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Register a new userEnergy-Efficient Task Offloading Under E2E Latency Constraints
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Added by
Sina Ebrahimi
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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In this paper, we propose a novel resource management scheme that jointly allocates the transmit power and computational resources in a centralized radio access network architecture. The network comprises a set of computing nodes to which the requested tasks of different users are offloaded. The optimization problem minimizes the energy consumption of task offloading while takes the end-to-end latency, i.e., the transmission, execution, and propagation latencies of each task, into account. We aim to allocate the transmit power and computational resources such that the maximum acceptable latency of each task is satisfied. Since the optimization problem is non-convex, we divide it into two sub-problems, one for transmit power allocation and another for task placement and computational resource allocation. Transmit power is allocated via the convex-concave procedure. In addition, a heuristic algorithm is proposed to jointly manage computational resources and task placement. We also propose a feasibility analysis that finds a feasible subset of tasks. Furthermore, a disjoint method that separately allocates the transmit power and the computational resources is proposed as the baseline of comparison. A lower bound on the optimal solution of the optimization problem is also derived based on exhaustive search over task placement decisions and utilizing Karush-Kuhn-Tucker conditions. Simulation results show that the joint method outperforms the disjoint method in terms of acceptance ratio. Simulations also show that the optimality gap of the joint method is less than 5%.
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To overcome devices limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are re-associated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale while a dynamic task offloading and resource allocation policy is executed in the short timescale. Simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly-reliable task computation and lower delay performance, compared to several baselines.
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The 5G Internet of Vehicles has become a new paradigm alongside the growing popularity and variety of computation-intensive applications with high requirements for computational resources and analysis capabilities. Existing network architectures and resource management mechanisms may not sufficiently guarantee satisfactory Quality of Experience and network efficiency, mainly suffering from coverage limitation of Road Side Units, insufficient resources, and unsatisfactory computational capabilities of onboard equipment, frequently changing network topology, and ineffective resource management schemes. To meet the demands of such applications, in this article, we first propose a novel architecture by integrating the satellite network with 5G cloud-enabled Internet of Vehicles to efficiently support seamless coverage and global resource management. A incentive mechanism based joint optimization problem of opportunistic computation offloading under delay and cost constraints is established under the aforementioned framework, in which a vehicular user can either significantly reduce the application completion time by offloading workloads to several nearby vehicles through opportunistic vehicle-to-vehicle channels while effectively controlling the cost or protect its own profit by providing compensated computing service. As the optimization problem is non-convex and NP-hard, simulated annealing based on the Markov Chain Monte Carlo as well as the metropolis algorithm is applied to solve the optimization problem, which can efficaciously obtain both high-quality and cost-effective approximations of global optimal solutions. The effectiveness of the proposed mechanism is corroborated through simulation results.
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