Energy-delay Tradeoff Research Articles

Energy-Delay Tradeoff in Helper-Assisted NOMA-MEC Systems: A Four-Sided Matching Algorithm

Energy-Delay Tradeoff in Helper-Assisted NOMA-MEC Systems: A Four-Sided Matching Algorithm

DRL-based dependent task offloading with delay-energy tradeoff in medical image edge computing

AbstractTask offloading solves the problem that the computing resources of terminal devices in hospitals are limited by offloading massive radiomics-based medical image diagnosis model (RIDM) tasks to edge servers (ESs). However, sequential offloading decision-making is NP-hard. Representing the dependencies of tasks and developing collaborative computing between ESs have become challenges. In addition, model-free deep reinforcement learning (DRL) has poor sample efficiency and brittleness to hyperparameters. To address these challenges, we propose a distributed collaborative dependent task offloading strategy based on DRL (DCDO-DRL). The objective is to maximize the utility of RIDM tasks, which is a weighted sum of the delay and energy consumption generated by execution. The dependencies of the RIDM task are modeled as a directed acyclic graph (DAG). The sequence prediction of the S2S neural network is adopted to represent the offloading decision process within the DAG. Next, a distributed collaborative processing algorithm is designed on the edge layer to further improve run efficiency. Finally, the DCDO-DRL strategy follows the discrete soft actor-critic method to improve the robustness of the S2S neural network. The numerical results prove the convergence and statistical superiority of the DCDO-DRL strategy. Compared with other algorithms, the DCDO-DRL strategy improves the execution utility of the RIDM task by at least 23.07, 12.77, and 8.51% in the three scenarios.

On the energy-delay trade-off in CCN caching strategy: a multi-objective optimization problem

On the energy-delay trade-off in CCN caching strategy: a multi-objective optimization problem

Energy-Delay Tradeoff for Dynamic Trajectory Planning in Priority-Oriented UAV-Aided IoT Networks

Unmanned aerial vehicles (UAVs) play a crucial role in emergency-oriented applications. However, in UAV-aided Internet of Things (IoT) networks, the sensor nodes (SNs) would be mobile which poses a big challenge for trajectory planning of the UAV. In this paper, we investigate priority-oriented UAV-aided time-sensitive data collection problems in an IoT network with movable SNs. By defining different levels of delay sensitivities for each SN, we jointly minimize the energy consumed by a UAV and the average delay of different SNs through optimizing the trajectory of the UAV. The problem is formulated as a multi-objective optimization problem (MOP). To solve the formulated problem, we first transform the MOP into a single-objective optimization problem based on the weighted sum method. Then, we propose a novel autofocusing heuristic trajectory planning algorithm based on reinforcement learning (AHTP-RL) which can be operated in an online manner. The proposed algorithm can well extract the network dynamic topology and the delay-priority of SN through an attention mechanism, hence can structure the UAV’s trajectory efficiently. Extensive simulations results demonstrate that the proposed online AHTP-RL algorithm can achieve a superior balance between the communication delay and energy consumption for both low and high SN mobilities.

Lyapunov-Guided Delay-Aware Energy Efficient Offloading in IIoT-MEC Systems

With the increasingly humanized and intelligent operation of Industrial Internet of Things (IIoT) systems in Industry 5.0, delay-sensitive and compute-intensive (DSCI) devices have proliferated, and their demand for low latency and low power consumption has become more and more eager. In order to extend the battery life and improve the quality of user experience, we can offload DSCI-type workloads to mobile edge computing (MEC) servers for processing. However, offloading massive amounts of tasks will incur higher energy consumption, which is a severe test for the limited battery capacity of devices. In addition, the delay caused by frequent communication between IIoT devices and MEC cannot be ignored. In this article, we first formulate the stochastic computation offloading problem to minimize long-term energy consumption. Then, we construct a virtual queue using perturbed Lyapunov optimization techniques to transform the problem of guaranteeing task deadlines into a stable control problem for the virtual queue. Based on this, a novel delay-aware energy-efficient (DAEE) online offloading algorithm is proposed, which can adaptively offload more tasks when the network quality is good. Meanwhile, it delays transmission in the case of poor connectivity but ensures that the deadline is not violated. Moreover, we theoretically demonstrated that DAEE can enable the system to achieve an energy-delay tradeoff, and analyzed the feasibility of constructing virtual queues to assist the actual queue offloading tasks. Finally, simulation results show that DAEE performs well in minimizing energy consumption and maintaining low latency, especially for DSCI-type tasks.

Delay-Tolerant UAV-Assisted Communication: Online Trajectory Design and User Association

UAV-assisted communication is a promising paradigm to enhance the performance of wireless networks. In this paper, we consider the network randomness in the delay-tolerant UAV-assisted communication systems. We aim to optimize both the UAV trajectory and user association in an online manner to achieve a finite queueing delay at the users' buffers with the minimum energy consumption of the UAVs. We first formulate our problem as a stochastic dynamic programming problem that integrates the future expected energy consumption and delay into its objective function and constraints, respectively. To deal with the inaccurate knowledge of future user demand and mobility, we apply the Lyapunov optimization technique and convert it into a series of online joint UAV trajectory and user association optimization problems. However, each online problem is a non-convex non-linear integer programming problem, which is very challenging to solve in general. As such, we first eliminate the coupling relationship of user association variables in each online problem. By exploiting the special graph structure of the problem, we can decompose the joint problem into inner optimization and outer optimization problems. Then, we propose the online UAV mobility and user association selection (UMUAS) scheme, which includes two steps to solve the inner and outer problems. Simulation results show that our online UMUAS scheme achieves the near-optimal performance of the energy-delay tradeoff, which also has a 44.2% improvement over the greedy scheme and a 51.3% improvement over the interference-free scheme. Moreover, it achieves the highest energy efficiency among the three benchmark schemes.

Joint multiple resource allocation for offloading cost minimization in IRS-assisted MEC networks with NOMA

Effective resource allocation can exploit the advantage of intelligent reflective surface (IRS) assisted mobile edge computing (MEC) fully. However, it is challenging to balance the limited energy of MTs and the strict delay requirement of their tasks. In this paper, in order to tackle the challenge, we jointly optimize the offloading delay and energy consumption of mobile terminals (MTs) to realize the delay-energy tradeoff in an IRS-assisted MEC network, in which non-orthogonal multiple access (NOMA) and multiantenna are applied to improve spectral efficiency. To achieve the optimal delay-energy tradeoff, an offloading cost minimization model is proposed, in which the edge computing resource allocation, signal detecting vector, uplink transmission power, and IRS phase shift coefficient are needed to be jointly optimized. The optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension variables. To solve the intractable problem, we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable types. The computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource allocation. Further, the wireless transmission subproblem is solved iteratively through decoupling the residual variables. In each iteration, the closed-form solution of residual variables is obtained through different successive convex approximation (SCA) methods. We verify the proposed algorithm can converge to an optimum with polynomial complexity. Simulation results indicate the proposed method achieves average saved costs of 65.64%, 11.24%, and 9.49% over three benchmark methods respectively.

Hybrid Full Adders: Optimized Design, Critical Review and Comparison in the Energy-Delay Space

In this paper, we design and compare seven meaningful hybrid one-bit full adders topologies that are optimized in terms of energy-delay trade-offs to operate in multibit ripple carry adders. The goal is to provide the designer with a simple and powerful approach for choosing the best topology for a given power budget, speed performance, or any combination of both. The design and comparison deal with 4-bit and 8-bit ripple carry adders and exploit the derivation of the energy-efficient curves in the energy-delay space. To do so, first we define the procedures to obtain energy consumption and propagation delay by simulating a ripple carry adder designed at a transistor level. Then, we introduce a design methodology to optimize a ripple carry adder by minimizing some significant figures-of-merit in terms of energy-delay trade-offs. The comparison of the energy-efficient curves allows us to make a simple and effective comparison as well as to identify the best one-bit full adder topologies.

Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network

Mobile-edge computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation intensive and delay-critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering nonstationary computation demands, user mobility, and wireless fading channels. This article aims to study the tradeoff between energy efficiency (EE) and service delay for multiuser multiserver MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on the Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[O(1/V), O(V)]$ </tex-math></inline-formula> tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.

Energy and Delay Guaranteed Joint Beam and User Scheduling Policy in 5G CoMP Networks

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations.

Energy–Delay Tradeoff in Adaptive Cooperative Caching for Energy-Harvesting Ultradense Networks

Edge caching in collaborative edge computing (CEC) is a resource-friendly technique to improve energy efficiency and alleviate backhaul link congestion. Caching diverse contents based on the social features among users at energy-harvesting-powered (EH-powered) small base stations can further save on-grid energy, but it may lead to a longer delay to mobile users (MUs). In this article, we focus on an energy–delay tradeoff (EDT) problem in CEC-assisted and EH-powered ultradense networks and propose an EDT-oriented adaptive cooperative caching (EDT-ACC) scheme. We regard delay and energy as two types of cost and introduce a weighted cost function to transform the EDT problem into a cost minimization problem. An alternating optimization based on an improved quantum genetic algorithm (AO-IQGA) is proposed to solve the cost minimization problem. In AO-IQGA, the alternating optimization is utilized to divide the cost minimization problem into two subproblems (adaptive tuning weight subproblem and caching decision subproblem). We improve the quantum genetic algorithm in terms of repairing unfeasible solutions and adaptively updating quantum genes. Numerical results demonstrate the efficiency of the proposed AO-IQGA and illustrate the fundamental tradeoff between delay and energy consumption under different parameters, such as content popularity, storage size, and battery capacity.

Analysis and Comparison in the Energy-Delay Space of Nanometer CMOS One-Bit Full-Adders

In this paper we analyze, design and compare six significant topologies of one-bit full adders in terms of their Energy-Efficient Curves in the Energy-Delay Space.We define the simulation strategies that are adopted to make a fair comparison even among cells with very different characteristics. Each topology is designed through a methodology which, thanks to the adoption of a circuit optimizer, allows to design the circuit under different energy-delay trade-offs and to derive the Energy-Efficient Curves. The comparison of the topologies is made using a 28 nm CMOS technology in terms of normalized Energy-Efficient Curves. In particular, plotting all these Energy-Efficient Curves in a single graph makes the comparison very effective and allows the designer to choose the best topology or discard the worst ones, at a glance.

Clustered mobile data collection in WSNs: an energy-delay trade-off

Wireless sensor networks enable monitoring remote areas with limited human intervention. However, the network connectivity between sensor nodes and the base station (BS) may not be always possible due to the limited transmission range of the nodes. In such a case, one or more mobile data collectors (MDCs) can be employed to visit nodes for data collection. If multiple MDCs are available, it is desirable to minimize the energy cost of mobility while distributing the cost among the MDCs in a fair manner. Despite availability of various clustering algorithms, there is no single fits all clustering solution when different requirements and performance metrics are considered. Depending on the available wireless communication technology, the MDCs may or may not be required to visit the BS to forward the collected data. This paper considers both cases and suggests clustering algorithms for various performance metrics including the energy consumption and the maximum travel time.

Energy-delay tradeoff for virtual machine placement in virtualized multi-access edge computing: a two-sided matching approach

By decoupling network functions from the underlying physical machines (PMs) at the edge of the networks, the virtualized multi-access edge computing (MEC) enables deployment of new network services and elastic network scaling to reduce the maintenance costs in a more flexible, scalable and cost-effective manner. Although there are appealing performance gains to be achieved, the placement of virtual machines (VMs) on top of the sharing PMs to support computation-intensive applications for smart mobile devices becomes a major challenge, especially for an increasing network scale. In this paper, we attempt to deal with the VM placement problem in virtualized MEC system, which is targeted for finding a performance balance between energy consumption and computing/offloading delay. To capture such a tradeoff for VM placement, we formulate a weighted sum based cost minimization problem as a pure 0–1 integer linear programming problem, which is NP-complete and very difficult to solve with lower complexity. Based on the one-to-one mapping relation constraint, the VM placement problem is then converted into a many-to-many two-sided matching problem between the VM instances and the PMs. Motivated by the student project allocation problem, we develop an extended two-sided matching algorithm with lower computational complexity for solving the matching problem. Simulation results are presented to show the effectiveness of our proposed algorithm, and the normalization factor is of great significance to obtain the lower total cost.

The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)

The Internet of Things (IoT) tsunami, public embracement, and the ubiquitous adoption of smart devices are affecting virtually every industry, directly or indirectly. The success of the current and future landscape of IoT and connected devices requires service provision characterized by scalability, ubiquity, reliability, and high-performance, among others. In order to achieve this attribution, the integration of IoT and Cloud Computing (CC), known as cloud IoT, has emerged as a new paradigm providing advanced services specific to aggregating, storing, and processing data generated by IoT. While the convergence of IoT and Cloud brings opportunities, it suffers from specific limitations such as bandwidth, latency, and connectivity. The increasing need for supporting interaction between cloud and IoT led to Edge and Fog Computing (FC) in which computing and storage resources are located not only in the cloud but also at the edges near the source of data. The hierarchical and collaborative edge–fog–cloud architecture brings tremendous benefits as it enables us to distribute the intelligence and computation – including Artificial Intelligence (AI), Machine Learning (ML), and big data analytics – to achieve an optimal solution while satisfying the given constraints e.g., delay-energy tradeoff. Due to the hierarchical, cross-layer, and distributed nature of this model, achieving an osmotic and effective convergence of IoT, edge, fog, and cloud computing requires overcoming many challenges with respect to design and implementation, as well as deployment and evaluation. This paper provides a comprehensive insight into the edge-fog-cloud computing paradigm by providing a blend of discussions on all important aspects of the underlying technologies to offer opportunities for more holistic studies and to accelerate knowledge acquisition. To gain a deep understanding of edge–fog–cloud, we will begin this paper by providing an in-depth tutorial and presenting the main requirements, state-of-the-art reference architectures, building blocks, components, protocols, applications, and other similar computing paradigms, including their similarities and differences. Following this, a holistic reference architecture for edge–fog–cloud IoT is presented and the major corresponding design and deployment considerations (e.g., service models, infrastructure design, provisioning, resource allocation, offloading, service migration, performance evaluation, and security concerns) are discussed. Next, we will take a look at the role of privacy-preserving, distributed, and collaborative analytics as well as the interaction between edge, fog, and cloud computing. Finally, we will overview the main challenges in the field of edge–fog–cloud computing that need to be tackled to realize the full potential of IoT.

Security and Energy-aware Collaborative Task Offloading in D2D communication

Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment.

Energy-Delay Tradeoff Scheme for NOMA-Based D2D Groups With WPCNs

An integration of nonorthogonal multiple access (NOMA) in device-to-device (D2D) mobile groups (DMGs) generates a large amount of energy and delay due to sharing the same subcarrier. In this article, we investigate a distributed energy-efficient and delay tradeoff scheme for NOMA-based DMGs in wireless powered communication networks. The stochastic optimization problem is formulated to maximize the energy efficiency and minimize the delay. Many-to-one matching-based subcarrier allocation, weighted sum method, and Lyapunov optimization scheme are used to investigate the possible solution. Further, to obtain the closed-form expression of power and to optimize the time for wireless powered communication networks, we use Lagrangian function. Simulation results show that the proposed scheme achieves 2.6% and 15.78% superior performance than the existing NOMA and orthogonal frequency division multiple access (OFDMA) schemes under the condition of constant circuit power consumption.

Relay-Assisted Multicast With Markov Mobility

In this paper, we investigate the capacity and delay tradeoff under multicast scheme in the MANET, based on a general Markovian mobility model. To reduce the delay of the network, we propose a relay-assisted multicast scheme. Specifically, a two-hop relay algorithm is developed, in which Lyapunov drift is utilized to derive the average packet delay. In addition, we utilize the cache in relay nodes and propose the two-hop relay algorithm with redundancy. Theoretical analysis indicates that the network delay is significantly decreased, whereas the capacity remains the same. To guarantee the fairness and efficiency of the network, a two-hop relay selection algorithm with redundancy is proposed to decide which packet to serve in a queue. Moreover, the minimum energy function is applied to characterize the energy consumption of each node. We accordingly derive the accurate minimum energy function under the proposed relay-assisted multicast scheme. Furthermore, we design an efficient minimum energy algorithm, which pushes the actual energy consumption arbitrarily close to the minimum energy function at the cost of increasing delay. Theoretical results show that the optimal energy-delay tradeoff is achieved in our proposed algorithm. Numerous experiments are carried out to evaluate the performance of our proposed algorithms, where the experimental results well conform our theoretical findings.

Cooperative Computation Offloading in FiWi Enhanced 4G HetNets Using Self-Organizing MEC

Multi-access edge computing (MEC) is an emerging paradigm to meet the rapidly growing computation demands of mobile applications. This paper investigates the performance gains of cooperative computation offloading for MEC enabled FiWi enhanced HetNets with capacity-limited backhaul links. After presenting the envisioned two-tier MEC architecture for a FiWi based networking infrastructure, we propose a simple but efficient offloading strategy, which relies on the flexible trilateral cooperation between end-device, edge servers, and the remote cloud. We then present an analytical framework to estimate the average response time and energy consumption of mobile users for various offloading scenarios with different wireless access modes (i.e., WiFi and 4G LTE-A). The presented analysis flexibly allows for incorporating both offloaded and conventional human-to-human (H2H) traffic of mobile users as well as fixed (wired) subscribers. Finally, we present our self-organization based mechanism, which enables mobile users to make suitable energy-delay trade-offs by jointly minimizing the average task execution time and energy consumption, using only their local information. The obtained results demonstrate the feasibility of the proposed cooperative self-organizing offloading strategy and its superior performance over schemes with MEC- or cloud-only offloading strategies.

Energy-Efficient Decision Making for Mobile Cloud Offloading

Mobile cloud offloading migrates heavy computation from mobile devices to remote cloud resources or nearby cloudlets. It is a promising method to alleviate the struggle between resource-constrained mobile devices and resource-hungry mobile applications. Caused by frequently changing location mobile users often see dynamically changing network conditions which have a great impact on the perceived application performance. Therefore, making high-quality offloading decisions at run time is difficult in mobile environments. To balance the energy-delay tradeoff based on different offloading-decision criteria (e.g., minimum response time or energy consumption), an energy-efficient offloading-decision algorithm based on Lyapunov optimization is proposed. The algorithm determines when to run the application locally, when to forward it directly for remote execution to a cloud infrastructure and when to delegate it via a nearby cloudlet to the cloud. The algorithm is able to minimize the average energy consumption on the mobile device while ensuring that the average response time satisfies a given time constraint. Moreover, compared to local and remote execution, the Lyapunov-based algorithm can significantly reduce the energy consumption while only sacrificing a small portion of response time. Furthermore, it optimizes energy better and has less computational complexity than the Lagrange Relaxation based Aggregated Cost (LARAC-based) algorithm.