A New Allocation Scheme for Parallel Applications with Deadline and Security Constraints on Clusters

Parallel applications with energy and low-latency constraints are emerging in various networked embedded systems like digital signal processing, vehicle tracking, and infrastructure monitoring. However, conventional energy-driven task allocation schemes for a cluster of embedded nodes only concentrate on energy-saving when making allocation decisions. Consequently, the length of the schedules could be very long, which is unfavorable or in some situations even not tolerated. In this paper, we address the issue of allocating a group of parallel tasks on a heterogeneous embedded system with an objective of energy-saving and short-latency. A novel task allocation strategy, or BEATA (Balanced Energy- Aware Task Allocation), is developed to find an optimal allocation that minimizes overall energy consumption while confining the length of schedule to an ideal range. Experimental results show that BEATA significantly improves the performance of embedded systems in terms of energy-saving and schedule length over an existing allocation scheme.

Fog computing has emerged as a potential solution to accommodate the explosive computational demand of mobile users. This potential mainly stems from the capacity of task offloading and allocation at the network edge, reducing the delay and improving the quality of service. However, optimizing the performance of a fog network is often challenging, when consider the distinct abilities and capacities of fog nodes. We study the distributed task allocation problem in such a heterogeneous fog computing network under noises. We formulate the problem as a social-concave game, where the players attempt to minimize their regret while converging to Nash equilibrium. We develop a no-regret strategy for task allocation. The strategy, namely bandit gradient ascent with momentum, is an online convex optimization algorithm with bandit feedback. Theoretical and numerical analysis show the superior performance of the proposed strategy for efficient task allocation compared to the state-of-the-art methods.

In order to minimize the processing time for multiple robots to finish lots of tasks, an optimized multiple robots task allocation and path planning approach is proposed. In this approach, the optimized task sequence is first schemed on the assumption that all the tasks are processed by one robot. While scheming the task sequence. A star algorithm is employed to plan paths and calculate the path cost. Then, according to the number of the robots, the task sequence has been randomly divided into several task segments that will be assigned to robots. At last, the task number of each task segments are averaged according to the time each robot used, so that the optimized task allocation scheme is got. In this approach, not only multiple robot task allocation, but also the optimized path planning is realized to ensure that the robots can finish all the tasks in the shortest time. The experiments and simulation are provided to validate the effectiveness of the approach. The results show that the proposed approach can scheme optimized multiple robots task allocation and path planning scheme.

In radar networks, research on the task allocation strategy for multi-target imaging is essential, especially under limited radar resources. From the targets' perspective, the goal is to acquire enough resources to obtain high-quality inverse synthetic aperture radar (ISAR) images. Then there is competition among targets for radar resources. Therefore, the task allocation optimization problem is considered as a cooperative game, specifically a non-transferable coalitional game when the sparse ISAR imaging algorithm is adopted. A stable coalition structure, which allows cooperation among targets to achieve satisfactory image resolution using the least amount of time, represents an optimal task allocation scheme. Then, the corresponding algorithm based on coalition-forming rules is proposed. Through the framework of cooperative game theory, a stable network structure (i.e., the task allocation strategy) is formed while minimizing the radar time resource cost. Finally, experiments confirm that the proposed algorithm can achieve an optimal task allocation strategy with a lower computation time cost.

Mobile cloud computing is a powerful technology for supporting the growing demand of mobile applications. Unfortunately, existing mobile cloud service architectures are imperfect since mobile devices are not considered as the available resources. In this paper, a general framework is proposed that regarding mobile devices as resources by introducing proxy. Differs from other architectures, this framework is the cost-aware that can select appropriate task allocation strategy based on user's demands. The possible solutions of key technologies are also discussed, including reliability measure and task allocation strategy. Particularly, a suitable method is proposed that combine statistical-based with group-based method for reliability measure, and the allocation strategy with cost-aware for distributing mobile resources evenly is also designed.

In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case.

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

Edge computing for machine learning has become a heated research topic. On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, one obstacle is that transfer learning imposes heavy computation burden to the resource-constrained edge devices. Motivated by the fact that only a few tasks of Multi-task Transfer Learning (MTL) have a higher potential for overall decision performance improvement, we design a novel task allocation scheme, which assigns more important tasks to more powerful edge devices to maximize the overall decision performance. In this paper, we focus on task allocation under multi-task scenarios by introducing task importance and make the following contributions. First, we reveal that it is important to measure the impact of tasks on overall decision performance improvement and quantify task importance. We also observe the long-tail property of task importance, i.e., only a few tasks are important, which facilitates more efficient task allocation. Second, we show that task allocation with task importance for MTL (TATIM) is in fact a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we innovatively propose a Data-driven Cooperative Task Allocation (DCTA) approach. Third, we evaluate the performance of our DCTA approach by applying it to a real-world industrial operation (e.g., AIOps) scenario. Experiments show that our DCTA approach can reduce 3.24 times of processing time compared with the state-of-the-art when solving TATIM. We offer our DCTA approach as an effective and practical mechanism for reducing the required resource associated with performing MTL on edge devices.

In this article, we propose a reactive task allocation architecture for a multi-agent system, for scenarios where the tasks arrive at random times and are grouped into multiple queues. Two-stage tasks are considered, where every task has a beginning, an intermediate and a final part, typical in pick-and-drop and inspect-and-report scenarios. A centralized auction-based task allocation system is proposed, where an auction system considers bids submitted by the agents for individual tasks, current length of the queues and the waiting times of the tasks in the queues, to decide on a task allocation strategy. The costs associated with these considerations, along with the constraints of having unique mappings between tasks and agents and constraints on the maximum number of agents that can be assigned to a queue, results in a Linear Integer Program (LIP) that is solved using the SCIP solver. For the scenario where the queue lengths are penalized but not the waiting times, we demonstrate that the auction system allocates tasks in a manner that all the queue lengths become constant, which is termed balancing. For the scenarios where both costs are considered, we qualitatively analyse the effect of the choice of the relative weights on the resulting task allocation and provide guidelines for the choice of the weights. We present simulation results that illustrate the balanced allocation of tasks and validate the analysis for the trade-off between the costs related to queue lengths and task waiting times.

Mobile crowdsensing (MCS) refers to the process in which a collection of mobile devices share sensing data, which can then be aggregated to develop policies or applications that benefit a community. Traditionally, mobile crowdsensing has been deployed using a platform-centric task allocation approach. These platform-centric systems are primarily concerned with gaining profits for the platform by minimizing rewards given to users. Instead of this approach, this paper focuses on a more altruistic task allocation approach by proposing a user-centric task allocation approach. In this regard, we have developed two task allocation schemes: Nearest User Task Allocation (NUTA) and Nearest User Fair Task Allocation (NUFTA) in order to facilitate an optimized and equitable workload for the users.

In this paper, we propose a novel multi-objective particle swarm optimization algorithm with a task allocation and archive-guided mutation strategy (TAMOPSO), which effectively solves the problem of inefficient search in traditional algorithms by assigning different evolutionary tasks to particles with different characteristics. First, TAMOPSO divides multiple subpopulations according to the particle distribution status of each iteration of the population and designs a new task allocation mechanism to improve the evolutionary search efficiency. Second, TAMOPSO adopts an adaptive Lévy flight strategy according to the population growth rate, automatically increasing the global variation probability to expand the search range when the population converges and enhancing the local variation to conduct fine search when the population disperses to realize the dynamics of global and local variations. Finally, TAMOPSO measures the contribution of particles to the population optimization through the particle evolution contribution rate index and filters out valuable historical solutions for subsequent reuse to accelerate the convergence speed; in addition, TAMOPSO improves the individual optimal particle selection mechanism, changes the bias of the traditional algorithm, ensures that each particle has an equal opportunity, and enhances the fairness of the selection process. The fairness of the selection process is enhanced at the same time. The performance of TAMOPSO is compared with ten existing algorithms on 22 standard test problems, and the experimental results show that TAMOPSO outperforms the other algorithms in several standard test problems and has better performance in solving multi-objective problems.

In modern marine warfare, unmanned underwater vehicles (UUVs) have fast and efficient attack capabilities. However, existing research on UUV attack strategies is relatively limited, often ignoring the requirement for the effective allocation of different strategic value areas, which restricts its performance in the marine combat environment. To this end, this paper proposes an innovative UUV task allocation and saturation attack strategy. The strategy first divides the area according to the distribution density of enemy UUVs, and then reasonably allocates tasks according to the enemy’s regional value and the attack capability of our UUVs. Our UUVs then sail to the enemy area and are evenly distributed in the encirclement to ensure accurate saturation attacks. In the task allocation link, the grey wolf optimizer is improved by introducing Logistic chaos mapping and differential evolution mechanism, which improves the search efficiency and allocation accuracy. At the same time, the combination of the optimal matching algorithm and Bezier curve dynamic path control ensures the accuracy and flexibility of a coordinated attack. The simulation experimental results show that the strategy shows high attack efficiency and practicality in marine combat scenarios, providing an effective solution for UUV attack tasks in complex marine environments.

Enabled to provide pervasive access to distributed resources in parallel ways, heterogeneous scheduling is extensively applied in large-scaled computing system for high performance. Conventional real-time scheduling algorithms, however, either disregard applications' security needs and thus expose the applications to security threats or run applications at inferior security levels without optimizing security performance. In recognition of high reliability, a security-aware model is firstly presented via quantization of security overheads of heterogeneous systems. Secondly, inspired by multi disciplines, the meta-heuristic is addressed based on the supercomputer hybrid architecture. On the other hand, some technological breakthroughs are achieved, including boundary conditions for different heterogeneous computing and cloud scheduling and descriptions of real-time variation of scheduling indexes (stringent timing and security constraints). Extensive simulator and simulation experiments highlight higher efficacy and better scalability for the proposed approaches compared with the other three meta-heuristics; the overall improvements achieve 8 %, 12 % and 14 % for high-dimension instances, respectively.

Because of current market trends, the evaluation of task allocation strategies in multiprocessor system-on-chips (MPSoCs) must take into account both performance and energy consumption. Furthermore, complex interconnection structures, such as networks-on-chip (NoCs), must be considered. Simulators for the evaluation of energy consumption of detailed communication patterns in NoCs are available, as well as performance simulators that consider detailed task execution in processors. However, in order to evaluate task allocation strategies in MPSoCs, these two types of simulation models must be combined, since communication and computation events interfere with each other. Besides that, this simulator must implement low-power mechanisms, such as dynamic voltage scaling (DVS), in order to evaluate allocation algorithms that explore the trade-off between performance and energy. A cycle-accurate simulation of the processor and communication behaviors, however, would be too time-consuming, making impossible a fast exploration of different allocation algorithms. This work presents an MPSoC simulator that implements the appropriate abstractions for a precise evaluation of the energy consumption of task allocation algorithms that explore DVS, which is based on the scheduling of synthetic task graphs. A NoC mesh topology is considered, due to its simplicity and scalability. Experiments that implement the allocation of task graphs using different bin-packing heuristics combined with DVS demonstrate the energy-performance design space that may be explored by task allocation algorithms.KeywordsNetworks-on-ChipTask AllocationEnergy EstimationSimulationMultiprocessor SoCs