• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.27-36
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• 1993

This paper proposes a shortest path allocation algorithm over the processors on the hypercube system based on the message passing techniques with the optimized module allocation. On multiprocessor systems, how to divide one task into multiple tasks efficiently is an important issue due to the hardness of the life cycle estimation of each process. To solve the life cycle discrepancies, the appropriate task assignment to each processor and the flexible communications among the processors are indispensible. With the concurrent program execution on hypercube systems, each process communicaties to others with the method of message passing. And, each processor has its own memory. The proposed algorithm generates a callable tree out of the module, assigns the weight factors, constructs the allocation graph, finds the shortest path allocation tree, and maps them with hypercube.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.45-47
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• 2022

Mobile edge computing (MEC), which enables mobile terminals to offload computational tasks to a server located at the user's edge, is considered an effective way to reduce the heavy computational burden and achieve efficient computational offloading. In this paper, we study a multi-user MEC system in which multiple user devices (UEs) can offload computation to the MEC server via a wireless channel. To solve the resource allocation and task offloading problem, we take the total cost of latency and energy consumption of all UEs as our optimization objective. To minimize the total cost of the considered MEC system, we propose an DRL-based method to solve the resource allocation problem in wireless MEC. Specifically, we propose a Asynchronous Advantage Actor-Critic (A3C)-based scheme. Asynchronous Advantage Actor-Critic (A3C) is applied to this framework and compared with DQN, and Double Q-Learning simulation results show that this scheme significantly reduces the total cost compared to other resource allocation schemes

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.58-61
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• 2002

In this paper, we present a new generalized k-Tree-based (CU/PE) allocation model to perform dynamic resource (CU/PE) allocation/deallocation decision for the reconfigurable MSIMD/MIMD multi-dimensional (k-D) mesh-connected architectures. Those reconfigurable multi-SIMD/MIMD systems allow dynamic modes of executing tasks, which are SIMD and MIMD. The MIMD task requires only the free sub-system; however the SIMD task needs not only the free sub-system but also the corresponding free CU. In our new k-Tree-based (CU/PE) allocation model, we introduce two best-fit heuristics for the CU allocation decision: 1) the CU depth first search (CU-DFS) in O(kN$_{f}$ ) time and 2) the CU adjacent search (CU-AS) in O(k2$^{k}$ ) time. By the simulation study, the system performance of these two CU allocation strategies was also investigated. Our simulation results showed that the CU-AS and CU-DFS strategies performed the same system performance when applied for the reconfigurable MSIMD/MIMD 2-D and 3-D mesh-connected architectures.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.9-21
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• 2003

In this paper, we propose a new submesh allocation scheme which improves the performance of multicomputer systems. The key idea of this allocation scheme is to reduce waiting time of task by minimizing the submesh search time and the submesh a]location delay caused by external fragmentation. This scheme reduces the submesh search time by classifying independent free submeshes according to their types (square, horizontal rectangle, vertical rectangle) and searching a best-fit submesh from the classified free submesh list. If a submesh allocation delay occurs due to external fragmentation, the proposed scheme relocates tasks, executing In allocated submeshes, to another free submeshes and compacts processor fragmentation. This results in reducing the submesh allocation delay. Through simulation, we show that it is more effective to reduce the submesh allocation delay due to external fragmentation than reducing the submesh search time with respect to the waiting time of task. We also show that the proposed strategy improves the performance compared to previous strategies.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.601-608
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• 2001

The automated assignment of multiple tasks to executing agents is a key problem in the area of multi-agent systems. In many domains, significant savings can be achieved by reallocating tasks among agents with different costs for handling tasks. The automation of task reallocation among self-interested agents requires that the individual agents use a common negotiation protocol that prescribes how they have to interact in order to come to an agreement on "who does what". In this paper, we introduce the multi-agent Traveling Salesman Problem(TSP) as an example of task reallocation problem, and suggest the Vickery auction as an interagent negotiation protocol for solving this problem. In general, auction-based protocols show several advantageous features: they are easily implementable, they enforce an efficient assignment process, and they guarantce an agreement even in scenarios in which the agents possess only very little domain-specific Knowledge. Furthermore Vickrey auctions have the additional advantage that each interested agent bids only once and that the dominant strategy is to bid one′s true valuation. In order to apply this market-based protocol into task reallocation among self-interested agents, we define the profit of each agent, the goal of negotiation, tasks to be traded out through auctions, the bidding strategy, and the sequence of auctions. Through several experiments with sample multi-agent TSPs, we show that the task allocation can improve monotonically at each step and then finally an optimal task allocation can be found with this protocol.

• ETRI Journal
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• v.44 no.5
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.35-50
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• 2020

This study aimed to investigate the changes of emotional perception biases according to the level of social phobia and the relationship with visuospatial attention allocation ability. This study used the Korean self-report assessment test (Korean version of social avoidance and distress, K-SAD) to measure the level of social phobia. Moreover, the emotional perception and useful field of view (UFOV) tasks were employed for measuring emotional perception biases and visuospatial attention allocation ability, respectively. A total of 118 participants participated in this experiment, and only 107 (males: 94, females: 13) data were analyzed due to the exclusion of response errors and other statistical problems. The average age of the participants used in the analysis was 21 years (SD: 3.64), and those participants were divided into three groups according to the K-SAD scores. Consequently, all experimental groups showed negative emotional perception biases in the emotional perception task, but the magnitudes of the biases of each group were not significantly different. Furthermore, the positive emotional perception biases were higher at higher levels of social phobia, which could be related to the tendency of interpreting positive stimuli negatively. In the UFOV task, the higher the level of social phobia, the lower the visuospatial attention allocation ability. These results suggest that the deterioration of visuospatial attention allocation ability potentially contributes to the increase of positive emotional perception biases by being difficult to perceive external stimuli. Thus, this paper discusses the potential contribution of visuospatial attention allocation ability to the increased perceptual biases of positive emotions as the level of social phobia increases.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.76-89
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• 2010

The performance of Distributed Heterogeneous Computing System depends on the algorithm which schedules input DAG graph. Among various scheduling algorithms, list scheduling algorithm provides superior performance with low complexity. List scheduling consists of task prioritizing phase and processor allocation phase, but most studies only focus on task prioritizing phase. In this paper, we propose LIP policy which has the same complexity with traditional allocation policies but has superior performance. The performance of LIP has been observed by applying them to task prioritizing phase of traditional list scheduling algorithms, HCPT, HEFT, GCA, and PETS. The results show that LIP has better performance than insertion-based policy and non-insertion-based policy, which are traditional processor allocation policies.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1667-1677
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• 1999

An effective allocation of requested number of processors to newly incoming tasks in two-dimensional (2D) mesh interconnection networks is very important for achieving the desired high performance and resource utilization. It also needs to guarantee the complete recognition of the free submeshes based on contiguous and available processors with minimum overhead. An efficient task allocation algorithm for 2D meshes is presented in this paper. By employing a new approach for searching the one-dimensional array, the proposed algorithm can find the available submesh without the scanning of the entire 2D array unlike earlier designs. As a result, the new algorithm can significantly reduce the task allocation time. Comprehensive computer simulation shows that the average allocation time and waiting delay are much smaller than earlier irrespective of the size of meshes. The hardware overhead is comparable to other algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4025-4041
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• 2020

Mobile edge computing (MEC) is capable of providing services to smart devices nearby through radio access networks and thus improving service experience of users. In this paper, an offloading strategy for the joint optimization of computing and communication resources in multi-user and multi-MEC overlapping scene was proposed. In addition, under the condition that wireless transmission resources and MEC computing resources were limited and task completion delay was within the maximum tolerance time, the optimization problem of minimizing energy consumption of all users was created, which was then further divided into two subproblems, i.e. offloading strategy and resource allocation. These two subproblems were then solved by the game theory and Lagrangian function to obtain the optimal task offloading strategy and resource allocation plan, and the Nash equilibrium of user offloading strategy games and convex optimization of resource allocation were proved. The simulation results showed that the proposed algorithm could effectively reduce the energy consumption of users.