uplink data rate, a joint optimization problem is formulated with the consideration of device access, sub-channel division, and transmission power allocation. Due to the non-convexity of this problem, we propose an alternating optimization algorithm aiming to find the optimal solution. The proposed algorithm first decomposes the joint optimization problem into three sub-problems through the block coordinate descent (BCD), and then obtains the near-optimal solution by solving the decomposed sub-problems alternately. Extensive simulations validate our analysis for the convergence of the proposed algorithm. The numerical results demonstrate that the proposed algorithm significantly outperforms the benchmark algorithms in terms of improving system energy utility.

Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network.

To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on-off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on-off design.

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology

Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the network. To address these issues, we first propose a SAGIN architecture with energy-harvesting devices, where the base station (BS) is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power an unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighboring BSs is designed to fully utilize RE to reduce carbon emissions. Secondly, on the basis of task offloading decisions, the UAV trajectory, and the RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emissions.

Deep Reinforcement Learning for Energy Efficiency Maximization in Cache-Enabled Cell-Free Massive MIMO Networks: Single- and Multi-Agent Approaches

Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging beyond fifth-generation (5 G) technology that improves energy efficiency (EE) and removes cell structure limitation by using multiple access points (APs). This study investigates the EE maximization problem. Forming proper cooperation clusters is crucial when optimizing EE, and it is often done by selecting AP–user pairs with good channel quality or aligning AP cache contents with user requests. However, the result can be suboptimal if we determine the clusters based solely on either aspect. This motivates our joint design of user association and content caching. Without knowing the user content preferences in advance, two deep reinforcement learning (DRL) approaches, i.e., single-agent reinforcement learning (SARL) and multi-agent reinforcement learning (MARL), are proposed for different scenarios. The SARL approach operates in a centralized manner which has lower computational requirements on edge devices. The MARL approach requires more computation resources at the edge devices but enables parallel computing to reduce the computation time and therefore scales better than the SARL approach. The numerical analysis shows that the proposed approaches outperformed benchmark algorithms in terms of network EE in a small network. In a large network, the MARL yielded the best EE performance and its computation time was reduced significantly by parallel computing.

Communication-efficient ADMM-based distributed algorithms for sparse training

In large-scale distributed machine learning (DML), the synchronization efficiency of the distributed algorithm becomes a critical factor that affects the training time of machine learning models as the computing scale increases. To address this challenge, we propose a novel algorithm called Grouped Sparse AllReduce based on the 2D-Torus topology (2D-TGSA), which enables constant transmission traffic that does not change with the number of workers. Our experimental results demonstrate that 2D-TGSA outperforms several benchmark algorithms in terms of synchronization efficiency. Moreover, we integrate the general form consistent ADMM with 2D-TGSA to develop a distributed algorithm (2D-TGSA-ADMM) that exhibits excellent scalability and can effectively handle large-scale distributed optimization problems. Furthermore, we enhance 2D-TGSA-ADMM by adopting the resilient adaptive penalty parameter approach, resulting in a new algorithm called 2D-TGSA-TPADMM. Our experiments on training the logistic regression model with ℓ1-norm on the Tianhe-2 supercomputing platform demonstrate that our proposed algorithm can significantly reduce the synchronization time and training time compared to state-of-the-art methods.

Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing

In multi-access edge computing (MEC) networks, parallelized service function chains (P-SFCs) can provide low-delay network services for mobile users by deploying virtualized network functions (VNFs) to process user requests in parallel. These VNFs are unreliable due to software faults and server failures. A practical way to address this is to deploy idle backup VNFs (BVNFs) near these active VNFs and activate them when active VNFs fail. However, deploying BVNFs preempts server resources and decreases the number of accepted user requests. Thus, this paper proposes a reliability enhancement approach that uses BVNFs satisfying the delay requirement as active VNFs to form P-SFCs, which contributes to the delay reduction and reliability enhancement. Since the resource capacities of edge servers can only deploy a certain number of P-SFCs and BVNFs, establishing how to deploy the minimum number of P-SFCs and BVNFs to satisfy the delay and reliability requirements of mobile users and maximize the number of accepted user requests is a challenging problem. In this paper, we first model the dynamics of delay and reliability caused by VNF parallelization and BVNFs deployment, then formulate the joint deployment problem of P-SFCs and BVNFs. Next, we design an approximation algorithm to deploy critical VNFs and BVNFs on a target edge server and schedule the data traffic of user requests processed by P-SFCs. Experimental results based on real-world datasets show that our proposed algorithm outperforms two benchmark algorithms in terms of throughput, delay, reliability, and resource utilization.

DTN-Assisted Dynamic Cooperative Slicing for Delay-Sensitive Service in MEC-Enabled IoT via Deep Deterministic Policy Gradient With Variable Action

Network Slicing (NS) provides customized services to users of the Internet of Things (IoT) by creating logical virtual networks, and NS combined with Multi-access Edge Computing (MEC) can significantly minimize the latency for delay-sensitive service. Therefore, it is important to research how to employ NS to achieve low latency for delay-sensitive service in MEC-enabled IoT. In this paper, we propose a paradigm of dynamic cooperative slicing based on Digital Twin Network (DTN) to achieve low latency for delay-sensitive service. Specifically, we first build a DTN for the MEC-enabled IoT, and build basic models and function models including prediction and decision-making in DTN. Then we realize dynamic cooperative slicing through the built basic models and function models. Secondly, with the assistance of the ubiquitous computing resources in MEC-enabled IoT based on DTN, we construct joint optimization problem of communication resources, computing resources, and collaboration proportion with the objective of ensuring low delay of delay-sensitive service while maximizing the long-term utility of operators. Thirdly, considering that the different MEC servers participating in the cooperation in each time slot lead to different action spaces in different time slots, we propose a Deep Deterministic Policy Gradient algorithm with Variable Action space, called VADDPG, which draws on the idea of action masking and introduces the action adjustor to realize the hard control of action space. Finally, a large number of simulations demonstrate that the proposed algorithm outperforms the benchmark algorithms in terms of both the long-term utility of operators and the delay obtained by slicing.

Act Before Another Is Aware: Safeguarding Backscatter Systems With Covert Communications

This letter investigates and analyzes covert communications to safeguard backscatter systems, i.e., how to hide backscattering transmission for privacy and security, which is rarely a concern yet. Specifically, we consider a backscatter communication system in the presence of an eavesdropper (Eve), in which a passive tag supported by the power source (Alice) attempts to communicate reliably while simultaneously guaranteeing covertness to Eve. Furthermore, artificial noise (AN) is also injected from Alice to mislead Eve for security purposely. Our objective is to maximize the transmission rate via joint power control, AN injection, and reflection coefficient (RC) adjustment while satisfying the physical constraints on the total power budget, the circuit energy consumption, the minimum transmission rate, and the covertness requirement. To deal with this intractable non-convex problem, we reformulate the problem by utilizing the Kullback-Leibler divergence (KLD) and Lambert W function. Subsequently, an alternative iterative algorithm is designed to solve it. Besides, we derive the closed forms for RC and transmit power for more insight. Simulation results show that the proposed algorithm outperforms the benchmark algorithms in terms of both security and effectiveness.

Harris Hawks Optimization-Based Clustering Algorithm for Vehicular Ad-Hoc Networks

Vehicular ad-hoc network (VANET) is highly dynamic due to the high speed and sparse distribution of vehicles on the road. This creates major challenges (e.g., network fragmentation, packet routing) for the researchers to enable robust, reliable, and scalable communication, especially in a highly dense network. Clustering in VANET is one of the remedies to address the scalability issue. However, it is observed in the literature, that existing clustering techniques produce a high number of clusters for the vehicular environment. Consequently, it increases the consumption of scarce resources in a wireless network. Furthermore, it also increases the communication overhead as well as the number of hops for data routing. As a result communication latency also increases and the reliability of communication protocol decreases. So it is highly desirable to find out the optimal clusters for a given vehicular environment. As finding optimal clusters is a multi-objective combinatorial optimization problem, therefore by employing nature-inspired meta-heuristic algorithms we can optimize the multi-objective problem. To this end, we proposed a novel clustering algorithm based on the Harris Hawks Optimization (HHO) algorithm for VANET (HHOCNET). HHO algorithm is a nature-inspired meta-heuristic algorithm inspired by the foraging maneuver of hawks called surprise pounce. The proposed framework imitates the cooperative foraging maneuver of hawks (i.e., surprise pounce for creating optimized vehicular clusters). The stochastic operators of the HHO algorithm and proper maintenance of the equilibrium state between the operations of exploration and exploitation enable the proposed algorithm to escape from the local optima and provide a globally optimal solution (i.e., the optimal number of vehicular clusters). Simulations are performed in MATLAB and the results are compared with the state-of-art schemes (i.e., Gray Wolf optimization-based clustering algorithm (GWOCNET), Multi-objective Particle Swarm Optimization (MO, PSO), and Comprehensive Learning Particle Swarm Optimization (CLPSO)) using different performance metrics. The results demonstrate that the proposed approach is an effective approach for clustering in VANET and outer performs the other benchmark algorithms in terms of optimizing the multi-objective clustering problem. HHOCNET algorithm selects 36.04% of nodes as cluster heads while the existing state-of-the-art schemes are providing 50.42%, 56.7%, and 60.89% for GWOCNET, CLPSO, and Multi-objective Particle Swarm Optimization (MOPSO). The proposed HHOCNET algorithm enhances the performance of the vehicular network by up to 15%. Consequently, it increases network efficiency by reducing the consumption of the required wireless resources. It also reduces the number of hops for packet routing. Hence it achieves a minimum end-to-end communication latency.

New Bridge to Cloud: An Ultra-Dense LEO Assisted Green Computation Offloading Approach

Mobile edge computing and cloud computing have emerged as effective technologies to alleviate the increasing computational workload of mobile devices. As a promising enabling 6G technology, the ultra-dense (UD) low earth orbit (LEO) satellite network with low communication latency and high throughput is considered a new bridge for cloud computation offloading. In this paper, we investigate energy-efficient cloud and edge computing in UD-LEO-assisted terrestrial-satellite networks. An optimization problem aiming at minimizing the energy consumption of the computation tasks is formulated. The optimization problem is a mixed-integer non-linear programming problem. To solve this problem, we decompose it into two subproblems, i.e., a joint user association and task scheduling subproblem, and an adaptive computation resource allocation subproblem. For the first subproblem, we model the input of a forward neural network (NN) as the large-scale information (i.e., channel gain and task arrival rates) and obtain the optimal solution by transforming the direct output of the NN. For the second subproblem, we introduce a successive convex approximation method to optimize it iteratively. The simulation results show that our proposed user association and task scheduling strategy outperforms two benchmark algorithms in terms of energy consumption under a strict delay bound and high user density.

Online map-matching assisted by object-based classification of driving scenario

Different types of roads in complex road networks may run side-by-side or across in 2D or 3D spaces, which causes mismatched segments using existing online map-matching algorithms. A driving scenario that represents the driving environment can inform map-matching algorithms. Images from vehicle cameras contain extensive information about driving scenarios, such as surrounding key objects. This research utilized vehicle images and developed an object-based method to classify driving scenarios (Object-Based Driving-Scenario Classification: OBDSC) to calculate the probabilities of the current image in predefined types of driving scenarios. We implemented an online map-matching algorithm with the OBDSC method (OMM-OBDSC) to obtain optimal matching segments. The algorithm was tested on nine trajectories and OpenStreetMap data in Shanghai and compared with five benchmark algorithms in terms of the match rate, recall and accuracy. The OBDSC method is also applied to the benchmark algorithms to verify the effectiveness of map matching. The results show that our algorithm outperforms the benchmark algorithms with both the original interval and downsampled intervals (96.6%, 96.5%, 93.7% on average with 1–20 s intervals for the three metrics, respectively). The average match rate has improved by 8.9% for all benchmark algorithms after the addition of the OBDSC method.

A DRL-based online VM scheduler for cost optimization in cloud brokers.

The virtual machine (VM) scheduling problem in cloud brokers that support cloud bursting is fraught with uncertainty due to the on-demand nature of Infrastructure as a Service (IaaS) VMs. Until a VM request is received, the scheduler does not know in advance when it will arrive or what configurations it demands. Even when a VM request is received, the scheduler does not know when the VM's lifecycle expires. Existing studies begin to use deep reinforcement learning (DRL) to solve such scheduling problems. However, they do not address how to guarantee the QoS of user requests. In this paper, we investigate a cost optimization problem for online VM scheduling in cloud brokers for cloud bursting to minimize the cost spent on public clouds while satisfying specified QoS restrictions. We propose DeepBS, a DRL-based online VM scheduler in a cloud broker which learns from experience to adaptively improve scheduling strategies in environments with non-smooth and uncertain user requests. We evaluate the performance of DeepBS under two request arrival patterns which are respectively based on Google and Alibaba cluster traces, and the experiments show that DeepBS has a significant advantage over other benchmark algorithms in terms of cost optimization.

2-D fragmentation-aware RMSA based on the relative flexibility of advance reservation requests in elastic optical networks

With the advantages of fine granularity spectrum allocation (SA) and large capacity, elastic optical networks (EONs) are considered as the most promising development direction of optical network in the future. The emergence of two-dimensional (2-D) fragmentation during dynamic SA of EONs not only reduces the availability of resources but also increases the blocking probability of requests. In addition, the advance reservation (AR) request has the characteristics of resource reservation and time flexibility, which further complicates the fragmentation issue. To solve the above issues, in this paper, a fragmentation-aware routing, modulation and spectrum assignment (RMSA) algorithm for AR requests is proposed. First, delayed resource allocation is developed for AR requests. Second, a resource allocation sorting strategy based on the relative flexibility of requests is proposed. The strategy makes full use of the time characteristics of AR request to determine the allocation order of requests. In addition, from the perspective of the impact on adjacent resources, a 2-D path resource model is introduced to evaluate the impact of 2-D fragmentation from the perspective of resource matching degree and resource connectivity. Moreover, simulation results show that the proposed algorithm is better than the benchmark algorithm in terms of blocking probability, resource utilization and fragmentation rate.

DDCM: a decentralized density clustering and its results gathering approach

The use of distributed clustering is an important method of solving large-scale data mining problems. There are still some problems associated with distributed clustering, such as a performance bottleneck on the master node and network congestion caused by global broadcasting. This paper proposes a decentralized clustering method based on density clustering and the content-addressable network technique. It can form a cluster with excellent scalability and load balancing capabilities based on several surrounding nodes. In addition, a method is presented for optimizing the way clustering results are gathered in different application scenarios. Based on our extensive experiments, the proposed approach performs three times better than benchmark algorithms in terms of efficiency and has a stable expanding ratio of about 0.6 for large-scale data sets.

FiFo: Fishbone Forwarding in Massive IoT Networks

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges, such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. Our <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiFo</monospace> method completes two tasks: 1) it clusters devices based on the unweighed pair group method with the arithmetic average and 2) it creates the main axis and subaxes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiFo</monospace> by using a real-world data set. Through intensive and comprehensive simulations, we show that the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiFo</monospace> method outperforms benchmark algorithms in terms of the forwarding efficiency.

Combination of Throughput-optimal Scheduling and Network Utility Maximization in NOMA Systems with Flow-Level Dynamics

In wireless networks, the network queue stability (throughput-optimality) and the network utility maximization are two crucial tasks for network operators. Besides, new techniques of mobile communications, e.g. the non-orthogonal multiple access (NOMA), have emerged endlessly to meet coming challenges. In this paper, we for the first time, investigate a downlink NOMA system with flow-level dynamics, where long-lived flows and short-lived flows exist simultaneously, and formulate an optimization problem of user selection and power allocation. We aim to maximize the network utility at each time slot and simultaneously guarantee the throughput-optimality, under the quality of service (QoS) constraints. Considering the practicability of algorithm, the suboptimal algorithm containing two stages, is proposed. And we analyze the complexity of proposed algorithm. In the performance evaluation, the benchmark algorithm is provided for comparison. And we take the energy efficiency (EE) as an example of the network utility and define the scheduling delay (SD). Simulation results show that the suboptimal algorithm is throughput-optimal, and understandably, its performances could be better than those in the benchmark algorithm in terms of EE and SD, as long as the appropriate parameter is set.

Joint Communication and Action Learning in Multi-Target Tracking of UAV Swarms with Deep Reinforcement Learning

Communication is the cornerstone of UAV swarms to transmit information and achieve cooperation. However, artificially designed communication protocols usually rely on prior expert knowledge and lack flexibility and adaptability, which may limit the communication ability between UAVs and is not conducive to swarm cooperation. This paper adopts a new data-driven approach to study how reinforcement learning can be utilized to jointly learn the cooperative communication and action policies for UAV swarms. Firstly, the communication policy of a UAV is defined, so that the UAV can autonomously decide the content of the message sent out according to its real-time status. Secondly, neural networks are designed to approximate the communication and action policies of the UAV, and their policy gradient optimization procedures are deduced, respectively. Then, a reinforcement learning algorithm is proposed to jointly learn the communication and action policies of UAV swarms. Numerical simulation results verify that the policies learned by the proposed algorithm are superior to the existing benchmark algorithms in terms of multi-target tracking performance, scalability in different scenarios, and robustness under communication failures.