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Register a new userDistributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm
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Added by
Sheng Zhou
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In a vehicular edge computing (VEC) system, vehicles can share their surplus computation resources to provide cloud computing services. The highly dynamic environment of the vehicular network makes it challenging to guarantee the task offloading delay. To this end, we introduce task replication to the VEC system, where the replicas of a task are offloaded to multiple vehicles at the same time, and the task is completed upon the first response among replicas. First, the impact of the number of task replicas on the offloading delay is characterized, and the optimal number of task replicas is approximated in closed-form. Based on the analytical result, we design a learning-based task replication algorithm (LTRA) with combinatorial multi-armed bandit theory, which works in a distributed manner and can automatically adapt itself to the dynamics of the VEC system. A realistic traffic scenario is used to evaluate the delay performance of the proposed algorithm. Results show that, under our simulation settings, LTRA with an optimized number of task replicas can reduce the average offloading delay by over 30% compared to the benchmark without task replication, and at the same time can improve the task completion ratio from 97% to 99.6%.
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This letter studies an ultra-reliable low latency communication problem focusing on a vehicular edge computing network in which vehicles either fetch and synthesize images recorded by surveillance cameras or acquire the synthesized image from an edge computing server. The notion of risk-sensitive in financial mathematics is leveraged to define a reliability measure, and the studied problem is formulated as a risk minimization problem for each vehicles end-to-end (E2E) task fetching and offloading delays. Specifically, by resorting to a joint utility and policy estimation-based learning algorithm, a distributed risk-sensitive solution for task fetching and offloading is proposed. Simulation results show that our proposed solution achieves performance improvements up to 40% variance reduction and steeper distribution tail of the E2E delay over an averaged-based baseline.
Vehicular edge computing (VEC) is envisioned as a promising approach to process the explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates power to process partial tasks through offloading and the remaining tasks through local execution. During the offloading, each VU adopts the multi-input multi-out and non-orthogonal multiple access (MIMO-NOMA) channel to improve the channel spectrum efficiency and capacity. However, the channel condition is uncertain due to the channel interference among VUs caused by the MIMO-NOMA channel and the time-varying path-loss caused by the mobility of each VU. In addition, the task arrival of each VU is stochastic in the real world. The stochastic task arrival and uncertain channel condition affect greatly on the power consumption and latency of tasks for each VU. It is critical to design an optimal power allocation scheme considering the stochastic task arrival and channel variation to optimize the long-term reward including the power consumption and latency in the MIMO-NOMA VEC. Different from the traditional centralized deep reinforcement learning (DRL)-based scheme, this paper constructs a decentralized DRL framework to formulate the power allocation optimization problem, where the local observations are selected as the state. The deep deterministic policy gradient (DDPG) algorithm is adopted to learn the optimal power allocation scheme based on the decentralized DRL framework. Simulation results demonstrate that our proposed power allocation scheme outperforms the existing schemes.
Master-worker distributed computing systems use task replication in order to mitigate the effect of slow workers, known as stragglers. Tasks are grouped into batches and assigned to one or more workers for execution. We first consider the case when the batches do not overlap and, using the results from majorization theory, show that, for a general class of workers service time distributions, a balanced assignment of batches to workers minimizes the average job compute time. We next show that this balanced assignment of non-overlapping batches achieves lower average job compute time compared to the overlapping schemes proposed in the literature. Furthermore, we derive the optimum redundancy level as a function of the service time distribution at workers. We show that the redundancy level that minimizes average job compute time is not necessarily the same as the redundancy level that maximizes the predictability of job compute time, and thus there exists a trade-off between optimizing the two metrics. Finally, by running experiments on Google cluster traces, we observe that redundancy can reduce the compute time of the jobs in Google clusters by an order of magnitude, and that the optimum level of redundancy depends on the distribution of tasks service time.
In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high. In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.
Vehicular Cloud Computing (VCC) is a new technological shift which exploits the computation and storage resources on vehicles for computational service provisioning. Spare on-board resources are pooled by a VCC operator, e.g. a roadside unit, to complete task requests using the vehicle-as-a-resource framework. In this paper, we investigate timely service provisioning for deadline-constrained tasks in VCC systems by leveraging the task replication technique (i.e., allowing one task to be executed by several server vehicles). A learning-based algorithm, called DATE-V (Deadline-Aware Task rEplication for Vehicular Cloud), is proposed to address the special issues in VCC systems including uncertainty of vehicle movements, volatile vehicle members, and large vehicle population. The proposed algorithm is developed based on a novel Contextual-Combinatorial Multi-Armed Bandit (CC-MAB) learning framework. DATE-V is `contextual because it utilizes side information (context) of vehicles and tasks to infer the completion probability of a task replication under random vehicle movements. DATE-V is `combinatorial because it aims to replicate the received task and send the task replications to multiple server vehicles to guarantee the service timeliness. We rigorously prove that our learning algorithm achieves a sublinear regret bound compared to an oracle algorithm that knows the exact completion probability of any task replications. Simulations are carried out based on real-world vehicle movement traces and the results show that DATE-V significantly outperforms benchmark solutions.
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