Showing papers on "Distributed algorithm published in 2019"

Time, clocks, and the ordering of events in a distributed system

Abstract: The concept of one event happening before another in a distributed system is examined, and is shown to define a partial ordering of the events. A distributed algorithm is given for synchronizing a system of logical clocks which can be used to totally order the events. The use of the total ordering is illustrated with a method for solving synchronization problems. The algorithm is then specialized for synchronizing physical clocks, and a bound is derived on how far out of synchrony the clocks can become.

Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis

Abstract: Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning.

A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems

Abstract: Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

Event-Triggered-Based Distributed Cooperative Energy Management for Multienergy Systems

Abstract: This paper investigates the issues of day-ahead and real-time cooperative energy management for multienergy systems formed by many energy bodies. To address these issues, we propose an event-triggered-based distributed algorithm with some desirable features, namely, distributed execution, asynchronous communication, and independent calculation. First, the energy body, seen as both energy supplier and customer, is introduced for system model development. On this basis, energy bodies cooperate with each other to achieve the objective of maximizing the day-ahead social welfare and smoothing out the real-time loads variations as well as renewable resource fluctuations with the consideration of different timescale characteristics between electricity and heat power. To this end, the day-ahead and real-time energy management models are established and formulated as a class of distributed coupled optimization problem by felicitously converting some system coordinates. Such problems can be effectively solved by implementing the proposed algorithm. With the effort, each energy body can determine its owing optimal operations through only local communication and computation, resulting in enhanced system reliability, scalability, and privacy. Meanwhile, the designed communication strategy is event-triggered, which can dramatically reduce the communication among energy bodies. Simulations are provided to illustrate the effectiveness of the proposed models and algorithm.

Multi-Hop Cooperative Computation Offloading for Industrial IoT–Edge–Cloud Computing Environments

Abstract: The concept of the industrial Internet of things (IIoT) is being widely applied to service provisioning in many domains, including smart healthcare, intelligent transportation, autopilot, and the smart grid. However, because of the IIoT devices’ limited onboard resources, supporting resource-intensive applications, such as 3D sensing, navigation, AI processing, and big-data analytics, remains a challenging task. In this paper, we study the multi-hop computation-offloading problem for the IIoT–edge–cloud computing model and adopt a game-theoretic approach to achieving Quality of service (QoS)-aware computation offloading in a distributed manner. First, we study the computation-offloading and communication-routing problems with the goal of minimizing each task's computation time and energy consumption, formulating the joint problem as a potential game in which the IIoT devices determine their computation-offloading strategies. Second, we apply a free–bound mechanism that can ensure a finite improvement path to a Nash equilibrium. Third, we propose a multi-hop cooperative-messaging mechanism and develop two QoS-aware distributed algorithms that can achieve the Nash equilibrium. Our simulation results show that our algorithms offer a stable performance gain for IIoT in various scenarios and scale well as the device size increases.

Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks

Abstract: In this paper, the problem of joint caching and resource allocation is investigated for a network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground users over the LTE licensed and unlicensed bands. The considered model focuses on users that can access both licensed and unlicensed bands while receiving contents from either the cache units at the UAVs directly or via content server-UAV-user links. This problem is formulated as an optimization problem, which jointly incorporates user association, spectrum allocation, and content caching. To solve this problem, a distributed algorithm based on the machine learning framework of liquid state machine (LSM) is proposed. Using the proposed LSM algorithm, the cloud can predict the users’ content request distribution while having only limited information on the network’s and users’ states. The proposed algorithm also enables the UAVs to autonomously choose the optimal resource allocation strategies that maximize the number of users with stable queues depending on the network states. Based on the users’ association and content request distributions, the optimal contents that need to be cached at UAVs and the optimal resource allocation are derived. Simulation results using real datasets show that the proposed approach yields up to 17.8% and 57.1% gains, respectively, in terms of the number of users that have stable queues compared with two baseline algorithms: Q-learning with cache and Q-learning without cache. The results also show that the LSM significantly improves the convergence time of up to 20% compared with conventional learning algorithms such as Q-learning.

Distributed estimation of principal eigenspaces.

Abstract: Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts latent principal factors that contribute to the most variation of the data. When data are stored across multiple machines, however, communication cost can prohibit the computation of PCA in a central location and distributed algorithms for PCA are thus needed. This paper proposes and studies a distributed PCA algorithm: each node machine computes the top K eigenvectors and transmits them to the central server; the central server then aggregates the information from all the node machines and conducts a PCA based on the aggregated information. We investigate the bias and variance for the resulting distributed estimator of the top K eigenvectors. In particular, we show that for distributions with symmetric innovation, the empirical top eigenspaces are unbiased and hence the distributed PCA is "unbiased". We derive the rate of convergence for distributed PCA estimators, which depends explicitly on the effective rank of covariance, eigen-gap, and the number of machines. We show that when the number of machines is not unreasonably large, the distributed PCA performs as well as the whole sample PCA, even without full access of whole data. The theoretical results are verified by an extensive simulation study. We also extend our analysis to the heterogeneous case where the population covariance matrices are different across local machines but share similar top eigen-structures.

Distributed Consensus Based Algorithm for Economic Dispatch in a Microgrid

Abstract: Economic dispatch problem (EDP) is a fundamental optimization problem of power systems. With the penetration of renewable energy sources in microgrids, this paper proposes a distributed algorithm based on consensus theory to solve the EDP with a quadratic cost function. The method takes advantage of the fact that incremental costs need to be equal for all buses at optimal output power values. Thus, the incremental cost of each bus is selected as a consensus variable and the local mismatch between total demand and generation is assigned as a feedback variable to meet demand constraints. Unlike the existing related works, the feedback gains for the feedback variables are different and time-varying. Using multi-parameter perturbation theory and graph theory, the convergence of the algorithm is proved. Furthermore, the upper bounds of the feedback gains are given theoretically, and we obtain that the algorithm converges to the optimal solution at a rate governed by the second largest eigenvalue of the system matrix. Meanwhile, the algorithm is a fully distributed algorithm without a leader or a virtual command node. The simulation results illustrate the effectiveness of the algorithm even though there is a demand change and generator damage. Some simulation results intuitively illustrate how the convergence speed changes with the feedback gains.

Cooperative and Distributed Computation Offloading for Blockchain-Empowered Industrial Internet of Things

Abstract: Offloading computation-intensive blockchain mining tasks to the edge servers (ESs) is a promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the computing capabilities in IIoT are usually limited, whereas the blockchain mining tasks are computationally intensive. However, the computation offloading solutions for data processing tasks and for blockchain mining tasks have been studied separately. Moreover, most of the existing solutions for offloading assume that all IIoT devices can directly connect to the ESs or cloud data centers. To address these issues, in this paper, we propose a multihop cooperative and distributed computation offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT. First, we study the multihop computation offloading problem for both the data processing tasks and the mining tasks to minimize the economic cost of IIoT devices. Second, we formulate the offloading problem as a potential game in which the IIoT devices can make their decisions autonomously and prove the existence of Nash equilibrium (NE) for the game. Third, we design an efficient distributed algorithm based on exchanging messages between IIoT devices to achieve the NE with low computational complexity. Lastly, our experimental results demonstrate that our distributed algorithm scales well as the number of IIoT devices increases and has the minimum system cost compared with other approaches.

Deployment of Unmanned Aerial Vehicle Base Stations for Optimal Quality of Coverage

Abstract: This letter studies the network performance improvement by deploying flying base stations mounted on unmanned aerial vehicles (UAV-BSs) during some occasional events. Specifically, we consider the problem to minimize the average UAV-user distance while keeping the UAV-BSs connected to the stationary base stations. We propose a distributed algorithm which requires only local information and is scalable to a large number of UAV-BSs. We prove its convergence and conduct simulations with a real data set to evaluate its performance.

Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks

Abstract: Anomaly detection has attracted much attention in recent years since it plays a crucial role in many domains. Various anomaly detection approaches have been proposed, among which one-class support vector machine (OCSVM) is a popular one. In practice, data used for anomaly detection can be distributively collected via wireless sensor networks. Besides, as the data usually arrive at the nodes sequentially, online detection method that can process streaming data is preferred. In this paper, we formulate a distributed online OCSVM for anomaly detection over networks and get a decentralized cost function. To get the decentralized implementation without transmitting the original data, we use a random approximate function to replace the kernel function. Furthermore, to find an appropriate approximate dimension, we add a sparse constraint into the decentralized cost function to get another one. Then we minimize these two cost functions by stochastic gradient descent and derive two distributed algorithms. Some theoretical analysis and experiments are performed to show the effectiveness of the proposed algorithms. Experimental results on both synthetic and real datasets reveal that both of the proposed algorithms achieve low misdetection rates and high true positive rates. Compared with other state-of-the-art anomaly detection methods, the proposed distributed algorithms not only show good anomaly detection performance, but also require relatively short running time and low CPU memory consumption.

A Game Theory Based Efficient Computation Offloading in an UAV Network

Abstract: Recently, solutions based on mobile edge computing paradigm have been widely discussed in academia and industry. This paradigm offers solutions to address limitations, in terms of battery lifetime and processing power, of mobile and constrained devices. Despite the ever-increasing capabilities of these devices, resource requirements of applications can often transcend what is available within a single device. Offloading intensive computation tasks to a distant server can help applications reach their desired performances. In this work, we tackle the problem of offloading heavy computation tasks of unmanned aerial vehicles (UAVs) while achieving the best possible tradeoff between energy consumption, time delay, and computation cost. We focus on a scenario of a fleet of small UAVs performing an exploration mission. During their mission, these constrained devices have to carry-out highly intensive computation tasks such as pattern recognition and video preprocessing. We formulate the problem using a non-cooperative theoretical game with N players and three pure strategies. We provide a comprehensive proof for the existence of a Nash equilibrium and implement accordingly a distributed algorithm that converges to such an equilibrium. Extensive simulations are performed in order to provide thorough results and assess the performances of the approach compared to three other models. Results show that our algorithm outperforms all the three approaches. Our approach achieved in average about 19%, 58%, and 55% better results compared to local computing, offloading to the edge server, and offloading to base station, respectively.

Distributed Optimal Economic Dispatch for Microgrids Considering Communication Delays

Abstract: This paper investigates the economic dispatch problem of microgrids in a distributed fashion. To address this issue, a delay-free-based distributed algorithm is presented to optimally assign the whole energy demand among local generation units with the objective of minimizing the agminated operation cost. By implementing the proposed algorithm, each component can find its own optimal operations by only requiring local computation and communication. As a result, it enhances the system robustness, flexibility, privacy, etc. More importantly, the time-varying delays model is considered and embedded into the design of our distributed algorithm, such that the components can employ the delays information to achieve the collaborative operation, which are more general and applicable for practical power systems. In addition, we have proved that the proposed algorithm can converge to the global optimal point under some sufficient conditions. Finally, several simulations are provided to demonstrate the correctness and effectiveness of the proposed algorithm.

Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games

Abstract: In this paper, the distributed Nash equilibrium (NE) searching problem is investigated, where the feasible action sets are constrained by nonlinear inequalities and linear equations. Different from most of the existing investigations on distributed NE searching problems, we consider the case where both cost functions and feasible action sets depend on actions of all players, and each player can only have access to the information of its neighbors. To address this problem, a continuous-time distributed gradient-based projected algorithm is proposed, where a leader-following consensus algorithm is employed for each player to estimate actions of others. Under mild assumptions on cost functions and graphs, it is shown that players’ actions asymptotically converge to a generalized NE. Simulation examples are presented to demonstrate the effectiveness of the theoretical results.

ByRDiE: Byzantine-Resilient Distributed Coordinate Descent for Decentralized Learning

Abstract: Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional learning in fully distributed (also known as decentralized) settings. In this paper, an algorithm termed Byzantine-resilient distributed coordinate descent is developed and analyzed that enables distributed learning in the presence of Byzantine failures. Theoretical analysis (convex settings) and numerical experiments (convex and nonconvex settings) highlight its usefulness for high-dimensional distributed learning in the presence of Byzantine failures.

Distributed matroid-constrained submodular maximization for multi-robot exploration: theory and practice

Abstract: This work addresses the problem of efficient online exploration and mapping using multi-robot teams via a new distributed algorithm for multi-robot exploration, distributed sequential greedy assignment (DSGA), which is based on sequential greedy assignment (SGA). While SGA permits bounds on suboptimality, robots must execute planning steps sequentially. Rather than plan for each robot sequentially as in SGA, DSGA assigns plans to subsets of robots using a fixed number of sequential planning rounds. DSGA retains the same suboptimality bounds as SGA with the addition of a term that describes the additional suboptimality incurred when assigning multiple plans at once. We use this result to extend a single-robot planner based on Monte-Carlo tree search to the multi-robot domain and evaluate the resulting planner in simulated exploration of a confined and cluttered environment. The experimental results show that for teams of 4–32 robots suboptimality due to redundant sensor information introduced in the distributed planning rounds remains small in practice given only two or three distributed planning rounds while providing a 2–8 times speedup over SGA. We also incorporate aerial robots with inter-robot collision constraints and non-trivial dynamics and address subsequent impacts on safety and optimality. Real-time simulation and experimental results for teams of quadrotors demonstrate online planning for multi-robot exploration and indicate that collision constraints have limited impacts on exploration performance.

Balancing Communication and Computation in Distributed Optimization

Abstract: Methods for distributed optimization have received significant attention in recent years owing to their wide applicability in various domains including machine learning, robotics, and sensor networks. A distributed optimization method typically consists of two key components: communication and computation. More specifically, at every iteration (or every several iterations) of a distributed algorithm, each node in the network requires some form of information exchange with its neighboring nodes (communication) and the computation step related to a (sub)-gradient (computation). The standard way of judging an algorithm via only the number of iterations overlooks the complexity associated with each iteration. Moreover, various applications deploying distributed methods may prefer a different composition of communication and computation. Motivated by this discrepancy, in this paper, we propose an adaptive cost framework that adjusts the cost measure depending on the features of various applications. We present a flexible algorithmic framework, where communication and computation steps are explicitly decomposed to enable algorithm customization for various applications. We apply this framework to the well-known distributed gradient descent (DGD) method, and show that the resulting customized algorithms, which we call DGD $^t$ , NEAR-DGD $^t$ , and NEAR-DGD $^+$ , compare favorably to their base algorithms, both theoretically and empirically. The proposed NEAR-DGD $^+$ algorithm is an exact first-order method where the communication and computation steps are nested, and when the number of communication steps is adaptively increased, the method converges to the optimal solution. We test the performance and illustrate the flexibility of the methods, as well as practical variants, on quadratic functions and classification problems that arise in machine learning, in terms of iterations, gradient evaluations, communications, and the proposed cost framework.

Distributed Generalized Nash Equilibrium Seeking Algorithm Design for Aggregative Games Over Weight-Balanced Digraphs

Abstract: In this paper, two aggregative games over weight-balanced digraphs are studied, where the cost functions of all players depend on not only their own decisions but also the aggregate of all decisions. In the first problem, the cost functions of players are differentiable with Lipschitz gradients, and the decisions of all players are coupled by linear coupling constraints. In the second problem, the cost functions are nonsmooth, and the decisions of all players are constrained by local feasibility constraints as well as linear coupling constraints. In order to seek the variational generalized Nash equilibrium (GNE) of the differentiable aggregative games, a continuous-time distributed algorithm is developed via gradient descent and dynamic average consensus, and its exponential convergence to the variational GNE is proven with the help of Lyapunov stability theory. Then, another continuous-time distributed projection-based algorithm is proposed for the nonsmooth aggregative games based on differential inclusions and differentiated projection operations. Moreover, the convergence of the algorithm to the variational GNE is analyzed by utilizing singular perturbation analysis. Finally, simulation examples are presented to illustrate the effectiveness of our methods.

A Pareto-Based Estimation of Distribution Algorithm for Solving Multiobjective Distributed No-Wait Flow-Shop Scheduling Problem With Sequence-Dependent Setup Time

Abstract: Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions such as baker production, parallel computer system, and surgery scheduling. The performance criteria are the makespan and the total weight tardiness. In the MDNWFSP-SDST, several identical factories are considered with the related flow-shop scheduling problem with no-wait constraints. For solving the MDNWFSP-SDST, a Pareto-based estimation of distribution algorithm (PEDA) is presented. Three probabilistic models including the probability of jobs in empty factory, two jobs in the same factory, and the adjacent jobs are constructed. The PWQ heuristic is extended to the distributed environment to generate initial individuals. A sampling method with the referenced template is presented to generate offspring individuals. Several multiobjective neighborhood search methods are developed to optimize the quality of solutions. The comparison results show that the PEDA obviously outperforms other considered multiobjective optimization algorithms for addressing MDNWFSP-SDST. Note to Practitioners —This paper is motivated by the process cycles in multiproduction factories (or lines) of baker production, surgery scheduling, and parallel computer systems. In these process cycles, jobs are assigned to multiproduction factories (or lines), and no interruption exists between consecutive operations. This paper models this process as a multiobjective distributed no-wait flow-shop scheduling with SDST. Scheduling becomes more challenging when facing distributed factories. This paper provides an estimation of distributed algorithm with Pareto dominate concept which uses a probabilistic model to generate offspring. Experiment results suggest that the proposed algorithm can find superior solutions of large-scale instances. This scheduling model can be extended to practical problems by considering other constraints, such as assembly process, mixed no-wait, and transporting times. Besides, the proposed algorithm can be applied to solve other distributed scheduling problems and industrial cases, once their constraints are known, i.e., the processing time of operations, the setup time of machines.

Challenges, Designs, and Performances of a Distributed Algorithm for Minimum-Latency of Data-Aggregation in Multi-Channel WSNs

Abstract: In wireless sensor networks (WSNs), the sensed data by sensors need to be gathered, so that one very important application is periodical data collection. There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency. Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system , in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any (or no) data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems , since each sensor’s knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. This is a formidable motivation for us to investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio, termed the minimum-latency collision-avoidance multiple-data-aggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we provide the theoretical analyses of DCAS and conduct extensive simulations to demonstrate the performance of DCAS.

A Method for Optimized Deployment of Unmanned Aerial Vehicles for Maximum Coverage and Minimum Interference in Cellular Networks

Abstract: We focus on the problem of deploying unmanned aerial vehicles to service mobile users in a cellular network, with the aim of maximizing coverage and reducing interference effects. An optimization model that is distributed in nature is proposed and a maximizing algorithm is developed to find a locally optimal solution. The performance of this distributed algorithm is shown to be superior in quality and solution time to a standard greedy algorithm. Testing on a simulation of a practical scenario is performed to demonstrate the application of the method to real scenarios as well as to illustrate the tradeoff between maximizing coverage and minimizing interference.

Distributed Energy Management for Multiuser Mobile-Edge Computing Systems With Energy Harvesting Devices and QoS Constraints

Abstract: Mobile-edge computing (MEC) has evolved as a promising technology to alleviate the computing pressure of mobile devices by offloading computation tasks to MEC server. Energy management is challenging since the unpredictability of the energy harvesting (EH) and the quality of service (QoS). In this paper, we investigate the problem of power consumption in a multiuser MEC system with EH devices. The system power consumption, which includes the local execution power and the offloading transmission power, is designated as the main system performance index. First, we formulate the power consumption minimization problem with the battery queue stability and QoS constraints as a stochastic optimization programming, which is difficult to solve due to the time-coupling constraints. Then, we adopt the Lyapunov optimization approach to tackle the problem by reformulating it into a problem with relaxed queue stability constraints. We design an online algorithm based on the Lyapunov optimization method, which only uses current states of the mobile users and does not depend on the system statistic information. Furthermore, we propose a distributed algorithm based on the alternating direction method of multipliers to reduce the system computational complexity. We prove the optimality of the online algorithm and the distributed algorithm using rigorous theoretical analysis. Finally, we perform extensive trace-simulations to verify the theoretical results and evaluate the effectiveness of the proposed algorithms.

Distributed Energy Management for Smart Grids With an Event-Triggered Communication Scheme

Abstract: This paper is concerned with distributed energy management and control issues of both generators and loads. It aims to maximize the total social welfare that balances generation-side expanses, user-side payments, and transmission line costs. A distributed control strategy with continuous information exchange among neighbors is first proposed. It is shown that this distributed algorithm achieves the global optimal power outputs on generators and the optimal electricity usage on loads asymptotically. To reduce communication resource consumptions, the distributed optimization algorithm is further expanded to incorporate event-triggered communication and control mechanism. In this new algorithm, an event-triggering condition for each generator and each load is employed to determine when its related state information should be sampled and transmitted to its neighbors. Compared with the standard periodic sampling and communication schemes, this new distributed and event-triggered algorithm can significantly reduce communication data flows while achieving the nearly identical control performance to that under continuous data communications. The theoretical results of this paper are validated by using a simulation case study with distributed generators and multiple loads on an IEEE 9-bus system.

Joint Multi-User Computation Offloading and Data Caching for Hybrid Mobile Cloud/Edge Computing

Abstract: In this paper, we investigate a hybrid mobile cloud/edge computing system with coexistence of centralized cloud and mobile edge computing, which enables computation offloading and data caching to improve the performance of users. Computation offloading and data caching decisions are jointly optimized to minimize the total execution delay at the mobile user side, while satisfying the constrains in terms of the maximum tolerable energy consumption of each user, the computation capability of each MEC server, and the cache capacity of each access point (AP). The formulated problem is non-convex and challenging because of the highly coupled decision variables. To address such an untractable problem, we first transform the original problem into an equivalent convex one by McCormick envelopes and introducing auxiliary variables. To the end, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM), which can achieve near optimal computation offloading and data caching decisions. The proposed algorithm has lower computational complexity compared to the centralized algorithm. Simulation results are presented to verify that the proposed algorithm can effectively reduce computing delay for end users while ensuring the performance of each user.

A Distributed Online Pricing Strategy for Demand Response Programs

Abstract: We study a demand response (DR) problem from utility (also referred to as operator)’s perspective with realistic settings, in which the utility faces uncertainty and limited communication. Specifically, the utility does not know the cost function of consumers and cannot have multiple rounds of information exchange with consumers. We formulate an optimization problem for the utility to minimize its operational cost considering time-varying DR targets and responses of consumers. We develop a joint online learning and pricing algorithm. In each time slot, the utility sends out a price signal to all consumers and estimates the cost functions of consumers based on their noisy responses. We measure the performance of our algorithm using regret analysis and show that our online algorithm achieves logarithmic regret with respect to the operating horizon. In addition, our algorithm employs linear regression to estimate the aggregate response of consumers, making it easy to implement in practice. Simulation experiments validate the theoretic results and show that the performance gap between our algorithm and the offline optimality decays quickly.

Distributed Online Voltage Control in Active Distribution Networks Considering PV Curtailment

Abstract: In this paper, we propose a distributed online voltage control algorithm for distribution networks with multiple photovoltaic (PV) systems based on dual-ascent method. Conventional distributed algorithms implement voltage control only when the algorithms converge. However, our proposed algorithm is able to carry out voltage control immediately. In particular, we derive a closed-form solution for PV controllers to locally update the active and reactive power set points aiming at minimizing the total loss and maintaining bus voltages within the acceptable ranges. The optimality is guaranteed and the convergence is established analytically. Moreover, our proposed algorithm only requires the information exchange between neighboring PV systems, thus reducing communication complexity. Finally, numerical tests on IEEE 37-bus distribution system verify the effectiveness and robustness of our proposed algorithm.

Electric Vehicle Charge Scheduling Mechanism to Maximize Cost Efficiency and User Convenience

Abstract: This paper investigates the fee scheduling problem of electric vehicles (EVs) at the micro-grid scale. This problem contains a set of charging stations controlled by a central aggregator. One of the main stakeholders is the operator of the charging stations, who is motivated to minimize the cost incurred by the charging stations, while the other major stakeholders are vehicle owners who are mostly interested in user convenience, as they want their EVs to be fully charged as soon as possible. A bi-objective optimization problem is formulated to jointly optimize two factors that correspond to these stakeholders. An online centralized scheduling algorithm is proposed and proven to provide a Pareto-optimal solution. Moreover, a novel low-complexity distributed algorithm is proposed to reduce both the transmission data rate and the computation complexity in the system. The algorithms are evaluated through simulation, and results reveal that the charging time in the proposed method is 30% less than that of the compared methods proposed in the literature. The data transmitted by the distributed algorithm is 33.25% lower than that of a centralized one. While the performance difference between the centralized and distributed algorithms is only 2%, the computation time shows a significant reduction.