Inadequate resource coordination and control can result in poor quality of experience (QoE) for user devices in heterogeneous edge-enabled cyber-physical systems. Unfortunately, in a cooperative edge network, existing studies have rarely jointly optimized communication, computing resources, and batch size for QoE guarantee when controlling task offloading. To this end, we investigate the problem of harnessing bandwidth, computation, and batch size for fair quality of experience (HARBOR) in a practical collaborative edge-AI environment, where UEs have different accuracy requirements of inference services and edge devices possess different batch processing capabilities. Specifically, we introduce the task completion efficiency as the task-completion-time-to-deadline ratio to quantify individual QoE. Then, we formulate the problem HARBOR as a mixed integer nonlinear programming with constraints of accuracy, bandwidth, computation, task hard deadlines and so on. The objective is to minimize the maximum task completion efficiency among all tasks to achieve task-level fairness. After providing the NP-hardness proof for HARBOR, we then devise an efficient scheme named e-HARBOR with a competitive ratio guarantee, to solve the decoupled sub-problems of HARBOR with calibrated long short-term memory network for resource prediction. Both testbed and simulation experiments evidently demonstrate that the proposed scheme works efficiently and scales well compared to baselines.

Cooperative edge artificial intelligence (AI) has shown its advantages via edge-edge collaboration. By deploying deep neural network (DNN) inference service models at the edge server, the lifetime of user devices (UDs) can be prolonged through computation offloading. In practise, the service configuration delay or loading cost can potentially degrade the performance of cooperative edge-AI services. Although there have been recent studies on service caching and request routing having loading cost in mind, there exists performance gap between theory and practise, especially when UD applications have stringent deadlines, for example, running big data inference applications. This paper thus resolves the flaw of algorithm running time violates the task deadline using deep reinforcement learning. The original loading cost-aware joint cooperative edge-AI service deployment and computation offloading problem is reformulated with Markov decision process. The state, action spaces and the reward function have been well defined and the objective is to minimize the difference between the target network and evaluation network. Extensive simulation results demonstrate that compared with benchmark algorithms, the proposed algorithm can achieve more than 200 times performance gain on the algorithm running time, while obtains over 15% throughput enhancement than the benchmarks on various indices.

Edge artificial intelligence (Edge-AI) is emerging with the proliferation of both multi-access edge computing (MEC) and AI. Cooperative Edge-AI can not only increase the computing resource utilization ratio with edge-edge collaboration, but also improve the big data processing efficiency of mobile end devices through computation offloading to a group of edge servers. Existing paradigms for cooperative edge-AI applications are not tailored for heterogeneous types of applications, thus harming the quality of experience (QoE) of different application users or operators in the network. This paper thus fills the gap by firstly defining the fairness index as service completion ratio, and then formulating the max-min fairness problem subject to edge server’s storage, computation, deadline constraints and so on. The problem is proven to be NP-hard through reduction from a well-known NP-complete problem, the multi-knapsack problem. To tackle the dynamics of both computing resources and channel fading conditions, a deep reinforcement learning algorithm is invented on the basis of buffer replay and evaluation-target networks, to derive the joint service deployment and computation offloading strategy. Extensive experimental results demonstrate that the proposed scheme named as Envision is at least 17× faster than the existing ORA algorithm.

Cooperative edge computing has shown its advantage to expedite the computing speed and enhance resource utilization ratio when offering edge-AI services. Under such setting, existing works have studied the joint service deployment and request routing problem with cooperative edge servers, however, they have seldom considered the energy balance of edge servers, especially for those battery limited edge devices. To guarantee the edge-AI service quality and achieve a balanced energy consumption, this work thus addresses the joint service deployment and request offloading problem by optimizing the maximum energy consumption of an edge node in each small cell base station in the heterogeneous network. The problem is proven to be NP-hard with a randomized approximation solution. Experimental results demonstrate that the proposed algorithm RRMME can well guarantee the service quality and achieve energy balance. Compared to the designed benchmark algorithm without service quality guarantee, but has energy budget constraint, RRMME algorithm can significantly reduce the average energy consumption by about 28.7%, while has only a slightly service completion rate reduction of less than 8% averagely.

In vehicular ad-hoc networks, vehicle-to-vehicle fog computing (VFC) can not only alleviate the computing delay of inference tasks from vehicles, but also reduce the computational overheads of RSUs. Existing studies on cooperative vehicle task computation offloading assume that RSUs can obtain global computing capability information of vehicles and the service-providing vehicles are always willing to offer services, while overlooking the privacy and selfishness of vehicles. Motivated by contract theory, we propose a joint service caching and task offloading NP-hard problem for vehicular fog computing, aiming to maximize the minimum service completion rate and to offer incentives for both service vehicles and RSUs. By designing contract-based joint service caching and task offloading algorithms, vehicles are encouraged to provide fog computing resources while protecting privacy. Extensive simulation results show that the proposed greedy algorithm CGA can improve the minimum service completion rate by over 10.8% and 14.7%, given fixed number of edge servers, when compared to a benchmark algorithm without contract, and a designed contract-based algorithm that maximizes the total throughput. Moreover, the designed contract-based approximation algorithm CRA can achieve the performance that are close to the benchmark algorithm without contract.

The energy consumption of edge devices or nodes is critical to ensure a long lifetime of cooperative edge-AI service network, which has been somehow overlooked in the literature. Failure to accommodating the energy depletion can not only bring quality of service degradation of mobile terminal devices, but can also harm the connectivity of the multi-access edge computing network. This paper thus addresses the application service fairness problem under energy depletion constraints, to make the service fairness paradigm practical to suit for energy-limited edge nodes, e.g., the unmanned areal vehicles (UAVs), solar energy powered road side units (RSUs). The problem is formulated as a non-convex integer linear programming, which is NP-hard. Then a randomized rounding algorithm as well as a greedy algorithm are designed to maximize the minimum service type’s completion rate. Extensive simulation results have shown that compared to the algorithms without energy constraints, the proposed randomized rounding algorithm and greedy algorithm with energy constraints can reduce the average energy consumption by about 39.87% and 40.31% respectively, at the cost of a mild average system throughput degradation.

Abstract Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers.

In Internet of Vehicles (IoV), it is critical to collect sufficient data for model training, to support vehicular intelligent applications. However, the environment of IoV is highly dynamic due to the mobility of vehicles, making it challenging to efficiently allocate resources for data collection. Additionally, timely training of machine learning models with collected data is important for accurate representation in a constantly changing environment. This article aims to improve the performance of model training by collecting sufficient data from vehicles in IoV. A system throughput maximization problem is formulated under the limited bandwidth, storage, and computing resources, which is an NP-hard problem. To solve the problem, an iterative algorithm, namely, the iterative algorithm based on approaching minimum bandwidth (IAMB), is proposed to preferentially collect data from the vehicles with sufficient data and reliable communication quality. Besides, a genetic algorithm, namely, the genetic algorithm based on approaching minimum bandwidth (GAMB), is proposed to further improve the probability of superior individual by replacing operation. We also customize three greedy strategy-based algorithms as the baselines. Extensive experimental results show that our proposed algorithms outperform the baseline algorithms for all cases. Specifically, IAMB and GAMB can improve the throughput by up to 5% and 8%, respectively, compared with baseline algorithms. In addition, the customized genetic algorithm is also superior to the iterative algorithm on performance of system throughput. Moreover, the customized genetic algorithm is more stable than the proposed iterative algorithm in terms of system throughput for model training in the dynamic network environment.

Private car-based carpooling (PCC) has become an important transportation mode in our daily life. Unlike ride-hailing or taxi-based carpooling, PCC has two unique features that have yet to be fully explored: (i) A private-car driver has more bargaining space than a non-private car driver; (ii) There exists unfriendly congestion in private car-based carpooling if not handled well. Existing carpooling schemes are not tailored for PCC services with an oversimplified assumption that passengers pay detour fees and there is no guarantee on the passenger’s travel time. Consequently, such limitations not only harm the passenger’s carpooling incentive but also hurt the passenger’s quality of experience as well as the driver’s utility. We propose a novel framework for the double-sided preference-aware carpooling (D-SPAC) problem, after comprehensively addressing the above two unique features. We formulate the D-SPAC problem as a mixed-integer non-linear programming problem, which is proved to be NP-hard, to maximize the total utility of passengers while meeting the driver’s buyout asking price, traversal radius, passenger’s waiting time, budget and both sides’ detour length constraints. We design a coalitional double auction-based scheme that can better motivate both sides with guaranteed economic properties. We further design a deep reinforcement learning algorithm to cope with the position dynamics and the changing user requests. Extensive experimental results based on real-world data sets demonstrate the effectiveness of proposed algorithms over three benchmark algorithms.

In vehicular edge computing, efficient strategies for model deployment and task offloading offer tremendous potential to reduce response time for machine learning inference. However, existing works do not pay much attention to that there are shared structures among different types of inference tasks. This limits the improvement in response time. This paper aims to fill this gap by investigating a share-aware joint model deployment and task offloading problem for multi-task inference in vehicular edge computing. We formulate the problem with an objective to minimize the total response time of all inference requests, under constraints of per task response time, per roadside unit storage capacity, etc. We prove that the formulated problem is NP-hard. To solve the problem, a time period aware algorithm, called TPA, is proposed with guaranteed approximation ratio. In TPA, an iterative approach is designed to solve the problem of maximizing system throughput during a certain time period. Then, the certain time period approximates to the minimum time period of completing all requests. The algorithms are evaluated in the environment comprising two CPUs, two GPUs, state-of-the-art multi-task learning models and the dataset of Google cluster-usage trace. Simulation results derived from this environment show that, the proposed TPA outperforms the state-of-the-art methods for all cases, in terms of the total response time of all requests. For example, TPA can significantly reduce the total response time by at least$73.72\%$for different numbers of RSUs considered, compared with state-of-the-art methods.

The intrinsic nature of non-independent and identically distributed datasets on heterogeneous devices slows down the distributed model training process and reduces the training accuracy. To settle this problem, we propose a dataset reconstruction scheme to transform the data distribution of training device’s dataset into independent and identically distributed dataset via data exchange among trusted devices. For energy efficiency, we further consider power control for the devices. We then formulate an optimization problem, which is a mixed integer non-linear programming problem, to minimize the total energy consumption for each round of distributed training. Due to the NP-hardness and coupling property of the optimization problem, we decompose it into two subproblems for dataset reconstruction and power control, respectively. An approximation algorithm is designed to obtain a near-optimal auxiliary devices set for dataset reconstruction with minimum energy consumption, while meeting the variance constraint of the optimization problem. We prove that approximation algorithm has a worst-case approximation ratio of$1+\ln |\boldsymbol{\Omega }_{i}(t)|$, where$|\boldsymbol{\Omega }_{i}(t)|$is the required data samples for dataset reconstruction of each training device. For power control, we design a dynamic programming algorithm to further reduce the energy consumption. For comparison, we propose three benchmark schemes that adopt either one of the algorithms or neither. We also customize three baseline algorithms based on the state-of-the-arts to compare with our proposed algorithm. Numerical results show that, our proposed algorithm outperforms three benchmarks on the average energy consumption for one round for different cases. When varying the labels that each device owns, our proposed algorithm outperforms the other three baseline algorithms on training accuracy. Besides, when setting a target accuracy, our proposed algorithm always has the lowest energy consumption.

Abstract Parameter servers (PSs) placement is one of the most important factors for global model training on distributed deep learning. This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of storage capacity and the number of local PSs. Then, we provide the proof for the NP-hardness of the proposed problem. The whole training epochs are divided into two parts, i.e. the first epoch and the other epochs. For the first epoch, an approximation algorithm and a rounding algorithm are proposed in this paper, to solve the proposed problem. For the other epochs, an adjustment algorithm is proposed, by continuously adjusting the decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that the proposed approximation algorithm and rounding algorithm perform better than existing works for all cases, in terms of the training time of global model. Meanwhile, the training time of global model for the proposed approximation algorithm is very close to that for optimal solution generated by the brute-force approach for all cases. Besides, the integrated algorithm outperforms the existing works when the available storage capacity varies during the training.

Abstract Existing works on caching in multi-access edge computing focus on service caching and request routing. However, loading cost and execution time influenced by resource sharing have not been well exploited. To fill this gap, we investigate the joint optimization problem over deep neural network (DNN) model caching and DNN request routing with edge collaboration in edge-enabled wireless sensor networks. A problem is formulated, with the objective of maximizing throughput, under constraints of budget, accuracy and latency etc. The proof of NP-hardness for the formulated problem is provided. To solve the problem, an approximation algorithm based on randomized rounding is presented. In addition, the approximation ratio for the presented algorithm is proved to be $1/(1-\sqrt{4\ln S/\xi^\dagger})$, where $S$ is the number of edge servers and $\xi^\dagger$ is the objective value from linear relaxation. Extensive experiments demonstrate that the system throughput for the presented algorithm can be improved by 58.8% on average, compared with that of the baseline algorithm.

Abstract In wireless sensor networks (WSNs), wireless sensor nodes can be equipped with deep neural network accelerators to deal with the computation challenges in distributed training. However, the communication overhead of distributed training and the limited battery capacity of sensor nodes still impedes the broad deployment of distributed training applications. This article investigates the distributed training in WSNs by formulating an aggregation‐aware routing problem into a non‐linear integer programming problem. The objective of the formulated problem is to reduce the training time using data aggregation‐aware routing under the constraints of memory size and energy cost. Meanwhile, the NP‐Hardness of the formulated problem is proved in this article. Then, an intra‐cluster aggregation‐aware routing algorithm is proposed. The proposed algorithm accelerates the transmission of the data packet by integrating the K‐Means clustering and shortest path routing to choose the aggregators and the route paths. Extensive experiments demonstrate that the proposed algorithm outperforms two classical clustering routing algorithms UC‐LEACH and K‐Means by 29% and 37% in terms of average training time, and reducing the energy consumption by 21% and 15%, respectively.

In vehicular networks, task scheduling at the microarchitecture-level and network-level offers tremendous potential to improve the quality of computing services for deep neural network (DNN) inference. However, existing task scheduling works only focus on either one of the two levels, which results in inefficient utilization of computing resources. This paper aims to fill this gap by formulating a two-level scheduling problem for DNN inference tasks in a vehicular network, with an objective of minimizing total weighted sum of response time and energy consumption for all tasks under the following constraints: per task response time, per vehicle energy consumption, per vehicle storage capacity. We first formulate the problem and prove that it is NP-hard. A group transformation based algorithm, called GTA, is proposed. GTA makes scheduling decisions at the network-level using the group transformation based approach, and at the microarchitecture-level using a greedy strategy. In addition, an algorithm, denoted as DRL, is proposed to decrease total weighted sum of response time and energy consumption for all tasks. DRL trains two models with deep reinforcement learning to achieve two-level scheduling. The proposed algorithms are evaluated on a platform consisting of a desktop, Raspberry Pi, Eyeriss, OSM, SUMO, NS-3. Simulation results show that DRL outperforms the state-of-the-art methods for all cases, while the proposed GTA outperforms the state-of-the-art methods for most cases, in terms of total weighted sum of response time and energy consumption. Compared with four baseline algorithms, GTA and DRL reduce the total weighted sum of response time and energy consumption by 41.49% and 62.38%, on average respectively, for different numbers of tasks.

Mobile edge computing (MEC) is a promising edge technology to provide high bandwidth and low latency shared services and resources to mobile users. However, the MEC infrastructure raises major security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the mobile devices as they dynamically move from one subnetwork to another. In the proposed scheme, when a mobile device joins a subnetwork, it first generates lightweight key pairs for digital signature and communication, and broadcasts its public key to neighbouring peer users in the subnetwork blockchain. The blockchain miner in the subnetwork packs all the public key of mobile devices into a block that will be sent to other users in the subnetwork. This enables the mobile device to communicate with its peers in the subnetwork by encrypting the data with the public key stored in the blockchain. When the mobile device moves to another subnetwork in the tree network, all the mobile devices of the new subnetwork can quickly verify its identity by checking its record in the local or higher hierarchy subnetwork blockchain. Furthermore, when the mobile device leaves the subnetwork, it does not need to do anything and its records will remain in the blockchain which is an append-only database. Theoretical security analysis shows that the proposed scheme can defend against the 51 percent attack and malicious entities in the blockchain network utilizing Proof-of-Work consensus mechanism. Moreover, the backward and forward secrecy is also preserved. Experimental results demonstrate that the proposed scheme outperforms two baselines in terms of computation, communication and storage.

Most existing works on edge service caching and request routing fail to consider the influence of the service loading time. Meanwhile, the requests generated by end devices will change dynamically, which means that the caching strategy should adapt accordingly. In this paper, we investigate loading cost-aware joint model caching and request routing with cooperative edge computing, considering both the service loading time and the dynamic user requests. A system throughput maximization problem is formulated, which is proved to be NP-hard. Then, a randomized rounding-based online algorithm with M/(M − 2 ln N)-approximation ratio is proposed to solve it, where M and N are the numbers of end devices and deep neural network (DNN) models, respectively. Extensive experimental results demonstrate that our algorithm achieves more than 42.7% throughput gain than baseline algorithms.

Summary Blockchain has been applied in wide range of fields to guarantee security. However, it has been very challenging for blockchain to flourish in mobile environment with limited resources. Existing studies mainly assume that single mobile user can buy the whole resources from edge servers in mobile blockchain. This paper formulates the problem of maximizing the social welfare for computation offloading in mobile blockchain. A three‐stage auction scheme with approximation ratio of based on group‐buying mechanism is proposed to allocate edge server resources for mobile blockchain applications. In the first stage, the miners are divided into groups, and a Vickrey–Clarke–Groves based auction is proposed to determine the bid of each group for each edge server. In the second stage, a matching algorithm is proposed to match edge servers and Access Points for maximizing the profit of edge servers. In the third stage, the edge server resources are allocated to mobile users for mining base on the results in the above stages. We prove that our auction scheme guarantees truthfulness, individual rationality and budget balance. Simulation results show that, the social welfare of our scheme is improved by 33.78%, 21.84%, 19.69%, and 6.69% for 1000 miners, compared with the existing works.

Most of existing Multi-access edge computing (MEC) studies consider the remote cloud server as a special edge server, the opportunity of edge-cloud collaboration has not been well exploited. We propose a dependency-aware offloading scheme in MEC with edge-cloud cooperation under task dependency constraints. Each mobile device has a limited budget and has to determine which sub-task should be computed locally or should be sent to the edge or remote cloud. To address this issue, we divide the offloading problem into two application finishing time minimization sub-problems with two different cooperation modes, both of which are proved to be NP-hard. We then devise one greedy algorithm with approximation ratio of$1+\epsilon$for the first mode with edge-cloud cooperation but no edge-edge cooperation. Then we design an efficient greedy algorithm for the second mode, considering both edge-cloud and edge-edge co-operations. Extensive simulation results show that for the first mode, the proposed greedy algorithm achieves near optimal performance for typical task topologies. On average, it outperforms the modified Hermes benchmark algorithm by about$23\%\sim 43.6\%$in terms of application finishing time with given budgets. By further exploiting collaborations among edge servers in the second cooperation mode, the proposed algorithm helps to achieve over 20.3 percent average performance gain on the application finishing time over the first mode under various scenarios. Real-world experiments comply with simulation results.

Load balance in vehicular ad hoc networks (VANETs) is a challenge in vehicle-to-vehicle computation offloading, due to stochastic requests of users, heterogeneous service capabilities and high mobility of vehicles, etc. This paper aims to fill this gap by formulating a problem for load balance in a VANET, with the objective of minimizing the maximum load under transmit power, storage capacity, per task completion time and energy consumption constraints. The formulated problem is proved to be NP-hard, then it is investigated by decomposing it into two subproblems, i.e., how to offload tasks for the case of fixed transmit power and how to adjust transmit power for the given offloading decision. For the first subproblem, an approximation algorithm is proposed by offloading the tasks in the vehicle with the maximum load to the vehicle with minimum load. Meanwhile, a deep reinforcement learning algorithm is proposed, in order to focus on the network dynamics. A coalition based algorithm, a distributed coalition based algorithm, as well as an incentive algorithm based on deep reinforcement learning, are proposed to maximize the total payoff for the selfishness of vehicles. For the second subproblem, an adjustment strategy for transmit power is customized to further reduce the computing load. The algorithms are evaluated on an integrated simulation platform with open street map, SUMO, NS-3 and dataset of Google cluster-usage traces. Simulation results show that, the proposed algorithms outperform three state-of-the-art works for most cases, in terms of the maximum load. The proposed distributed algorithm can significantly accelerate the proposed centralized algorithm with acceptable increase in maximum load. Besides, the load can be further reduced by the proposed adjustment strategy.

Multi-access edge computing (MEC) is emerging to improve the quality of experience of mobile devices including internet of things sensors by offloading computing intensive tasks to MEC servers. Existing MEC-enabled cooperative computation offloading works focus on the optimization of total energy consumption but fail to exploit multi-relay diversity and min-max fairness of energy consumption on participated sensors. We explore a typical wireless sensor network with multi-source, multi-relay, and one edge server, where relay nodes can provide both cooperative communication and computation services. We divide the energy efficiency optimization problem into two sub-problems: One is to minimize the weighted average total energy consumption per time slot, and the other is to minimize the maximum weighted energy consumption. For the first sub-problem, we propose an optimal algorithm named as optimal weighted average total energy consumption algorithm (OTCA) based on bipartite matching. For the second sub-problem, greedy algorithm for fairness guarantee (GAF) is proposed with an approximation ratio of (1 + ε), where ε is a small positive constant. Extensive numerical results show that OTCA outperforms the baseline algorithms by 26.7–77.4% on the average total weighted energy consumption while GAF outperforms benchmark algorithms by 30.7–84.4%. NS-3 simulation experiments comply with numerical results.

Fog computing has become an emerging distributed computing paradigm to provide services with low latency and high throughput. However, load unbalance is serious due to the difference in geography, which results in performance deterioration and low utilization of resources in the fog network. In this paper, the load is the tradeoff between the delay and energy consumption for fog nodes. Meanwhile, the problem of minimizing the maximum load in the homogeneous fog network is formulated and its NP-hardness is proved. Then, a greedy algorithm is proposed for solving the problem by giving the preference to offloading the task in the fog node with the maximum load to the fog node with the minimum load in the network. Moreover, for solving the problem with consideration of selfishness of fog nodes, a coalition based algorithm is proposed to encourage the fog nodes with a light load to share their resources to reduce the maximum load. We evaluate the performance of the proposed algorithms on NS-3 and simulation results show that the proposed algorithms outperform the existing algorithm about 40% in terms of the maximum load.

Vehicular fog computing (VFC) has become an appealing paradigm to provide services for vehicles and traffic systems. However, high mobility is one of the great challenges to the communication and computation service qualities in VFC. A network model for directional vehicle mobility is proposed in this paper to guarantee the service qualities of vehicles in VFC. In the model, vehicles are configured into three vehicular subnetworks according to their turning directions at the next crossing. For each subnetwork, vehicles communicate with each other via vehicle-to-vehicle communication, and with roadside units via vehicle-to-infrastructure communication. The aim is to minimize the average response time of the tasks originated from vehicles. By carefully choosing neighboring vehicles as task processing helpers, a greedy algorithm is proposed to solve the mentioned optimization problem. Besides, two bipartite matching based algorithms, named BMA1and BMA2, are proposed by exploiting Kuhn-Munkras approach and minimum-cost maximum-flow approach, respectively. Performance of the proposed model and the offloading algorithms are evaluated on the combined simulation platform by open street map, SUMO and NS-3. Simulation results show that, the proposed model outperforms four existing models in terms of average response time, when the five models have similar number of unsuccessful tasks. Moreover, the proposed BMA1and BMA2are superior to the existing greedy algorithm in terms of the average response time of tasks, and the proposed greedy algorithm significantly accelerates the generation of offloading decisions in comparison to BMA1, BMA2and the existing greedy algorithm.

In heterogeneous cellular network, task scheduling for computation offloading is one of the biggest challenges. Most works focus on alleviating heavy burden of macro base stations by moving the computation tasks on macro cell user equipment (MUE) to remote cloud or small cell base stations. But the selfishness of network users is seldom considered. Motivated by the multiple access mobile edge computing, this paper provides incentive for task transfer from macro cell users to small cell base stations. The proposed incentive scheme utilizes small cell user equipments to provide relay services. The problem of computation offloading is modeled as a two-stage auction, in which the remote MUEs with common social character can form a group and then buy the computation resource of small cell base stations with the relaying of small cell user equipment. A two-stage auction scheme named TARCO is contributed to maximize utilities for both sellers and buyers in the network. The truthfulness, individual rational and budget balance properties of TARCO are also proved in this paper. In addition, two algorithms are proposed to further refine TARCO on the social welfare of the network. One can achieve higher utility of MUEs and the other can obtain higher total social welfare. Extensive simulation results demonstrate that, TARCO is better than random algorithm by 104.90 percent in terms of average utility of MUEs, while the performance of TARCO is further improved up to 28.75 percent and 17.06 percent by the proposed two algorithms, respectively.

Mobile edge computing enables the execution of compute-intensive applications, e.g. deep learning applications, on the end devices with limited computation resources. However, the deep learning applications bring the performance bottleneck in mobile edge computing, due to the movements of a large amount of data incurred by the large number of layers and millions of weights. In this paper, the computing model for parallel deep learning applications in mobile edge computing is proposed, by considering the occupancy allocation of processors, cost of context switch, and multi-processors in edge server and remote cloud. The problem of minimizing the completion time for deep learning applications is formulated, and the NP-hardness of the problem is proved. To solve the problem, an integrated algorithm by merging and scheduling is proposed. Moreover, a real-world distributed platform is developed for evaluating the proposed algorithm. Experimental results show that, the completion time of deep learning application for the proposed algorithm is decreased by 63% and 75%, respectively, without extra control costs, compared with the existing algorithms.

Abstract The task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station’s storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods.

To motivate passengers to participate in carpooling, we focus on how to guarantee the quality of experience (QoE) of passengers in carpooling using coalition game. We formulate the QoE-guarantee problem as a benefit allocation problem. To solve the problem, we quantify the impatience of passengers due to detouring time delay. The algorithm named PCA is proposed to minimize the impatience of all passengers and calculate the compensation for them based on Shapley value. We prove that PCA can guarantee the fairness of passengers. Simulation results demonstrate that PCA can minimize the impatience of passengers and produce a win-win solution for both passengers and drivers in carpooling.

Blockchain technology is emerging in various fields, to guarantee security of digital currency and internet of things. In this paper, we provide incentive to encourage edge servers to serve mobile users for the mobile blockchain application. We formulate the problem as a resource allocation problem, then we propose a three-stage auction to implement resource allocation specially designed for mobile blockchain, and introduce the group-buying mechanism to motivate mobile users. We prove that our auction scheme is truthful, individual rationality, and computational efficiency. We compare proposed scheme with TACD and HAF mechanisms, and simulation results show that the social welfare achieved by our scheme is higher than that of TACD and HAF mechanisms.

A critical issue in data replication is to wisely place data replicas which involves identifying the best possible nodes to duplicate data. Facing dynamics of data requests, this paper investigates the problem of replica placement and update in tree networks, where part of nodes have pre-existing replicas. We aim to develop efficient algorithms to accelerate the replica placement and update without causing obvious degradation in solution quality via reusing pre-existing replicas. Firstly, an efficient heuristic algorithm GRP is proposed to quickly place replicas when users change their requests dynamically, under the Closest policy where a client must be served by the closest server. Then, a Tabu search algorithm TSRP is customized to further refine the solution obtained by GRP. Furthermore, we propose a heuristic algorithm MPFSF for the replica placement and update problem, under the Multiple policy where requests of a client are served by multiple servers. Simulation results show that, GRP and TSRP can accelerate existing dynamic programming algorithm by 87.97% while quality degradation is bounded by 2.49%. MPFSF can achieve the best improvement for about 84.6% than existing heuristic algorithm.

In the large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs),choosing the appropriate position to place cloudlet is very important for reducing the user's access delay. For service provider, it isalways very costly to deployment cloudlets. How many cloudletsshould be placed in a WMAN and how much resource eachcloudlet should have is very important for the service provider. In this paper, we study the cloudlet placement and resourceallocation problem in a large-scale Wireless WMAN, we formulatethe problem as an novel cloudlet placement problem that givenan average access delay between mobile users and the cloudlets, place K cloudlets to some strategic locations in the WMAN withthe objective to minimize the number of use cloudlet K. Wethen propose an exact solution to the problem by formulatingit as an Integer Linear Programming (ILP). Due to the poorscalability of the ILP, we devise a clustering algorithm K-Medoids(KM) for the problem. For a special case of the problem whereall cloudlets computing capabilities have been given, we proposean efficient heuristic for it. We finally evaluate the performanceof the proposed algorithms through experimental simulations. Simulation result demonstrates that the proposed algorithms areeffective.

Mobile cloud computing addresses resource scarcity problem of mobile devices by offloading computation data from mobile devices into the cloud. However, remote server may be far from mobile users. Cloudlet could be used to deal with the long access delay problem. In the large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs), choosing the appropriate position of cloudlet is very important to reducing access delay. Recently, a heuristic algorithm has been proposed. However, it has so many repeated sorting process of APs that the algorithm efficiency is poor. In this paper, we propose a New Heuristic Algorithm (NHA) and a Particle Swarm Optimization (PSO) algorithm for the delaying problem. We evaluate the performance of the proposed algorithms through extensive simulations. Simulation results demonstrate NHA is more efficient then existing algorithm. For the PSO algorithm, in the case of parallelized execution, it is more efficient than the new heuristic algorithm within a bounded delay.

In this paper, we develop an access controller management model which provides new opportunities for further reducing the computation repetition and data transmission redundancy for Mobile Edge Computing (MEC) in 5G network. We propose novel algorithms for solving the offloading problem with consideration of tradeoff between energy consumption and the amount of offloaded data under constraint of overall task computation time. For sequential topology applications, we develop a dynamic programming algorithm to produce optimal solutions. For general topology applications, a critical-path based heuristic algorithm is proposed by repeatedly identifying partial critical path (PCP) from the application task graph and calculating optimal solution for the PCP by performing the proposed dynamic programming algorithm. In addition, the interference of parallel data transmission between tasks (one-to-many, manyto-one and many-to-many) using single channel is taken into consideration. Experimental results demonstrate the effectiveness of our proposed method.

To tackle the challenging secrecy communication problem in energy harvesting cognitive radio networks, this paper considers an overlay system with one energy harvesting secondary user (SU) to assist primary transmission under the assumption that the primary channel at primary receiver is worse than the eavesdropper. Under such scenario, we optimize the secrecy rate of the PU transmitter by jointly investigating energy harvesting slot, cooperative transmission slot and so on. Given the transmission rate requirement between SUs, the optimization problem is formulated as a mixed integer non-linear (MINLP) program. Due to the special features, we design a polynomial time algorithm SRMA to optimally solve this problem. The algorithm computes the lower bound and upper bound of the transmission power in a secondary transmitter, which are relative with the QoS requirement and energy harvesting parameters. Then SRMA determines its optimal transmission power by iteratively searching between two bounds. Numerical results demonstrate that the primary secrecy rate grows with the increasing energy save ratio and optimal energy save ratio is inversely proportional to the energy harvesting rate.

Spectrum auction is an emerging economic scheme to stimulate both primary spectrum operators (POs) and secondary users (SUs) to be involved in spectrum sharing. Previous spectrum auction works mostly assume each PO can only have one type spectrum or each SU can only buy homogeneous spectrum bands from the same PO. However, in a ubiquitous network scenario, each PO possesses heterogeneous spectrum resources such as WiFi, 3G and each SU may request different types of spectrum bands from the same PO. Existing auction schemes cannot be used to effectively solve the problem. Therefore, the authors come out with a lightweight combinatorial double auction to tackle this challenge. Since spectrum combinatorial double auction problem is NP‐hard, the authors develop a general greedy algorithm G‐Greedy to solve the problem. Inspired by the recent group‐buying discounts, they also invent an enhanced scheme E‐Greedy to further optimise total utility. They theoretically prove the economy properties of the proposed schemes such as individual rationality, budget balance and truthfulness. Simulation results show that both of the two algorithms can yield higher utilities and are effective.

With the development of cloud computing, there is an increasing number of market-based mechanisms for cloud resource allocation. Inspired by the emerging group-buying Web sites, we advocate that group-buying can be applied to cloud resource allocation, and thus cloud providers can benefit from demand aggregation due to the advantage of group-buying in attracting customers, while cloud users can enjoy lower price. However, none of the existing allocation mechanisms is specifically designed for the scenario with group-buying, and it is a challenge for mechanism design to take full advantage of group-buying to maximize the total utility. In this paper, we fill this gap by proposing an innovative auction mechanism. The mechanism is designed based on a combinatorial double auction, in which the allocation algorithm and payment scheme are specifically designed to efficiently generate allocation and compute prices considering group-buying. We theoretically prove that the necessary economic properties in auction design, such as individual rationality, budget balance and truthfulness, are satisfied in our work. The experiments show that the proposed mechanism yields higher total utility, and has good scalability.