Energy-efficient Task Assignment Research Articles

Communication-Aware Energy Consumption Model in Heterogeneous Computing Systems

Abstract Large heterogeneous computing systems are composed of conventional central processing units and graphics processing units (GPUs) where communication plays a crucial role for system performance. This paper presents an energy consumption analytical model in terms of communication perception for the communication–computing pipeline characterization of discrete GPUs systems. We propose a dynamically adaptive energy-efficient task assignment approach, which harnesses particle swarm optimization. Static energy optimization is addressed by optimal task partition granularity. The experimental results demonstrate that the communication-based energy optimization algorithms can be more energy-saving than those without communication consideration. For some application benchmarks, the energy consumption can be saved by up to 31%. This implies the potential that the energy-saving optimization methods can be incorporated in system engineering processes.

A Socially-Aware Hybrid Computation Offloading Framework for Multi-Access Edge Computing

Computation offloading manages resource-intensive and mobile collaborative applications (MCA) on mobile devices where much processing is replicated with multiple users in the same environment. In this article, we propose a novel hybrid multicast-based task execution framework for multi-access edge computing (MEC), where a crowd of mobile devices at the network edge leverage network-assisted device-to-device (D2D) collaboration for wireless distributed computing (MDC) and outcome sharing. The framework is socially aware in order to build effective D2D links. A key objective of this framework is to achieve an energy-efficient task assignment policy for mobile users. Specifically, we first introduce the socially aware hybrid computation offloading (SAHCO) system model, which combines of MEC offloading and D2D offloading in detail. Then, we formulate the energy-efficient task assignment problem by taking into account the necessary constraints. We next propose a Monte Carlo Tree Search based algorithm, named, TA-MCTS for the task assignment problem. Simulation results show that compared to four alternative benchmark solutions in literature, our proposal can reduce energy consumption up to 45.37 percent.

Energy-Efficient Multimedia Task Assignment and Computing Offloading for Mobile Edge Computing Networks

With the rapid development of 5G technology in recent years, multimedia communication services, such as live online and short video, have received wide attention and become the important means of people's daily social intercourse. However, the rapid growth of multimedia communication demands pose greater challenges to both the wireless network communication capacity and the network processing capacity. Mobile Edge Computing (MEC) is widely regarded as a promising technology to cope with the above challenges. To satisfy the growing demands and improve the quality of experience for users, it is in urge need to seek the effective and efficient task assignment and computing offloading strategy for MEC networks. In this paper, we focused on the multimedia services which need to be processed, uploaded and shared in the network and research the long-term task assignment and resource coordination problem. We formulate the optimization problem as a stochastic optimization problem with the aim of the minimizing the time-average energy consumption of the system. By using the Lyapunov optimization technique, we decompose the original problem into several subproblems which can be solved with current system information and low computational complexities. On this basis, we propose an online energy-efficient task assignment and computing offloading strategy to adaptively decide the task assignment, coordinate and optimize the wireless and computation resource allocation by taking the dynamic wireless condition and service delay constraints into consideration. Extensive simulation results show that our proposed algorithm can achieve considerate energy consumption and delay performances under different conditions.

A Dynamic Application-Partitioning Algorithm with Improved Offloading Mechanism for Fog Cloud Networks

This paper aims to propose a new fog cloud architecture that performs a joint energy-efficient task assignment (JEETA). The proposed JEETA architecture utilizes the dynamic application-partitioning algorithm (DAPTS), a novel algorithm that efficiently decides and switches the task to be offloaded or not in heterogeneous environments with minimal energy consumption. The proposed scheme outperforms baseline approaches such as MAUI, Think Air and Clone Cloud in many performance aspects. Results show that for the execution of 1000 Tasks on fog, mobile offloaded nodes, JEETA consumes the leas, i.e., 23% of the total energy whereas other baseline approaches consume in between 50%–100% of the total energy. Results are validated via real test-bed experiments and trice are driven efficient simulations.

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale–Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

Energy Efficient Task Assignment with Guaranteed Probability Satisfying Timing Constraints for Embedded Systems

The trade-off between system performance and energy efficiency (service time) is critical for battery-based embedded systems. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments (e.g., fluctuant network bandwidth and different user inputs). By adopting a probabilistic approach, this paper proposes a model and a set of algorithms to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with a guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task. Finally, to escape from local optima, a local search with restarts searches the optimal solution from candidate solutions by updating the objective function, until the stop criteria are reached or a time bound is elapsed. The experimental results demonstrate that for probability 1.0, our approach yields slightly better results than the well-known algorithms like ASAP/ALAP (As Soon As Possible/As Late As Possible) and ILP (Integer Linear Programming) with/without DVS (Dynamic Voltage Scaling). However, for probabilities 0.8 and 0.9, our approach significantly outperforms those algorithms (maximum improvement of 50.3 percent).