Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless device...
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Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint dynamic assignment of computational tasks to slices, management of radio resources across slices and management of radio and computing resources within slices. We formulate the Joint Slice Selection and Edge Resource Management (JSS-ERM) problem as a mixed-integer problem with the objective to minimize the completion time of computational tasks. We show that the JSS-ERM problem is NP-hard and develop an approximation algorithm with bounded approximation ratio based on a game theoretic treatment of the problem. We use extensive simulations to provide insight into the performance of the proposed solution from the perspective of the whole system and from the perspective of individual slices. Our results show that the proposed slicing policy can achieve significant gains compared to the equal slicing policy, and that the computational complexity of the proposed task placement algorithm is approximately linear in the number of devices.
Mobile target tracking is crucial in various applications such as surveillance and autonomous navigation. This study presents a decentralized tracking framework utilizing a Consensus-Based Estimation Filter (CBEF) int...
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Change-Point Detection (CPD) has been researched for a long time in statistical signal processing, and has been widely used in many applications such as monitoring and anomaly detection. When detecting over networks, ...
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Change-Point Detection (CPD) has been researched for a long time in statistical signal processing, and has been widely used in many applications such as monitoring and anomaly detection. When detecting over networks, existing algorithms require sensors to exchange information via transmitting full-precision variables, which requires large bandwidth and consumes a large amount of energy. To address these problems, we explore the idea of quantizing data with low precision without deteriorating the detection performance. Specifically, considering the strong autocorrelation of the transmitted sequence in time domain and the accumulation of quantization error due to the autocorrelation, we design a scheme that combines the differential quantization technique and the error feedback technique. Based on this scheme, we propose a communication-efficient decentralized CPD algorithm named Quantized CUSUM (Q-CUSUM), which only requires small bandwidth and has low energy consumption. We theoretically analyze two commonly-used detection criteria, namely Average Run Length (ARL) and Expected Detection Delay (EDD), and compute their theoretical bounds. It is proved that the detection performance is robust to the quantization error. The proposed quantization scheme can be easily extended to the partial-communication case, where at each time only a subset of sensor pairs are allowed to exchange information. Experiments are implemented on both synthetic and real-world datasets. The results indicate that the proposed quantization scheme can effectively reduce the communication cost without deteriorating the detection performance.
We study task assignment in online service platforms, where unlabeled clients arrive according to a stochastic process and each client brings a random number of tasks. As tasks are assigned to servers, they produce cl...
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We study task assignment in online service platforms, where unlabeled clients arrive according to a stochastic process and each client brings a random number of tasks. As tasks are assigned to servers, they produce client/server-dependent random payoffs. The goal of the system operator is to maximize the expected payoff per unit time subject to the servers' capacity constraints. However, both the statistics of the dynamic client population and the client-specific payoff vectors are unknown to the operator. Thus, the operator must design task-assignment policies that integrate adaptive control (of the queueing system) with online learning (of the clients' payoff vectors). A key challenge in such integration is how to account for the nontrivial closed-loop interactions between the queueing process and the learning process, which may significantly degrade system performance. We propose a new utility-guided online learning and task assignment algorithm that seamlessly integrates learning with control to address such difficulty. Our analysis shows that, compared with an oracle that knows all client dynamics and payoff vectors beforehand, the gap of the expected payoff per unit time of our proposed algorithm can be analytically bounded by three terms, which separately capture the impact of the client-dynamic uncertainty, client-server payoff uncertainty, and the loss incurred by backlogged clients in the system. Further, our bound holds for any finite time horizon. Through simulations, we show that our proposed algorithm significantly outperforms a myopic-matching policy and a standard queue-length-based policy that does not explicitly address the closed-loop interactions between queueing and learning.
In multi-agent systems, multi-agent planning and diagnosis are two key subfields - multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multiagent diagnosis appro...
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ISBN:
(纸本)9798400708480
In multi-agent systems, multi-agent planning and diagnosis are two key subfields - multi-agent planning approaches identify plans for the agents to execute in order to reach their goals, and multiagent diagnosis approaches identify root causes for faults when they occur, typically by using information from the multi-agent planning model as well as the resulting multi-agent plan. However, when a plan fails during execution, the cause can often be related to some commonsense information that is neither explicitly encoded in the planning nor diagnosis problems. As such existing diagnosis approaches fail to accurately identify the root causes in such situations. To remedy this limitation, we extend the Multi-Agent STRIPS problem (a common multi-agent planning framework) to a Commonsense Multi-Agent STRIPS model, which includes commonsense fluents and axioms that may affect the classical planning problem. We show that a solution to a (classical) Multi-Agent STRIPS problem is also a solution to the commonsense variant of the same problem. Then, we propose a decentralized multi-agent diagnosis algorithm, which uses the commonsense information to diagnose faults when they occur during execution. Finally, we demonstrate the feasibility and promise of this approach on several key multiagent planning benchmarks.
Traditional techniques for marine life tracking use stationary receivers that detect and obtain measurements from tagged animals. Recently, such static systems have been replaced by multiple mobile robots, e.g., auton...
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ISBN:
(纸本)9781450395175
Traditional techniques for marine life tracking use stationary receivers that detect and obtain measurements from tagged animals. Recently, such static systems have been replaced by multiple mobile robots, e.g., autonomous underwater vehicles (AUVs), equipped with omni-directional hydrophones that can accurately localize marine life. In this paper, the application of homogeneous multi-AUV systems to track and localize marine life is used as a motivating example to develop new MRMP (Multi-Robot Motion Planning) algorithms. These algorithms generate trajectories that maximize a new fitness function that incorporates 1) probabilistic motion models generated from historical data of live sharks, and 2) ideal AUV formations for observing a shark from multiple sensor vantage points. The two expansive RRT variants, named Independent State Expansion (ISE) planning and Joint State Expansion (JSE) planning, differ in how new samples are randomly generated during the algorithm's random search. The fitness function was developed to quantify how accurately the positioning of AUVs would trilaterate the target animal. Through simulation, it was found that the Joint planner was 70% faster with respect to run time than Independent planner, while both could produce similar mean fitness function values. The fitness for these variants was also measured for simulations where different target motion models were used when calculating the fitness function, highlighting the improved performance when using actual target motion motion models.
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base st...
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Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we consider non-cooperative multi-UAV scenarios, in which multiple UAVs need to fly from initial locations to destinations, while satisfying collision avoidance, wireless connectivity, and kinematic constraints. We aim to find trajectories for the UAVs with the goal to minimize their mission completion time. We first formulate the multi-UAV trajectory optimization problem as a sequential decision making problem. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is developed to obtain values given the agent's joint state (including the agent's information, the nearby agents' observable information, and the locations of the nearby GBSs). A signal-to-interference-plus-noise ratio (SINR)-prediction neural network is also designed, using accumulated SINR measurements obtained when interacting with the cellular network, to map the GBSs' locations into the SINR levels in order to predict the UAV's SINR. Numerical results show that with the value network and SINR-prediction network, real-time navigation for multi-UAVs can be efficiently performed in various environments with high success rate.
This letter proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions that combine locally evaluable costs with network-wide coupling component...
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This letter proposes decentralized resource-aware coordination schemes for solving network optimization problems defined by objective functions that combine locally evaluable costs with network-wide coupling components. These methods are well suited for a group of supervised agents trying to solve an optimization problem under mild coordination requirements. Each agent has information on its local cost and coordinates with the network supervisor for information about the coupling term of the cost. The proposed approach is feedback-based and asynchronous by design, guarantees anytime feasibility, and ensures the asymptotic convergence of the network state to the desired optimizer. Numerical simulations on a power system example illustrate our results.
We study dynamic decision-making problems in networks and markets under uncertainty about future payoffs. This problem is difficult in general since 1) Although the current decision (potentially) affects future decisi...
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We study dynamic decision-making problems in networks and markets under uncertainty about future payoffs. This problem is difficult in general since 1) Although the current decision (potentially) affects future decisions, the decision-maker does not have exact information on the future payoffs when he/she commits to the current decision; 2) The decision made at one part of the network usually interacts with the decisions made at the other parts of the network, which makes the computation scales very fast with the network size and brings computational challenges in practice. In this thesis, we propose computationally efficient methods to solve dynamic optimization problems on markets and networks, specify a general set of conditions under which the proposed methods give theoretical guarantees on global near-optimality, and further provide numerical studies to verify the performance empirically. The proposed methods/algorithms have a general theme as “local algorithms”, meaning that the decision at each node/agent on the network uses only partial information on the network. In the first part of this thesis, we consider a network model with stochastic uncertainty about future payoffs. The network has a bounded degree, and each node takes a discrete decision at each period, leading to a per-period payoff which is a sum of three parts: node rewards for individual node decisions, temporal interactions between individual node decisions from the current and previous periods, and spatial interactions between decisions from pairs of neighboring nodes. The objective is to maximize the expected total payoffs over a finite horizon. We study a natural decentralized algorithm (whose computational requirement is linear in the network size and planning horizon) and prove that our decentralized algorithm achieves global near-optimality when temporal and spatial interactions are not dominant compared to the randomness in node rewards. decentralized algorithms are parameterized by the
In this letter, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level variance reduction to remove the uncertai...
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In this letter, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level variance reduction to remove the uncertainty caused by stochastic gradients, network-level gradient tracking to address the distributed nature of the data, and push-sum consensus to tackle directed information exchange. We show that Push-SAGA achieves linear convergence to the exact solution for smooth and strongly convex problems and is thus the first linearly-convergent stochastic algorithm over arbitrary strongly connected directed graphs. We also characterize the regime in which Push-SAGA achieves a linear speed-up compared to its centralized counterpart and achieves a network-independent convergence rate. We illustrate the behavior and convergence properties of Push-SAGA with the help of numerical experiments on strongly convex and non-convex problems.
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