• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.6
• /
• pp.369-375
• /
• 2017

In this paper, we present a self-adaptive software platform that enables an IoT gateway to perform autonomous operation considering IoT devices connected each other in resource-constrained environments. Based on the oneM2M device software platform publicly available, we have designed an additional part, called SAS (self-adaptive software) consisting of MAM (memory-aware module), NAM (network-aware module), BAM (battery-aware module), DAM (data-aware module), and DH (decision handler). A prototype system is implemented to show the feasibility of the proposed self-adaptive software architecture. Our proposed system demonstrates that it can adaptively adjust the operation of gateway and connected devices to their resource conditions under the desired service scenarios.

• Industrial Engineering and Management Systems
• /
• v.6 no.1
• /
• pp.83-93
• /
• 2007

This paper gives an attempt to build a decision support tool linked with a simulation software called ARENA for evaluating and comparing the performance of the push and pull material driven strategies operating in the flow shop environment with a bottleneck resource as the shop's constraint. To be fair for such evaluation, the comparison must be made fairly under the optimal setting of both systems' operating parameters. In this study, an optimal-seeking heuristic algorithm, Genetic Algorithm (GA), is employed to suggest a systems' best design based on the economic consideration, which is the profit generated from the system. Results from the study have revealed interesting outcomes, letting us know the strength and weakness of the push and pull mechanisms as well as the effect of each operating parameter to the overall system's financial performance.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.28 no.1
• /
• pp.140-146
• /
• 2005

This study presents the new dispatching rules of job shop scheduling with auxiliary resource constraint to improve the schedule performance measures related to completion time and due dates. The proposed dispatching rules consider the information of total work remaining and machine utilization to decrease mean flowtime and mean tardiness. The results of computer experiments show that those schedule performances are significantly improved by using the new dispatching rules. The results provide guidance for the researchers and practitioners of auxiliary resource constrained job shop scheduling to decrease mean flowtime and mean tardiness.

• International conference on construction engineering and project management
• /
• 2005.10a
• /
• pp.881-885
• /
• 2005

Scheduling repetitive projects under limitations on the amounts of available resources (labor and equipment) has been an active subject because of its practical relevance. Traditionally, the limitation is specified as a deterministic (fixed) number, such as 1000 labor-hours. The limitation, however, is often exposed to uncertainty and variability, especially when the project is lengthy. This paper presents a stochastic optimization model to treat the situations where the limitations of resources are expressed as probability functions in lieu of deterministic numbers. The proposed model transfers each deterministic resource constraint into a corresponding stochastic one and then solves the problem by the use of a chance-constrained programming technique. The solution is validated by comparison with simulation results to show that it can satisfy the resource constraints with a probability beyond the desired confidence level.

• The Pure and Applied Mathematics
• /
• v.30 no.2
• /
• pp.191-201
• /
• 2023

In this paper the control system described by Urysohn type integral equation is studied. It is assumed that control functions are integrally constrained. The trajectory of the system is defined as multivariable continuous function which satisfies the system's equation everywhere. It is shown that the set of trajectories is Lipschitz continuous with respect to the parameter which characterizes the bound of the control resource. An upper estimation for the diameter of the set of trajectories is obtained. The robustness of the trajectories with respect to the fast consumption of the remaining control resource is discussed. It is proved that every trajectory can be approximated by the trajectory obtained by full consumption of the control resource.

• Journal of Communications and Networks
• /
• v.12 no.5
• /
• pp.449-458
• /
• 2010

In this paper, we present a distributed resource allocation algorithm for multi-cell uplink systems that increases the weighted sum of the average data rates over the entire network under the average transmit power constraint of each mobile station. For the distributed operation, we arrange each base station (BS) to allocate the resource such that its own utility gets maximized in a noncooperative way. We define the utility such that it incorporates both the weighted sum of the average rates in each cell and the induced interference to other cells, which helps to instigate implicit cooperation among the cells. Since the data rates of different cells are coupled through inter-cell interferences, the resource allocation taken by each BS evolves over iterations. We establish that the resource allocation converges to a unique fixed point under reasonable assumptions. We demonstrate through computer simulations that the proposed algorithm can improve the weighted sum of the average rates substantially without requiring any coordination among the base stations.

• Industrial Engineering and Management Systems
• /
• v.9 no.2
• /
• pp.70-79
• /
• 2010

Makespan and cost minimization are two important factors in project investment. This paper considers a multi-mode resource-constrained project scheduling problem with the objective of minimizing costs, subject to a deadline constraint. A number of studies have focused on minimizing makespan or resource availability cost with a specified deadline. This problem assumes a fixed cost for the availability of each renewable resource per period, and the project cost to be minimized is the sum of the variable cost associated with the execution mode of each activity. The presented memetic algorithm (MA) consists of three features: (1) a truncated branch and bound heuristic that serves as effective preprocessing in forming the initial population; (2) a strategy that maintains two populations, which respectively store deadline-feasible and infeasible solutions, enabling the MA to explore quality solutions in a broader resource-feasible space; (3) a repair-and-improvement local search scheme that refines each offspring and updates the two populations. The MA is tested via ProGen generated instances with problem sizes of 18, 20, and 30. The experimental results indicate that the MA performs exceptionally well in both effectiveness and efficiency using the optimal solutions or the current best solutions for the comparison standard.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.5
• /
• pp.1238-1259
• /
• 2024

Previous studies on joint optimization of computation offloading and service caching policies in Mobile Edge Computing (MEC) have often neglected the impact of dependency-aware subtasks, edge server resource constraints, and multiple users on policy formulation. To remedy this deficiency, this paper proposes a many-objective joint optimization dependency-aware task offloading and service caching model (MaJDTOSC). MaJDTOSC considers the impact of dependencies between subtasks on the joint optimization problem of task offloading and service caching in multi-user, resource-constrained MEC scenarios, and takes the task completion time, energy consumption, subtask hit rate, load variability, and storage resource utilization as optimization objectives. Meanwhile, in order to better solve MaJDTOSC, a many-objective evolutionary algorithm TSMSNSGAIII based on a three-stage mating selection strategy is proposed. Simulation results show that TSMSNSGAIII exhibits an excellent and stable performance in solving MaJDTOSC with different number of users setting and can converge faster. Therefore, it is believed that TSMSNSGAIII can provide appropriate sub-task offloading and service caching strategies in multi-user and resource-constrained MEC scenarios, which can greatly improve the system offloading efficiency and enhance the user experience.

• Journal of Korea Multimedia Society
• /
• v.4 no.5
• /
• pp.371-376
• /
• 2001

Since continuous media such as video and audio data are displayed within a certain time constraint, their computation and manipulation should be handled under limited condition. Traditional real-time scheduling algorithms cold be directly applicable, because they are not suitable for multimedia scheduling applications which support many clients at the same time. Rate Regulating Proportional Share Scheduling Algorithm based on the stride scheduler is a scheduling algorithm considered the time constraint of the continuous media. The stride schedulers, which are designed to general tasks, guarantee the fairness of resource allocation and predictability. The key concept of RRPSSA is a rate regulator which prevents tasks from receiving more resource than its share in a given period. But this algorithm loses fairness which is a strong point of the stride schedulers, and does not show graceful degradation of performance under overloaded situation. This paper proposes a new modified algorithm, namely Modified Proportional Share Scheduling Algorithm considering the characteristics of multimedia data such as its continuity and time dependency. Proposed scheduling algorithm shows graceful degradation of performance in overloaded situation and it reduces the scheduling violations up to 70% by maintaining the fair resource allocation. The number of context switching is 8% less than RRPSSA and the overall performance is increased.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.2
• /
• pp.687-708
• /
• 2017

The machine-to-machine (M2M) communication is featured by tremendous number of devices, small data transmission, and large uplink to downlink traffic ratio. The massive access requests generated by M2M devices would result in the current medium access control (MAC) protocol in LTE/LTE-A networks suffering from physical random access channel (PRACH) overload, high signaling overhead, and resource underutilization. As such, fairness should be carefully considered when M2M traffic coexists with human-to-human (H2H) traffic. To tackle these problems, we propose an adaptive Slotted ALOHA (S-ALOHA) and time division multiple access (TDMA) hybrid protocol. In particular, the proposed hybrid protocol divides the reserved uplink resource blocks (RBs) in a transmission cycle into the S-ALOHA part for M2M traffic with small-size packets and the TDMA part for H2H traffic with large-size packets. Adaptive resource allocation and access class barring (ACB) are exploited and optimized to maximize the channel utility with fairness constraint. Moreover, an upper performance bound for the proposed hybrid protocol is provided by performing the system equilibrium analysis. Simulation results demonstrate that, compared with pure S-ALOHA and pure TDMA protocol under a target fairness constraint of 0.9, our proposed hybrid protocol can improve the capacity by at least 9.44% when ${\lambda}_1:{\lambda}_2=1:1$and by at least 20.53% when ${\lambda}_1:{\lambda}_2=10:1$, where ${\lambda}_1,{\lambda}_2$ are traffic arrival rates of M2M and H2H traffic, respectively.