Privacy leakage on Web-based platform has become a critical challenge as users continually share personal information through online social networks, health tracking platforms, and other Web services. Information recipients and third-party can progressively aggregate shared content across the Web, enabling increasingly accurate profiling of individuals. However, existing studies typically treat each disclosure as independent, overlooking the cumulative privacy risks that arise in continual information sharing. In addition, the subjective cognition of both users and adversaries in Web environments, where users and adversaries can dynamically adapt based on observable actions, remains underexplored. To address these challenges, we propose a dynamic Stackelberg game model for continual information sharing scenarios, where user's sequential privacy decisions are optimized to balance privacy protection and data utility. The model explicitly captures the cognitive behaviors of both the user and the adversary, allowing their subjective perceptions to shape the Stackelberg equilibrium. Building on this formulation, we develop a reinforcement learning-based algorithm to derive approximately optimal strategies for mitigating privacy leakage in the context of continual information sharing. Experiments on real-world datasets demonstrate that our method significantly reduces cumulative privacy risks while preserving the utility of shared content. The proposed model further provides actionable insights for the design of privacy-enhancing technologies and web platform policies.

With the widespread application of the Internet of Things (IoT), computing tasks on the terminal side have surged. Traditional cloud computing models, constrained by high network latency and overloaded central servers, can no longer effectively meet the dual requirements of real-time responsiveness and energy efficiency. The cloud–edge–device collaborative architecture, by enabling distributed resource scheduling, offers a promising solution to reduce both latency and energy consumption. However, optimizing carbon emissions under dynamic operating conditions remains a pressing and unresolved challenge. This paper proposes a carbon-aware dynamic scheduling framework for cloud–edge–device systems, which accounts for the stochastic nature of task arrivals, heterogeneous computing capabilities, and varying carbon intensity across devices and locations. A multi-layer carbon emission model is developed, and the long-term carbon minimization objective is formulated as a stochastic optimization problem. Using the Lyapunov drift-plus-penalty method, the problem is transformed into a tractable deterministic optimization framework, upon which a Carbon-Efficient Computation Offloading (CECO) algorithm is designed. CECO jointly optimizes local computation frequency, data transmission rate, and edge resource allocation to dynamically balance task queue stability and carbon emission intensity. Theoretical analysis and simulation results validate that the proposed algorithm significantly reduces system-level carbon emissions while maintaining quality of service, demonstrating strong potential for enabling green computing in intelligent distributed environments.

Mobile multimedia applications such as real-time video processing, augmented reality, and mobile gaming have raised high requirements for low latency and high efficiency. Edge-based autonomous systems have become a key technology for processing these application tasks. This article focuses on joint resource allocation and task slicing for mobile multimedia computing in edge-based autonomous systems. We propose an efficient resource allocation and task slicing strategy, aiming at the optimization of the overall utility of both edge servers and mobile devices simultaneously. We transform the resource allocation problem into resource pricing and purchasing behaviors. We present a Stackelberg game model and prove theorems for the existence of equilibrium and optimality. Based on the theorems, we design an algorithm namely G-RPTSS for resource purchasing and computation task slicing. Then, we employ Deep Reinforcement Learning (DRL) techniques in resource pricing and propose the DRL-ESRP algorithm which is capable of adaptively responding to dynamic computational scenarios in edge-based autonomous systems. Our scheme leverages the DRL technique for autonomous learning and policy adjustment. Simulation experiments, based on real-world scenario data, demonstrate the superior of our approach in learning efficiency and performance advantages to existing both non-DRL and other DRL algorithms.

Multi-access Edge Computing (MEC) can effectively process Internet of Things (IoT) data by transferring computing intensive tasks to edge servers, and has become an effective mechanism to meet the growing demand for computing. The flexible Unmanned Aerial Vehicle (UAV) and High-Altitude Platform (HAP) with powerful resources working together can significantly improve the efficiency of edge computing system. This paper investigates the resource allocation and trajectory optimization problems in HAP-UAV-MEC system with a Non-Orthogonal Multiple Access (NOMA) communication scenario. By utilizing Wireless Power Transfer (WPT) technology to provide energy support for UAV, we jointly optimize UAV trajectories, resource allocation, and offloading decisions to minimize the energy cost of IoT devices and the energy cost of UAV. This problem is described as a multi-stage Mixed Integer Nonlinear Program ming (MINLP) problem. A Deep Deterministic Policy Gradient (DDPG)-Attention-based Resource Allocation and Trajectory Optimization (DART) algorithm combining Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques is proposed to address this issue. DART algorithm utilizes the Lyapunov technique to transform the multi-stage MINLP problem into a deterministic optimization problem, and decomposes the original problem into four parallel subproblems. Through DDPG-attention algorithm based on reinforcement learning and deep learning attention mechanisms, we solve the problems of trajectory optimization and offloading decision. Meanwhile, for remaining subproblems related to resource allocation, convex optimization is used to solve them. The experimental results verify that the DART algorithm can significantly reduce the total cost while ensuring system stability and performance.

With the popularization of mobile devices, mobile crowdsensing (MCS) has become a paradigm with broad application prospects. However, traditional MCS face numerous challenges, such as surges in network traffic and infrastructure failures. To address these issues, we leverage flexible and low-cost Unmanned Aerial Vehicles (UAVs) in the MCS framework. UAV-assisted crowdsensing (UCS) provides an innovative approach to data collection that effectively addresses problems such as insufficient network coverage and congestion. In the UCS framework, UAVs can serve not only as temporary base stations (BSs) but also participate in collecting data and processing tasks. Nevertheless, the lack of adequate incentive mechanisms may lead both UAVs and mobile users to be reluctant to participate in sensing tasks. Therefore, this paper aims to investigate hierarchical incentive mechanisms for UCS. Considering the freshness of the collected data and the benefits of the platform, we adopt the Age of Information (AoI) metric to measure the quality of data. To ensure AoI of data, we model the incentive mechanisms from both the UAV and user perspectives, and we formulate them as single-dimensional and multi-dimensional contract-based incentives under scenarios of information asymmetry. Furthermore, we derive the optimal contract scheme under the constraints of individual rationality and incentive compatibility. Finally, experimental results confirm the effectiveness of the proposed contract design and maximize the utility of the model owner.

The convergence of edge computing and Artificial Intelligence (AI) gives rise to Edge-AI, which enables the deployment of real-time AI applications at the network edge. A key research challenge in Edge-AI is edge inference acceleration, which aims to realize low-latency high-accuracy Deep Neural Network (DNN) inference by offloading partitioned inference tasks from end devices to edge servers. However, existing research has yet to adopt a practical Edge-AI market perspective, which would explore the personalized inference needs of AI users (e.g., inference accuracy, latency, and task complexity), the revenue incentives for AI service providers that offer edge inference services, and multi-stakeholder governance within a market-oriented context. To bridge this gap, we propose anAuction-basedEdge Inference Pricing Mechanism (AERIA) for revenue maximization to tackle the multi-dimensional optimization problem of DNN model partition, edge inference pricing, and resource allocation. We develop a multi-exit device-edge synergistic inference scheme for on-demand DNN inference acceleration, and theoretically analyze the auction dynamics amongst the AI service providers, AI users and edge infrastructure provider. Owing to the strategic mechanism design via randomized consensus estimate and cost sharing techniques, the Edge-AI market attains several desirable properties. These include competitiveness in revenue maximization, incentive compatibility, and envy-freeness, which are crucial to maintain the effectiveness, truthfulness, and fairness in auction outcomes. Extensive simulations based on four representative DNN inference workloads demonstrate that AERIA significantly outperforms several state-of-the-art approaches in revenue maximization. This validates the efficacy of AERIA for on-demand DNN inference in the Edge-AI market.

In this paper, a two-layer game based joint time and power allocation method for an underlay Energy Harvesting Cognitive Radio (EH-CR) network is proposed. The method first models the interplay between the Primary User (PU) and the Secondary Users (SUs) as a Stackelberg game and then models the interplay among the SUs as a Supermodel game in the underlay EH-CR network. Later, a coefficient for evaluating fair ness is introduced in order to promote fairness among the SUs. Subsequently, the utility function of the primary network and the utility function of the secondary network are defined based on their individual profits. By maximizing the secondary network's utility function, the Supermodel game's Nash Equilibrium (NE) solution is achieved. Then, by substituting the NE solution of the Supermodel game into the utility function of the primary network and then maximizing the utility function of the primary network, the NE solution of the Stackelberg game is obtained. Finally, a deterministic strategy can be obtained, which is the time coefficient of equalized spectrum sensing and the equalized power allocation scheme instead of a probabilistic strategy. Simulation outcomes demonstrate that, under the condition of maintaining the communication quality of the PU, the PU's revenue when PH0 = 0.8 can be improved by 18.2% and when PH0 = 0.6 can be improved by 13.3% compared with the conventional method.

In light of the frequent disaster events worldwide, the risk of communication network disruptions is increasingly prominent. Unmanned Aerial Vehicle (UAV) with flexibility and maneuverability is considered an effective solution for rapid service provisioning. Meanwhile, Non-Orthogonal Multiple Access (NOMA) enables simultaneous access for multiple User Equipments (UEs) on the same frequency band, enhancing spectral efficiency. This paper investigates a system that integrates NOMA with UAV-assisted Mobile Edge Computing (MEC) to provide efficient computational services. First, we construct a system model of UE task offloading and UAV deployment to reduce the overall service cost. Each UE and UAV strive to minimize their own cost. Then, the problem is modeled as the User Task Offloading and UAV Deployment Game (UTUD Game), and it is theoretically proven that at least one Nash equilibrium strategy exists for the offloading and deployment selection. Further, a decentralized algorithm based on game theory named Distributed Iterative Co-optimization for Offloading and Deployment (DICOD) algorithm is proposed to achieve this strategy. The performance of the algorithm is analyzed theoretically. Finally, we conduct experiments to analyze the upper bound of convergence time and evaluate the algorithm's performance in comparison with some other benchmarks.

With the rapid development of the Internet of Vehicles (IoV), massive amounts of distributed data are continuously generated, raising critical challenges in ensuring service reliability and security. Federated Learning (FL) has emerged as a promising approach to address these challenges by enabling privacy-preserving and scalable model training services. However, traditional FL frameworks, which rely on a central server for model aggregation, suffer from single-point failures and limited scalability. Furthermore, in IoV environments, the vast amounts of sensor-collected data often exhibit redundancy, making the existing aggregation strategy inapplicable, leading to inefficient model updates and ultimately degrading performance. To address these challenges, we propose C-DFL (Consensus-based Decentralized Federated Learning), a novel decentralized data processing framework specifically designed to enhance model training services in intelligent vehicle environments. The core innovation of C-DFL lies in its ability to transform data features into sketches, which are then distributed among vehicles to calculate non-redundant data. Considering the quantity of nonredundant data, we design a new federated learning aggregation function. We evaluate the performance of C-DFL through comprehensive experiments conducted on an NS3-based simulation platform using two real-world datasets. The results demonstrate that C-DFL consistently outperforms the compared Decentralized Federated Learning (DFL) methods in terms of accuracy and convergence rate.

Federated Learning (FL) is an emerging distributed learning model that enables multiple devices to collaboratively train a shared model. However, in practical scenarios, device heterogeneity can cause outdated model parameters in synchronous FL. To address this issue, asynchronous updates are introduced to mitigate delays in parameter updates. However, asynchronous updates can lead to imbalanced updates, resulting in excessive energy consumption and reduced model accuracy. To tackle these challenges and ensure both parameter freshness and efficient resource utilization, this paper introduces Age of Information (AoI) as a metric to measure model freshness and optimizes AoI to ensure parameter freshness. Additionally, we consider Age of Peak (AoP) and impose a constraint on peak age to prevent prolonged periods of stale parameters, which lead to inaccurate models. Based on this, we formulate an optimization problem involving update decisions and resource allocation, aiming to optimize AoI and energy consumption while imposing a constraint on AoP. Since asynchronous updates and training delays are both stochastic and dynamic, we employ stochastic optimization techniques to decompose the problem into two subproblems and design a low-complexity algorithm using optimization theory. Extensive experimental results demonstrate the proposed framework's effectiveness in improving parameter freshness and energy efficiency.

Privacy leakage remains a critical challenge in online social networks (OSNs), where users continuously share information despite potential risks. The interconnected nature of OSNs makes the control of privacy information diffusion more complex. This paper proposes a novel adaptive privacy protection framework that can dynamically mitigate the privacy risks during privacy information diffusion. We model privacy diffusion and inference risks along propagation paths and develop a privacy policy generation mechanism using deep reinforcement learning (DRL) to optimize the trade-off between privacy protection and information sharing utility. Extensive experiments on real and synthetic social network datasets show that our method outperforms baseline approaches in controlling privacy risk while preserving the information sharing utility. Moreover, our study highlights the effectiveness of future reward prediction in reinforcement learning for privacypreserving decision-making.

With the arrival of the 6G era, there is a rapid increase in computational demands, and multiaccess edge computing (MEC) has emerged as an effective mechanism to satisfy these needs. In disaster-affected or remote areas where ground base stations may struggle to provide service, unmanned aerial vehicle (UAV), and high-altitude platforms (HAPs) can leverage their flexible deployment capabilities to offer MEC services. In this article, we design a layered aerial computing framework comprising user equipments (UEs), UAV, and HAP. Building upon this, we establish a hierarchical offloading computation model and formulate an optimization problem to maximize resource utilization and task scheduling within the model. Specifically, the objective is to minimize task computation delay and maximize the remaining energy of the UAV, i.e., minimize energy consumption, subject to constraints on the total task quantity, delay requirements and UAV energy limitations. Due to the nonconvex and highly complex nature of this objective problem, we propose a deep reinforcement learning-based trajectory optimization and task offloading (DTOTO) algorithm that enables the agent, the UAV, to make correct decisions in complex environments and high-dimensional action spaces. The algorithm is capable of optimizing the UAV’s trajectory to obtain the correct offloading decisions. Additionally, we employ state normalization to improve training efficiency. Simulation experiment results validate the effectiveness of the computing framework and the DTOTO algorithm, and numerical results analyze to evaluate system performance.

Autonomous aerial vehicles (AAV) are regarded as a promising technology to provide additional computing capabilities and wide coverage for Internet of Things (IoT) devices, particularly in cases where these devices are situated beyond the reach of traditional communication infrastructure. This study investigates an AAV-assisted multiaccess edge computing (MEC) network comprising multiple AAVs with edge servers and several IoT Devices (IoTDs). IoTDs with a substantial number of computation tasks can select to offload their tasks to AAV-assisted edge servers to alleviate pressure and costs, while the AAV-assisted edge servers can profit from selling computing resources. The interaction between AAV-assisted edge servers and IoTDs is modeled as a Stackelberg game, where both entities aim to maximize their utility. Employing backward induction, the existence of a unique Nash equilibrium is proved. Subsequently, a Stackelberg game-based distributed computation offloading (SDCO) algorithm is designed to approximate the optimal solution. Finally, extensive simulations validate the effectiveness of the SDCO algorithm, demonstrating superior performance compared to other benchmark methods across diverse scenarios.

Due to the limitations of computing resources and battery capacity, the computation tasks of ground devices can be offloaded to edge servers for processing. Moreover, with the development of the low earth orbit (LEO) satellite technology, LEO satellite-terrestrial edge computing can realize a global coverage network to provide seamless computing services beyond the regional restrictions compared to the conventional terrestrial edge computing networks. In this paper, we study the computation offloading problem in the LEO satellite-terrestrial edge computing systems. Ground devices can offload their computation tasks to terrestrial base stations (BSs) or LEO satellites deployed on edge servers for remote processing. We formulate the computation offloading problem to minimize the cost of devices while satisfying resource and LEO satellite communication time constraints. Since each ground device competes for transmission and computing resources to reduce its own offloading cost, we reformulate this problem as the LEO satellite-terrestrial computation offloading game (LSTCO-Game). It is derived that there is an upper bound on transmission interference and computing resource competition among devices. Then, we theoretically prove that at least one Nash equilibrium (NE) offloading strategy exists in the LSTCO-Game. We propose the game-theoretical distributed computation offloading (GDCO) algorithm to find the NE offloading strategy. Next, we analyze the cost obtained by GDCO's NE offloading strategy in the worst case. Experiments are conducted by comparing the proposed GDCO algorithm with other computation offloading methods. The results show that the GDCO algorithm can effectively reduce the offloading cost.

Unmanned Aerial Vehicle (UAV)-assisted Low Earth Orbit (LEO) satellite edge computing (ULSE) networks can address the challenge communications issues in areas with harsh terrain and achieve global wireless coverage to provide services for mobile user devices (MUDs). This paper studies the LEO-UAV task offloading problem where MUDs compete for limited resources in the ULSE networks. We formulate the optimization problem with the goal of minimizing the cost of all MUDs while meeting resource constraint and satellite coverage time constraint. We first theoretically prove that this problem is NP-hard. We then reformulate the problem as a LEO-UAV task offloading game (LUTO-Game), and show that there is at least one Nash equilibrium solution for the LUTO-Game. We propose a joint UAV and LEO satellite task offloading (JULTO) algorithm to obtain the Nash equilibrium offloading strategy, and analyze the performance of the worst-case offloading strategy obtained by the JULTO algorithm. Finally, extensive experiments, including convergence analysis and comparison experiments, are carried out to validate the effectiveness of our JULTO algorithm.

Mobile Edge Computing (MEC), as a highly promising technology, effectively processes computation-intensive tasks by offloading them to edge servers. Utilizing the advantages of Unmanned Aerial Vehicles (UAVs) in deployment flexibility and broad coverage, UAV-assisted edge computing can significantly enhance system efficiency. This paper studies a scenario where a UAV-MEC system serves multiple Mobile Users (MUs) with random task arrivals and movements. We minimize the energy consumption of MUs by jointly optimizing UAV trajectory and resource allocation for MUs subjected to the UAV energy limit. The problem is formulated as a multi-stage Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we propose an algorithm called JTORA integrated Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques. Specifically, we initially transform the multi-stage MINLP problem into a deterministic optimization problem utilizing Lyapunov techniques and decompose the original problem into two sub-problems in parallel. Through DRL, we solve the first sub-problem of trajectory and communication resources optimization. For the second sub-problem involving computing resource allocation, convex optimization is employed to get the optimal solution. Theoretical analysis and experimental results demonstrate that the JTORA algorithm can effectively reduce the energy consumption of MUs while ensuring UAV endurance.

Due to the limited battery capacity and computational resources of mobile devices, computation-intensive tasks generated by mobile devices can be offloaded to edge servers for processing. This paper investigates the multi-user task offloading and resource pricing issues in Autonomous aerial vehicle (AAV)-assisted Multi-Access Edge Computing (MEC) systems. The optimization objectives is optimizing the utility of the server and the utility of the Edge Users (EUs), with decision variables encompassing the offloading strategies of EUs and the pricing strategies of the server. We divide the entire optimization problem into two parts. When optimizing the server's utility, server energy consumption is a crucial metric; hence, in the first part, we formulate the user allocation problem with the goal of minimizing the server's overall energy consumption. Utilizing game theory, we transform the user allocation problem into a multi-user non-cooperative game and prove the existence of a Nash Equilibrium (NE). The Game-based User Allocation (GBUA) algorithm is proposed to obtain the user allocation strategy. After addressing the user allocation problem, we consider the simultaneous optimization of both server and EUs utility. Therefore, in the second part, we model the server and EUs's engagement using the Stackelberg game model and employ backward induction to verify the presence of a Stackelberg Equilibrium (SE). Additionally, we propose the Resource Pricing and Task Offloading (RPATO) algorithm, based on game theory, to obtain the SE solution. Finally, extensive experiments are conducted to validate the effectiveness of the proposed algorithms, and numerous comparative algorithms are tested to prove the advancement and innovation of our proposed algorithms.

In UAV-assisted Multi-access Edge Computing (MEC) systems, UAV trajectories and resource pricing directly determine system utility - improper UAV positioning will lead to increased delay and energy costs for users, while inappropriate pricing will result in insufficient task offloading or UAV overload. This paper aims to optimize UAVs' trajectories, pricing strategies and users' offloading decisions, enabling UAVs to move to suitable locations and provide computing offloading services at appropriate prices, thereby reducing delay and energy, and enhancing the utilities of both UAV and users. The user utility specifically consist of throughput, energy consumption, delay penalties, and expenditures on purchasing computing resources. The UAV's utility consists of computing energy consumption, delay penalties, and revenue from selling computing resources as incentives. We formulate the pre-offloading problem to solve users' offloading selection, and transform the problem into a multi-user non-cooperative game using game theory while proving the existence of Nash equilibrium. Then we model the interaction between UAV and users using Stackelberg game model and prove the existence of Stackelberg equilibrium. We use multi-agent deep reinforcement learning (MADRL) and propose DRL and Game Theory-based Trajectory Optimization and Task Offloading (DGTT) algorithm to solve UAVs' trajectories and obtain the Stackelberg equilibrium solution. We sequentially solve for the pricing strategy and offloading decisions, thereby obtaining the optimized utilities of both UAV and users. Finally, we conduct simulation experiments to verify the feasibility of DGTT algorithm, along with comparative experiments that demonstrate our proposed DGTT algorithm's excellent performance in optimizing UAV and user utilities, while reducing system energy consumption and delay.

With the rapid development of 5G technology, the service demand in various application scenarios is continuously increasing. Mobile edge computing (MEC) has become a popular computing paradigm by placing services and corresponding computing resources to edge servers to satisfy the low latency demands of users. However, edge servers lack a stable infrastructure for protection and limited storage space and computing power. Considering the reliability and stability of the edge system, efficiently placing resources and offloading tasks to the edge servers has become an urgent challenge. In this paper, we consider resource placement and task offloading strategies under different time scales to optimize the service response time in a dynamic edge system environment. We established the Markov model to obtain a quantitative relationship between system reliability and latency, and analyze the time required for resource and task offloading. Then, we propose the resource placement and task offloading (RPTO) algorithms under different time scales based on deep reinforcement learning (DRL) techniques with the aim of minimizing the cost of service providers in the long term. The experimental results demonstrate that our approach effectively tackles the challenges of joint resource placement and task offloading in the MEC.

In the evolving landscape of federated learning (FL), ensuring the active participation of local model owners (LMOs) while safeguarding data privacy and service security presents a formidable challenge. Our investigation focuses on two different information scenarios: the weakly incomplete information scenario and the strongly incomplete information scenario, which pose unique challenges to the integrity and efficiency of FL systems. In the weakly incomplete information scenario, LMOs have motivations to hide their true types. We use contract theory and exploit its self-revealing properties to ensure LMOs truthfully report their types. In the strongly incomplete information scenario, We present the Contract-based Deep Reinforcement Learning (CDRL) algorithm, which combines the strategic framework of contract theory with the adaptive capabilities of DRL. The CDRL algorithm is designed to perform real-time contract design in dynamic environments, enabling the system to respond effectively to FL participation and ensure continuous alignment of incentives with system security and learning objectives. Through extensive experimentation on a real-world dataset, our proposed mechanism has demonstrated superiority in motivating LMOs to actively participate in FL, thereby significantly improving system performance.

With the swift progress of Internet of Things (IoT) technologies, the number of IoT devices has grown exponentially, leading to an increasing demand for computational power and system stability. Mobile edge computing (MEC) is a powerful solution that allows IoT devices to offload data to the edge for computing. In situations involving disasters or complex terrains, establishing ground-based stations may be challenging in providing computational services. Edge computing frameworks built with unmanned aerial vehicles (UAVs) and high-altitude platforms (HAPs) can provide airborne computational services for IoT devices situated in environments with disasters or complex terrains. In this article, we design a three-tier framework consisting of ground users (GUs), UAVs, and HAP, offering MEC services for GUs. Considering the randomness and dynamism of task arrivals and the wireless communication quality of devices, we propose an algorithm supporting nonorthogonal multiple access (NOMA) communication in aerial access networks. The objective of the algorithm is to reduce the overall energy consumption of the system while ensuring system stability. Employing stochastic optimization techniques, we convert the task offloading and resource allocation problem into several parallel solvable subproblems. We also provide a theoretical analysis of the algorithm. Through a series of comparative experiments, we demonstrate the feasibility and effectiveness of our proposed dynamic energy-efficient computation offloading (DEECO) algorithm.

Edge caching is crucial in the Internet of Things (IoT) by accelerating content delivery and reducing latency, offering significant advantages. However, inappropriate incentive mechanisms may prevent third-party edge caching nodes from caching data. To address the incentive challenges in edge caching, this paper proposes a contract theory approach to resolve the incentive issues between content providers (Google and Microsoft) and edge caching nodes. Initially, utilizing the framework of contract theory, the security service quality of edge caching nodes is classified into a finite number of types, and transactions between content providers and edge caching nodes are modelled. Subsequently, contract packages containing popular data content and corresponding rewards are designed for different types of edge caching nodes. Utilizing the revelation principle of contract theory addresses the problem of incomplete information in the system, enabling content providers to maximize revenue. A blockchain-based reputation mechanism is employed to identify abnormal nodes within edge caching nodes. Numerical results demonstrate that, compared to other mechanisms, our proposed contracts can effectively incentivize the participation of edge caching nodes, significantly enhance content providers’ revenue, and improve content delivery efficiency and effectiveness.

Multiaccess edge computing (MEC) provides services for resource-sensitive and delay-sensitive Internet of Things (IoT) applications by extending the capabilities of cloud computing to the edge of the networks. However, the high mobility of IoT devices (e.g., vehicles) and the limited resources of edge servers (ESs) affect the service continuity and access latency. Service migration and reasonable resource (re-)allocation consequently become needed to ensure Quality of Service (QoS). However, service migration results in additional latency. In addition, different mobile IoT users have different resource requirements and different resource allocation policies of target ESs also determine whether service migration is necessary. Subsequently, how to jointly optimize service migration and resource allocation (SMRA) is a challenge that needs to be carefully addressed. To this end, this article investigates the joint optimization problem of SMRA in MEC environments to minimize the access delay of IoT users. It proposes a joint SMRA algorithm based on deep reinforcement learning (DRL), which takes into account the mobility of IoT users and decides whether to migrate services, where to migrate, and how to allocate resources through the long short time memory (LSTM) algorithm and the parameterized deep$Q$-network (PDQN) algorithm. Moreover, the PDQN algorithm effectively solves the discrete-continuous hybrid action space challenge in the SMRA problem. Finally, we conduct evaluation using a real-world data set of Beijing cab trajectories to verify the effectiveness and superiority of our proposed SMRA solution.

As the Internet of Things (IoT) becomes ubiquitous and the demand for high-quality services increases, the limitations of traditional network models are starting to show due to endpoint resource constraints. Edge Computing, as an emerging computing paradigm, can significantly improve data processing efficiency and quality of experience by deploying edge servers with computing resources near terminal devices. However, the computing process can generate a lot of carbon emissions due to energy consumption. How to reduce energy consumption and therefore reduce carbon emissions while ensuring service latency is still a pressing issue to resolve. This article focuses on how to reasonably utilize technologies, such as mobile edge computing (MEC), nonorthogonal multiple access (NOMA), and small cell networks (SCNs) in the IoT environment, while considering service latency and energy-saving carbon reduction requirements. We model the problem as a stochastic problem involving local devices, small base stations (SBS), and macro base stations (MBSs), aiming to simultaneously ensure service latency and minimize carbon dioxide emissions. Due to uncertainties, such as task arrival rates and channel conditions, we convert the stochastic problem into a deterministic problem using mathematical optimization theory. Then, we propose an online algorithm called Carbon-aware dynamic task offloading (CADTO) which can solve the problem brought by NOMA and obtain a dynamic offloading strategy. Through theoretical analysis and simulation experiments, we prove that the CADTO algorithm can effectively reduce energy consumption and carbon emissions while ensuring service quality.

Abstract As machine learning (ML) technologies continue to evolve, there is an increasing demand for data. Mobile crowd sensing (MCS) can motivate more users in the data collection process through reasonable compensation, which can enrich the data scale and coverage. However, nowadays, users are increasingly concerned about their privacy and are unwilling to easily share their personal data. Therefore, protecting privacy has become a crucial issue. In ML, federated learning (FL) is a widely known privacy‐preserving technique where the model training process is performed locally by the data owner, which can protect privacy to a large extent. However, as the model size grows, the weak computing power and battery life of user devices are not sufficient to support training a large number of models locally. With mobile edge computing (MEC), user can offload some of the model training tasks to the edge server for collaborative computation, allowing the edge server to participate in the model training process to improve training efficiency. However, edge servers are not fully trusted, and there is still a risk of privacy leakage if data is directly uploaded to the edge server. To address this issue, we design a local differential privacy (LDP) based data privacy‐preserving algorithm and a deep reinforcement learning (DRL) based task offloading algorithm. We also propose a privacy‐preserving distributed ML framework for MEC and model the cloud‐edge‐mobile collaborative training process. These algorithms not only enable effective utilization of edge computing to accelerate machine learning model training but also significantly enhance user privacy and save device battery power. We have conducted experiments to verify the effectiveness of the framework and algorithms.

As the computing resources and the battery capacity of mobile devices are usually limited, it is a feasible solution to offload the computation-intensive tasks generated by mobile devices to edge servers (ESs) in mobile edge computing (MEC) . In this article, we study the multi-user multi-server task offloading problem in MEC systems, where all users compete for the limited communication resources and computing resources. We formulate the offloading problem with the goal of minimizing the cost of the users and maximizing the profits of the ESs. We propose a hierarchical EETORP (Economic and Efficient Task Offloading and Resource Purchasing) framework that includes a two-stage joint optimization process. Then we prove that the problem is NP-complete. For the first stage, we formulate the offloading problem as a multi-channel access game (MCA-Game) and prove theoretically the existence of at least one Nash equilibrium strategy in MCA-Game. Next, we propose a game-based multi-channel access (GMCA) algorithm to obtain the Nash equilibrium strategy and analyze the performance guarantee of the obtained offloading strategy in the worst case. For the second stage, we model the computing resource allocation between the users and ESs by Stackelberg game theory, and reformulate the problem as a resource pricing and purchasing game (PAP-Game). We prove theoretically the property of incentive compatibility and the existence of Stackelberg equilibrium. A game-based pricing and purchasing (GPAP) algorithm is proposed. Finally, a series of both parameter analysis and comparison experiments are carried out, which validate the convergence and effectiveness of the GMCA algorithm and GPAP algorithm.

In many remote areas lacking ground communication infrastructure support, such as wilderness, desert, ocean, etc., an integrated edge computing network in the air with edge computing nodes is an effective solution. It can provide over-the-air computing services for ground devices (GDs) with limited computing resources and battery life. In this paper, we study task offloading and resource allocation in the aerial-based mobile edge computing (MEC) system supported by a high altitude platform (HAP) and unmanned aerial vehicles (UAVs), with the goal of minimizing the GD's energy consumption. Considering that the task arrival of GDs and wireless communication quality are both stochastic and dynamic, we apply stochastic optimization techniques to transform this task offloading and resource allocation problem into two subproblems, i.e., 1) a subproblem for local computation resource allocation, and 2) a subproblem for offloading resource allocation. For the first subproblem, we use convex optimization methods to address it. For the second subproblem, we use game theory to formulate the competition of offloading resources among GDs and propose the Distributed Game-theoretical Multi-server Selection (DGMS) algorithm and the Transmission Power Allocation (TPA) algorithm. Finally, we propose a Distributed Online Task Offloading and Resource Allocation (DOTORA) algorithm and give the theoretical performance analysis of the algorithm. We perform extensive experiments, including the comparison experiments with the UAV-Only and HAP-Only framework, and the comparison experiments with other algorithms under our HAP-UAV framework. The experimental results validate our proposed framework and the DOTORA algorithm.

Due to the limited computing resource and battery capability at the mobile devices, the computation-intensive tasks generated by mobile devices can be offloaded to edge servers or cloud for processing. In this paper, we study the multi-user task offloading problem in an end-edge-cloud system, in which all user devices compete for the limited communication and computing resources. Particularly, we first formulate the offloading problem with the goal of maximizing the Quality of Experience (QoE) of the users subject to resource constraints. Since each user focuses on maximizing its own QoE, we reformulate the problem as a Multi-User Task Offloading Game (MUTO-Game). We then identify an important property that for any device, both the communication interference and the degree of computing resource competition can be upper bounded. Based on the property, we further theoretically prove that there exists at least one Nash Equilibrium offloading strategy in the MUTO-Game. We propose the Game-based Decentralized Task Offloading (GDTO) approach to obtain the Nash Equilibrium offloading strategy. Finally, we analyze the upper bound for the convergence time and characterize the performance guarantee of the obtained offloading strategy for the worst case. A series of experimental results are presented, in comparison with both the centralized optimal approach and the approximate approaches.

With the explosive development of mobile computing, federated learning (FL) has been considered as a promising distributed training framework for addressing the shortage of conventional cloud based centralized training. In FL, local model owners (LMOs) individually train their respective local models and then upload the trained local models to the task publisher (TP) for aggregation to obtain the global model. When the data provided by LMOs do not meet the requirements for model training, they can recruit workers to collect data. In this paper, by considering the interactions among the TP, LMOs and workers, we propose a three-layer hierarchical game framework. However, there are two challenges. First, information asymmetry between workers and LMOs may result in that the workers hide their types. Second, incentive mismatch between TP and LMOs may result in a lack of LMOs’ willingness to participate in FL. Therefore, we decompose the hierarchical-based framework into two layers to address these challenges. For the lower-layer, we leverage the contract theory to ensure truthful reporting of the workers’ types, based on which we simplify the feasible conditions of the contract and design the optimal contract. For the upper-layer, the Stackelberg game is adopted to model the interactions between the TP and LMOs, and we derive the Nash equilibrium and Stackelberg equilibrium solutions. Moreover, we develop an iterativeHierarchical-basedUtilityMaximizationAlgorithm (HUMA) to solve the coupling problem between upper-layer and lower-layer games. Extensive numerical experimental results verify the effectiveness of HUMA, and the comparison results illustrate the performance gain of HUMA.

The prosperous development of the Internet of Things (IoT) and wireless communication technologies has led to explosive growth in the number of IoT sensor devices. However, some sensor devices are inevitably deployed in remote and inaccessible areas. How to continuously and reliably power sensor devices is a critical problem that needs to be addressed. Deploying self-powered modules on sensor devices by adopting self-powered technology is an effective solution to the energy shortage of sensor devices. Besides, Mobile Edge Computing (MEC) as a promising paradigm has provoked widespread popularity. With the help of MEC, devices can offload computing tasks to edge servers for processing, which greatly alleviates the limitations on energy, storage, and computation capability of devices. In this paper, we jointly study task scheduling and resource allocation in the MEC scenario where the sensor devices are with self-powered modules. Our goal is to minimize the long-term average energy consumption of self-powered sensor devices while ensuring system performance. We adopt stochastic optimization techniques to transform the modeled stochastic problem into a deterministic problem. Then, the deterministic problem is decomposed into four sub-problems, and we propose a task scheduling and resource allocation (TSRA) algorithm to solve these problems. Finally, we carry out a series of parameter analysis and comparison experiments to verify the TSRA algorithm. The experimental results show that our TSRA algorithm can make a dynamic tradeoff between energy consumption and system performance. It also demonstrates the effectiveness of our TSRA algorithm compared with other baseline algorithms.

Abstract Software resource allocation is an significant factor of system configuration which plays a critical role in guaranteeing the performance of multitier web service systems. Computing the optimal allocation of different software resources in order to meet performance requirements under dynamic workloads conditions is in highly challenging. Existing approaches mostly rely on translating domain knowledge from experts into computational solutions through heuristics‐based optimization techniques. While such techniques are useful, they cannot leverage actual usage data generated by system users which may contain allocation strategies that are not captured by domain experts' knowledge. In this paper, we propose an iterative feedback mechanism which solves the problem to some extent by optimizing software resource allocation of multitier web systems through imitating system users who have achieved excellent performance. Specifically, we propose a deep Q‐learning network‐based approach for performance prediction to deal with the dynamic changes of complex workloads. The performance prediction method involves the reinforcement learning method for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the multitier web benchmark system, and the experimental results demonstrated significant improvement compared to models built based on domain knowledge.

With more computational intensive applications deployed involving mobile edge computing (MEC), the collaboration among mobile devices, edge and cloud servers becomes an effective mechanism to fully utilize all available distributed computing resources. However, two main challenges have yet to be addressed to enable this three-way collaboration for securing necessary computational resources and further guaranteeing the quality of service (QoS) of task handling. The first challenge is related to the partitioning of an application task into several dependent subtasks and schedule them among the collaborating device-edge-cloud (DEC). The second is focused on the allocation of necessary computing resources of device, edge and cloud servers for effective subtask handling. To this end, we study the joint task offloading and resource allocation for DEC collaboration in this paper by formulating a new optimization problem with the objective of minimizing the task handling latency. To solve this problem, we decompose the original problem into two subproblems, which include the first one of calculating the optimal task partitioning ratio by mathematical analytical method, as well as the second on using the Lagrangian dual (LD) method for obtaining the optimal task offloading and resource allocation policy. Finally, we conduct simulation experiments on a real-life dataset obtained from the central business district (CBD) of Melbourne, Australia, and the experimental results validate the efficacy of our approach in minimizing latency.

With the growing popularity of mobile edge computing (MEC), a number of data-intensive applications have been deployed. Quality of Service (QoS), as one of the most important requirements in MEC, has attracted significant attention of both academia and industry. There are two challenges for ensuring the Qos of the data-intensive services. Firstly, besides performance, reliability is another important concern especially for some critical applications, which remains unexplored. Secondly, data compression has to be involved for reducing the heavy workload of wireless communications at the edge of the network. To overcome these challenges, this paper jointly studies the reliability-aware data compression and task offloading for data-intensive applications in MEC. Markov models are constructed to capture the dynamics of the state transitions of MEC systems, and quantitative analyses are conducted for performance and reliability evaluation. For fully taking advantages of data compression in QoS guarantee, we formulate an optimization problem with the objective of minimizing latency while satisfying constraints on reliability and energy consumption. After describing the NP-Hardness of the problem, we apply the techniques of problem linearization and constraint relaxation, transform the original problem into a convex optimization problem. Then, the problem can be solved in a parallel way by introducing the Alternating Direction Multiplier Method (ADMM), and a distributed Reliability-aware Task Processing and Offloading (RTPO) algorithm is presented. Finally, extensive simulation experiments are conducted to validate the efficacy of our approach, the experimental results illustrate the superiority of our approach over both baseline and state-of-the-art algorithm.

With the maturity of 5G cellular communication systems and mobile-edge computing (MEC), a large number of base stations (BSs) with edge-computing servers are densely deployed. There are extensive overlapping coverage areas among the BSs in which some heavy computational tasks from Internet of Things (IoT) devices can be divided and offloaded to multiple BSs via the coordinated multipoint (CoMP) technique for parallel processing. However, it is a challenging issue about how to make proper task offloading decisions among multiple connected BSs while satisfying delay requirements of multiple devices. To address this challenge, this article presents an efficient multidevice and multi-BSs task offloading scheme with the goal of minimizing the delay for completing the tasks of the devices. By conducting quantitative analysis of local delay and offloading delay, a nonlinear and nonconvex delay optimization offloading problem, which is based on the theory of noncooperative game, is formulated. We prove the existence of Nash equilibrium by analyzing the feature of the proposed offloading problem and further propose a distributed task offloading algorithm called DOLA. Finally, simulation experiments based on real-world data set from the Melbourne CBD area of Australia are conducted to validate the efficacy of our DOLA algorithm. Comparison experiments are also carried out to demonstrate the superiority of DOLA in comparison with some existing schemes.

Computation offloading is a hot research topic in mobile edge computing (MEC). Computation offloading among multiedge nodes in heterogeneous networks can help reduce offloading cost. In addition, the unmanned aerial vehicles (UAVs) play a key role in MEC, where UAVs in the air communicate with ground base stations to improve the network performance. However, limited channel resources can lead to the increase of transmission delay and the decline of communication quality. Effective channel selection mechanisms can help address those issues by improving transmission rate and ensuring communication quality. In this paper, we study channel selection during communication between multiple UAVs and base stations in an MEC system with heterogeneous networks. To maximize the transmission rate of each UAV user, we formulate a channel selection problem and model it as a noncooperative game. Then, we prove the existence of Nash equilibrium (NE). In addition, we design a multiple UAV-enabled transmission channel selection (UTCS) algorithm to obtain the equilibrium strategy profile of all the UAV users. Experimental results validate that UTCS algorithm can converge after a finite number of iterations and it outperforms random transmission algorithm (RTA) and sequential transmission algorithm (STA).

For a cloud service provider, it necessitates an emerging cloud ecosystem to consolidate the existing users and attract more potential users, further gaining its market share. Therefore, in this article, we design a price-incentive resource auction mechanism in cloud environment. In response to the cloud resource price, each user synthesizes her bidding budget and QoS requirement, and purchases cloud resources according to her resource demand in a strategic manner. The cloud service provider, meanwhile, can regulate the resource demands of users through conducting a market-based pricing strategy, against too low prices to cover the operational costs (i.e., energy costs) or too high prices resulting in user churn. In virtue of an elaborate market-based pricing strategy, the interests of users and the cloud service provider are balanced. Our price-incentive resource auction mechanism targets to stimulate maximum users willing to purchase resources and perform their applications at the cloud, on the premise of a minimum profit rate guaranteed for the cloud service provider. It is also able to provide budge balance and truthfulness guarantee, and satisfy the envy-freeness. In order to carry out the above objectives, we carefully design the user utility function reflecting the complicated user interest, and formulate our resource pricing and auction problem as a bin packing problem, which has non-polynomial computational complexity. Regarding the NP-hardness of optimization problem and the concavity of user utility, we present a computational-efficient ($1+\epsilon $)-approximate algorithm namely PIRA. Finally, we conduct simulations based on the real-world dataset to validate the effectiveness of our proposed approach.

With more users outsourcing their applications to the cloud, resource pricing becomes an important issue for IaaS cloud management. Jointly considering her own bidding budget and the price of cloud resources, each user is self-motivated to purchase cloud resources according to her resource demand which maximizes her own utility. Meanwhile, the cloud service provider (CSP) regulates the price of cloud resources with a certain profitability objective achieved. With an elaborate resource pricing strategy, the goals from users and the CSP are balanced and respectively satisfied to some extent. This article provides an insight into the market-oriented cloud pricing strategy. In specific, we propose an auction market in the IaaS cloud, where multiple users with heterogeneous bidding budgets and QoS requirements subscribe cloud resources according to their resource demands. The resource pricing and demand allocation scheme targeting revenue maximization also satisfies essential properties including budget feasibility, incentive compatibility and envy-freeness. To attack the NP-hardness and non-convexity of revenue maximization problem, we design a price-incentive resource auction mechanism namely RARM, which preserves an ( 1+α) approximation ratio on revenue maximization. Finally, we evaluate our RARM mechanism based on the real-world dataset to certify the efficacy of our proposed approach.

Software resource allocation has a significant impact on the quality of service and the performance of multi-tier web systems. It poses a great challenge to compute the allocation of different software resources in order to meet performance requirements under dynamic workloads conditions. To this end, this paper proposes an iterative feedback mechanism to optimize software resource allocation of multi-tier web systems. Specifically, we propose a Q-learning network-based approach for performance prediction. The predictor involves a deep Q-learning network for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the RUBiS benchmark system, and the experimental results demonstrate its significant advantages.

Geographic information system (GIS) is an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships, and model spatial processes. With the growing popularity and wide application of GIS in reality, performance has become a critical requirement, especially for mobile GIS services. To attack this challenge, this paper tries to optimize the performance of GIS services by deploying them into edge computing architecture which is an emerging computational model that enables efficient offloading of service requests to edge servers for reducing the communication latency between end-users and GIS servers deployed in the cloud. Stochastic models for describing the dynamics of GIS services with edge computing architecture are presented, and their corresponding quantitative analyses of performance attributes are provided. Furthermore, an optimization problem is formulated for service deployment in such architecture, and a heuristic approach to obtain the near-optimal performance is designed. Simulation experiments based on real-life GIS performance data are conducted to validate the effectiveness of the approach presented in this paper.

Abstract Software Outsourcing Partnership (SOP) is considered as a type of risk and reward sharing relationship between a client organisation, in the developed countries, and its overseas vendor organisation. Regardless of numerous benefits, the development of SOP still remnants in its infancy stage due to several interactive barriers. Some studies have been conducted to examine the barriers to SOP formation. However, no attempt has been reported so far to explore the multifaceted interrelationships amongst them. To bridge the gap, this study implements Interpretive Structural Model (ISM) approach to reconnoitre the interrelationships amongst the barriers in the context of SOP formation. The objective of this research paper is to develop a framework for modelling structural association amongst the barriers. To achieve the objective, we used a hybrid methodology based on systematic literature review (SLR), empirical survey, and ISM. Firstly, via SLR study, we identified 27 barriers to SOP formation. Secondly, to empirically explore the interrelationships amongst the identified barriers, a questionnaire survey was performed with 50 experts from a total of 20 different countries. Further, interrelationships amongst the barriers were identified using ISM via panel review, and their classifications were carried out via Cross‐Impact Matrix Multiplication Applied to the Classification Approach.

The increasing momentum of service-oriented architecture has led to the emergence of divergent delivered services, where service selection is meritedly required to obtain the target service fulfilling the requirements from both users and service providers. Despite many existing works have extensively handled the issue of service selection, it remains an open question in the case where requests from multiple users are performed simultaneously by a certain set of shared candidate services. Meanwhile, there exist some constraints enforced on the context of service selection, e.g. service placement location and contracts between users and service providers. In this paper, we focus on the QoS-aware service selection with constraints from a fairness aspect, with the objective of achieving max-min fairness across multiple service requests sharing candidate service sets. To be more specific, we formulate this problem as a lexicographical maximization problem, which is far from trivial to deal with practically due to its inherently multi-objective and discrete nature. A fairness-aware algorithm for concurrent service selection (FASS) is proposed, whose basic idea is to iteratively solve the single-objective subproblems by transforming them into linear programming problems. Experimental results based on real-world datasets also validate the effectiveness and practicality of our proposed approach.

Service composition is popular for composing a set of existing services to provide complex services. With the increasing number of services deployed in cloud computing environments, many service providers have started to offer candidate services with equivalent functionality but different Quality of Service (QoS) levels. Therefore, QoS-aware service composition has drawn extensive attention. Most existing approaches for QoS-aware service composition assume a service's QoS values are not correlated to those of other services. However, QoS dependency exists in real life, and impacts the overall QoS values of the composite services. In this article, we study QoS dependency-aware service composition considering multiple QoS attributes. Based on the Pareto set model, we focus on searching for a set of Pareto optimal solutions. A candidate pruning algorithm for removing the unpromising candidates is proposed, and a service composition algorithm using Vector Ordinal Optimization techniques is designed. Simulation experiments are conducted to validate the efficiency and effectiveness of our algorithms. We are the first to take advantage of Vector Ordinal Optimization techniques to search for Pareto optimal composition solutions with QoS dependency involved. The capturing of QoS dependency enables us to find truly desirable solutions.

Edge computing is an emerging computational model that enables efficient offloading of service requests to edge servers. By leveraging the well‐developed technologies of cloud computing, the computing capabilities of mobile devices can be significantly enhanced in edge computing paradigm. However, upon the arrival of user requests, whether to dispatch them to the edge servers or cloud servers in order to guarantee the quality of service (QoS), i.e., the QoS‐aware service selection problem, still remains an open problem. Due to the dynamic mobility of users and the variation of task arrivals and service processes, it is extremely costly to obtain the global optimal solution by both mathematical approaches and simulation‐based schemes. To attack this challenge, this paper proposes a simulation‐based approach of QoS‐aware dynamic service selection for mobile edge computing systems. Stochastic system models are presented and mathematical analyses are provided. Based on the analytical results, the QoS‐aware service selection problem is formulated by a dynamic optimization problem. Goal softening is applied to the original problem, and service selection algorithms are designed using ordinal optimization techniques. Simulation experiments are conducted to validate the efficacy of the approach presented in this paper.

Performance analysis is crucial to the successful development of cloud computing paradigm. And it is especially important for a cloud computing center serving parallelizable application jobs, for determining a proper degree of parallelism could reduce the mean service response time and thus improve the performance of cloud computing obviously. In this paper, taking the cloud based rendering service platform as an example application, we propose an approximate analytical model for cloud computing centers serving parallelizable jobs using M/M/c/r queuing systems, by modeling the rendering service platform as a multi-station multi-server system. We solve the proposed analytical model to obtain a complete probability distribution of response time, blocking probability and other important performance metrics for given cloud system settings. Thus this model can guide cloud operators to determine a proper setting, such as the number of servers, the buffer size and the degree of parallelism, for achieving specific performance levels. Through extensive simulations based on both synthetic data and real-world workload traces, we show that our proposed analytical model can provide approximate performance prediction results for cloud computing centers serving parallelizable jobs, even those job arrivals follow different distributions.

In multi-tier cloud service systems, performance evaluation relies on numerous experiments in order to collect key metrics such as resources usage. The approach may result in highly time-consuming in practice. In this paper, we propose an automated framework for performance tracking, data management and analysis to minimize human intervention in multi-tier cloud service systems. The framework support fine-grained analysis of the mixed workloads through the Discrete-time Markov-modulated Poisson process (DMMPP). A general multi-tier application is theoretically formulated as a queueing network to evaluate the performance. The effectiveness of the model has been validated through extensive experiments conducted in the RUBiS benchmark system.

Soft resource allocation is an important factor of system configuration which plays a critical role in guaranteeing the performance of multi-tier web service systems. There is a tradeoff between real-time performance and resource consumption, and thus the real-time adjustment of soft resource allocation in response to dynamic workload is quite challenging. In this paper, we propose a real-time soft resource allocation method that integrates both model-based analysis and real-time optimization. Specifically, a multi-tier web service system is firstly formulated by a queueing network model, and theoretical analyses are provided. Then, an optimization approach for real-time soft resource allocation is designed by applying sliding window techniques, in order to cope with dynamic workloads and performance demands. Based on the RUBiS benchmark system, model parameters are obtained by measurements and the efficacy of our approach is finally validated.

With the rapid growth of cloud computing and mobile computing, it is commonplace for users to request cloud services from mobile devices. Mobile edge clouds (MECs) allow the users to access the cloud services seamlessly. Although cloudlets provide a promising technique to reduce the service access latency, how to place the data-intensive service in MECs to reduce the communication overhead between different services is yet to be addressed. To attack this challenge, this paper proposes an approach for mining interacting services and placing the services on the cloudlets by optimizing the communication overhead. In this approach, a frequent itemsets mining algorithm is proposed to obtain the fine-grained frequent 2-itemsets by analyzing the cookies. This algorithm determines the minimum support threshold automatically, based on which FP-tree with FP-matrix is constructed to avoid traversing the FP-tree during the process of frequent 2-itemsets discovery, then a searching algorithm is presented to mine the discriminative frequent 2-itemsets with interestingness measure. Furthermore, the communication overhead is optimized with the capacity constraints of cloudlets. Finally, we validate the efficacy of our approach by real-world data based simulations. The results show our approach can reduce the communication overhead for service placement in MECs.

With the increasing popularity of services published online, energy consumption of services computing systems is growing dramatically. Besides Quality of Service (QoS), energy efficiency has become an important issue and drawn significant attention. However, energy efficient request scheduling and service management for large-scale services computing systems face challenges because of the high dynamics and unpredictability of request arrivals. In this paper, we jointly consider the conflicting metrics of performance, queue congestion and energy consumption. We propose a distributed online scheduling and management algorithm which does not require any priori statistical knowledge of request arrivals. Mathematical analysis is conducted which demonstrates that our algorithm can achieve arbitrary tradeoff between performance and energy efficiency. Numerical and real trace data based experiments are carried out to validate the effectiveness of our algorithm in optimizing energy efficiency while stabilizing the system.

Soft resources, which are system software components that use hardware or synchronize the use of hardware, are playing a critical role in the performance of multi-tier web systems, and thus it is quite important to tune the soft resource allocation for using the limited hardware resources to obtain maximum effectiveness. In this paper, we integrate both theoretical and experimental studies to the soft resource allocation problem. Specifically, we apply the queueing network model for formulating multi-tier web systems, and conduct experimental measurements based on the RUBiS benchmark system to obtain precise model parameters. Quantitative analysis is carried out, based on which an optimization model as well as an algorithm are put forward for soft resource allocation. The efficacy of our approach is validated by both theoretical analyses and experimental results.

Nowadays, with the rapid development of hardware and network technologies, various types of smart devices are released by different vendors, resulting in the emergence of Internet of Things (IoT). However, most of the existing approaches for IoT device management are designed in a centralized way, whose efficiency meets challenges recently because of the large scale of heterogeneous device modules and highly dynamic essence of the networks. To tackle this challenge, we propose a distributed approach for IoT device management and service composition from a social network point of view. Specifically, we introduce Restful web service to encapsulate heterogeneous IoT device modules, providing uniform interfaces for IoT service invocation. According to the relationships between IoT devices, social network theory is applied to model IoT services in different dimensions. After fully considering the social properties, a flexible and scalable schemes for IoT service composition is designed to satisfactorily meet users' requirements from several aspects. Finally, simulation experiments based on dataset from reality are conducted to validate the effectiveness of our approach.

As web service has become a popular way for engineering software on the Internet, quality of service (QoS) which describes non-functional characteristics of web services is often employed in service composition. Since QoS is an aggregated concept consisting of several attributes, service composition on enormous candidate sets is a challenging multi-objective optimization problem. In this paper, we study the problem from a general Pareto optimal angle, seeking to reduce search space in service composition. Pareto set model for QoS-aware service composition is introduced, and its relationship with the widely used utility function model is theoretically studied, which proves the applicability of our model. QoS attributes are systematically studied according to their different types of aggregation patterns in service composition, and QoS-based dominance relationships between candidates and between workflows are defined. Taking advantage of pruning candidates by dominance relationships and constraint validations at candidate level, a service composition algorithm using partial selection techniques is proposed. Furthermore, a parallel approach is designed, which is able to significantly reduce search space and achieve great performance gains. A careful analysis of the optimality of our approach is provided, and its efficacy is further validated by both simulation experiments and real-world data based evaluations.

With the increasing presence of web services on the Internet, Quality of Service (QoS) is becoming important for describing non-functional characteristics of web services, and is often employed in web service composition. As QoS is an aggregated concept consisting of several attributes, service composition on enormous candidate sets is a challenging multi-objective optimization problem. In this paper, we study the problem from a general Pareto-optimal angle, seeking to reduce the search space in service composition. QoS attributes are systematically studied according to their different types of aggregation pattern in service composition, and QoS-based dominance relationships between candidates and between workflows are defined. Taking advantage of pruning candidates by dominance relationships and constraint validations at candidate level, a service composition algorithm using partial selection technique is proposed, which is able to significantly reduce the search space and achieve great performance gains. A careful analysis of the optimality of our approach is provided, and its efficacy is further validated by empirical evaluation.

With the increasing popularity of web services on the Internet, service selection has become an important issue in large-scale web service systems. In recent years, with the growing energy consumption associated with IT systems and services, energy efficiency has drawn extensive attention in service allocation and selection. However, existing approaches for energy efficient web service selection face great challenges, because of the high dynamics and unpredictability of task arrivals, and large scale of current web service systems. In this paper, we present a distributed online approach for web service selection that jointly considers the conflicting performance metrics including response time, queue congestion and energy consumption. Targeting at optimizing the long-term average system reward, our approach does not require any priori knowledge of the statistics or prediction on task arrivals. Mathematical analysis as well as simulation experiments demonstrate its effectiveness in optimizing energy efficiency while stabilizing the system.

With the increasing popularity of web services on the Internet, besides functionalities, Quality of Service (QoS) is becoming an important concern for describing characteristics of web services. QoS rankings provide valuable information for making optimal service selection and recommendation from a set of functionally similar or equivalent service candidates. However, in order to obtain such rankings, a huge number of invocations on the services are usually required, which is extremely expensive and even impractical in reality. To tackle this challenge, this paper proposes a scheme to derive the global ranking from observations of QoS rankings on subsets of the services, while the observations may also be contaminated by noise and errors. We introduce a pairwise comparison model to describe the relationships between services, and thus the ranking can be formulated as random walks over the services. A Markov chain based approach is proposed, and algorithms for deriving global rankings are designed. The efficacy of our approach is validated by both mathematical analysis and simulation experiments.

With the increasing popularity of Software defined network (SDN), designing a scalable SDN control plane becomes a critical problem. An effective approach to improving the scalability is to design distributed architecture of SDN control plane. However, how to evaluate the scalability of SDN control planes remains unexplored. In this paper, we propose a metric of scalability for SDN control planes, and study three typical SDN control plane structures, including centralized, decentralized and hierarchical architectures. We build performance models for response time, based on which we evaluate the scalability of these three structures. Furthermore, the comparison between different architectures are analyzed by mathematical methods. Numerical evaluations are also conducted to validate the conclusions drawn in this paper.

Error estimating codes (EEC) have recently been proposed for measuring the bit error rate (BER) in packets transmitted over wireless links. They however can provide such measurements only when there are no insertion and deletion errors, which could occur in various wireless network environments. In this work, we propose ``idEEC'', the first technique that can do so even in the presence of insertion and deletion errors. We show that idEEC is provable robust under most bit insertion and deletion scenarios, provided insertion/deletion errors occur with much lower probability than bit flipping errors. Our idEEC design can build upon any existing EEC scheme. The basic idea of the idEEC encoding is to divide the packet into a number of segments, each of which is encoded using the underlying EEC scheme. The basic idea of the idEEC decoding is to divide the packet into a few slices in a randomized manner -- each of which may contain several segments -- and then try to identify a slice that has no insertion and deletion errors in it (called a ``clean slice''). Once such a clean slice is found, it is removed from the packet for later processing, and this ``randomized divide and search'' procedure will be iteratively performed on the rest of the packet until no more clean slices can be found. The BER will then be estimated from all the clean slices discovered through all the iterations. A careful analysis of the accuracy guarantees of the idEEC decoding is provided, and the efficacy of idEEC is further validated by simulation experiments.

Dependability is an important consideration during the design and development of IT systems and services. But in services computing systems, the traditional definition and evaluation methods from the systems' and components' point of view meet challenges. In this paper, we veer from the angle of view and study the dependability and their attributes from the service-oriented perspective. A stochastic model using the semi-Markov process is put forward, and the quantitative analysis of the dependability attributes is carried out. By extending and transforming this model, the mean time to dependability attributes failure is calculated. Based on the analysis and calculations, some theorems are proposed and proved, to show the inter-relationships and comparisons of the different dependability attributes. Furthermore, we model the service composition and conduct workflow analysis to show how this model could deal with complex services. In addition, LANL service systems are analyzed as a case study to show how the proposed model and calculation methods could apply to real systems, and sensitivity analysis is also performed to identify the bottlenecks and find effective ways for dependability optimization.

Content-Centric Network (CCN) provides a cleanslate design for the Internet, where content becomes the primitive of communications. In CCN, routers are equipped with content stores, which act as caches for frequently requested content. This design enables the Internet to provide content distribution services without any application-layer support. On the other hand, as caches are integrated into routers, the overall performance of CCN will be influenced by the caching efficiency. This paper studies the performance issues of caches in CCN, with the aim to gain some understanding on how caches should be designed to maintain a high performance in a cost-efficient way. Specifically, we use a two-dimensional discrete-time Markov chain to model the two-layer cache hierarchy formed by CCN routers, and develop an efficient algorithm to calculate the hit ratios of these caches. Simulations validate the accuracy of our modeling method, and convey some understanding on cache design in CCN.

With the increase of the energy consumption associated with IT systems and services, energy efficiency is becoming a critical concern in the design, development and management of web service systems. In this paper, both the web service selection and server dynamic speed scaling are optimized by maximizing the quality of service (QoS) revenue and minimizing energy costs. Stochastic models of web service systems are proposed, and quantitative analysis of the performance and energy consumption is carried out. In addition, the service selection and speed scaling problem is formulated as a Markov Decision Process (MDP) problem, and algorithms to solve it are introduced. Furthermore, we propose an agent-based optimization framework and design related algorithms to solve the service selection and speed scaling problem in large-scale web service systems. Finally, their effectiveness is validated by simulation results.

In this paper, using relative low cost 130nm RFCMOS, a high linearity, SAW-less, LTE transmitter with quadrature passive voltage mixer driven by bootstrapped 25% duty-cycle LO is presented. A tunable LC tank is added between mixer and Pre-PA amplifier (PPA). As the result, requirement on linearity of reconstruction lowpass filter (LPF), mixer and PPA are reduced. The linearity of mixer is further improved by programmable bootstrap voltages. Sideband leakage and LO Feedthrough (LOFT) can be calibrated by these bootstrap voltages. 54dB of total 84dB gain dynamic range is implemented in PPA by using binary gm cells and C-2C voltage division. 25% duty-cycle LO is generated with IQ-AND gating scheme. Delivering +1.3dBm power at 2.5GHz carrier, the transmitter achieves -46.5dB adjacent ACLR, -60.5dB LO leakage and -35.3dB sideband leakage, while consuming 127mW. The out-of-band noise is -156.2dBc/Hz at 120 MHz offset and the measured EVM is 2.4%.

In cognitive radios, energy detector is considered for spectrum sensing in the literature. However, its performance deteriorates rapidly if the noise power is not known exactly. Moreover, due to the presence of a colored channel interferer or some other reasons, the conventional white Gaussian noise may become colored. In order to solve these problems, this paper proposes several multicumulant based spectrum sensing methods: generalized likelihood ratio test (GLRT) based multicumulant (GLRTMC) based detection method and multiantenna-assisted multicumulant (MAMC) based detection method. GLRTMC detection method is derived from generalized likelihood ratio test and assumed to be near optimum in theory. MAMC detection method, on the other hand, by using multiple antennas, is a complexity-reduced detector and allows us to make a compromise between performance and complexity. It is well known that cumulants higher than second order are zero for Gaussian distributions. Thus, GLRTMC detection method and MAMC detection method can extract a non-Gaussian signal from Gaussian noise even when the noise is colored. In addition, the proposed methods are nonparametric in the sense that they do not require any exact prior knowledge about the signal or the noise, such as noise power or cyclic frequencies. Hence they are immune from noise uncertainty. Simulation experiments are provided to show the validity and the superiority over single-cumulant based detector of the proposed multicumulant based detectors.

The virtual infrastructure has become more and more popular in the grid and cloud computing. With the aggrandizement scale, the management of the resources in virtual infrastructure faces a great technical challenge. To support the upper services effectively, it raises higher requirements for the performance, fault-tolerance and scalability of virtual infrastructure management systems. In this paper, we study the performance, fault-tolerance and scalability of virtual infrastructure management systems with the three typical structures, including centralized, hierarchical and peer-to-peer structures. We give the mathematical definition of the evaluation metrics and give detailed quantitative analysis, and then get several useful conclusions for enhancing the performance, fault-tolerance and scalability, based on the quantitative analysis. We believe that the results of this work will help system architects make informed choices for building virtual infrastructure.