• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권7호
• /
• pp.2815-2839
• /
• 2020

In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.). This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power. Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing. More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives. Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.

• Management Science and Financial Engineering
• /
• 제2권1호
• /
• pp.19-51
• /
• 1996

We suggest a distributed framework for task assignment in the computer-controlled shop floor where each of the resource agents and part agents acts like an independent profit maker. The job allocation problem is formulated as a linear programming problem. The LP formulation is analyzed to provide a rationale for the distributed task assignment procedure. We suggest an auction based negotiation procedure including a price-based bid construction and a price revising mechanism. The performance of the suggested procedure is compared with those of an LP formulation and conventional dispatching procedures by simulation experiments.

• Journal of Information Processing Systems
• /
• 제18권3호
• /
• pp.389-401
• /
• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.

• 한국정보처리학회논문지
• /
• 제6권9호
• /
• pp.2311-2319
• /
• 1999

병렬 컴퓨팅에 있어 NP-complete 문제인 태스크 할당문제에 대한 두 가지 휴리스틱 알고리즘을 제시한다. 할당문제는 분산 메모리 멀티컴퓨터의 멀티 프로세싱 노드에 다중통신 태스크들을 최적의 매핑을 찾는 것이다. 태스크들을 목표 시스템 구조의 노드들에 매핑시키는 목적은 해법 품질에 손상 없이 병렬 실행시간을 최소화하기 위함이다. 많은 휴리스틱 기법들이 만족한 매핑을 얻기 위해 채택되어 왔다. 본 논문에서 제시되는 휴리스틱 기법은 유전자 알고리즘(GA)과 시뮬레이티드 어닐링(SA) 기법에 기반을 둔다. 매핑 설정을 위한 총 계산 비용으로 목적함수를 수식화하고 휴리스틱 알고리즘들의 성능을 평가한다. 랜덤, 그리디, 유전자, 어닐링 알고리즘들을 사용하여 얻은 해법의 품질과 시간을 비교한다. 할당 알고리즘 시뮬레이션 연구를 통한 실험적 결과를 보여준다.

• International Journal of Computer Science & Network Security
• /
• 제23권1호
• /
• pp.140-146
• /
• 2023

Resource allocation is one of the top challenges in Internet of Things (IoT) networks. This is due to the scarcity of computing, energy and communication resources in IoT devices. As a result, IoT devices that are not using efficient algorithms for resource allocation may cause applications to fail and devices to get shut down. Owing to this challenge, this paper proposes a novel algorithm for managing computing resources in IoT network. The fog computing devices are placed near the network edge and IoT devices send their large tasks to them for computing. The goal of the algorithm is to conserve energy of both IoT nodes and the fog nodes such that all tasks are computed within a deadline. A bi-partite graph-based algorithm is proposed for stable matching of tasks and fog node computing units. The output of the algorithm is a stable mapping between the IoT tasks and fog computing units. Simulation results are conducted to evaluate the performance of the proposed algorithm which proves the improvement in terms of energy efficiency and task delay.

• International Journal of Quality Innovation
• /
• 제9권2호
• /
• pp.57-77
• /
• 2008

The $21^{st}$ century is a new century of environmental protection. Environmental protection is one of the most important subject matters yet to come. Moreover, as the public pays more attention to environmental problems, enterprises should increase their investment in environmental management. Therefore, determining the investment level for environmental management and allocating the investment to associated environmental management activities has become a major task. The principal and agent theory and sales response functions are used for analysis in this research. The allocation of capital investment in environmental management is found to have significant impact on the aggregate sales response, aggregate profit and investment level. Therefore, in preparing the budget for environmental management, enterprises should focus on investment allocation decisions, determine the investment level and allocation method using integrated means, and apply submarket data in the allocation decision-making process. In other words, in setting the investment level, executive management should take managers' willingness into consideration. In allocating capital investment, managers should identify the optimal allocation method based on submarket characteristics.

• 한국정밀공학회지
• /
• 제18권8호
• /
• pp.129-138
• /
• 2001

The paper presents an implementation procedure of assembly line for ABS motor, which is composed of four subassemblies-yoke, grommet, housing and armature. The characteristics of ABS motor and its assembly processes are analysed, and the automation possibility of each process is examined in order to decrease assembly time. The assembly machines and facilities are then selected for automatic assembly, and the layout of the selected facilities is determined. Finally, task allocation of each worker is achieved by assembly line balancing to increase assembly productivity and efficiency. The line efficiency is also analyzed using simulation.

• International Journal of Contents
• /
• 제7권2호
• /
• pp.1-5
• /
• 2011

Allocation of computing resources is a crucial issue when dealing with a huge number of tasks to be completed according to a given deadline and cost constraints. The task scheduling to several resources (e.g. grid, cloud or a supercomputer) with different characteristics is not trivial, especially if a trade-off in terms of time and cost is considered. We propose an allocation approach able to fulfill the given requirements about time and cost through the use of optimizing techniques and an adaptive behavior. Simulated productions of tasks have been run in order to evaluate the characteristics of the proposed approach.

• Journal of Information Processing Systems
• /
• 제20권2호
• /
• pp.226-238
• /
• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• 한국전자거래학회지
• /
• 제12권1호
• /
• pp.73-87
• /
• 2007

세계 주요 기업들을 중심으로 비즈니스 프로세스를 보다 표준화하고, 명확화하려는 움직임이 어느 때보다 두드러지고 있다. BPM은 이러한 시도를 구현할 수 있는 적합한 도구로서 주목받고 있다. 최근 BPM으로 구현된 프로세스를 보다 효율적으로 실행하는 것이 이슈로 대두되고 있다. 본 논문에서는 프로세스 효율성을 크게 프로세스 엔진과 업무담당자 관점에서 이를 해결하고자 한다. 프로세스 엔진 관점에서는, 엔진이 사용자에게 업무를 얼마나 효율적으로 전달하는지 바라본다. 업무담당자 관점에서는, 전달된 업무를 각 업무담당자가 어떻게 처리하는지에 주목한다. 본 연구는 각 단계에서만 효율성을 고려해왔던 과거 연구와는 달리, 두 단계에서 모두 효율적일 수 있는 방법론을 제안하며, 이를 실험을 통해 유효성을 검증한다.