Earticle

Cloud systems are popular computing environment because they can provide easy access to computing resources for users as well as efficient use of resources for companies. The resources of cloud computing are heterogeneous and jobs have various characteristics. One such issue is effective job scheduling. Scheduling in the cloud system may be defined as a multiple criteria decision model. To address this issue, this paper proposes a priority-based two-step job scheduling algorithm. On the first level, jobs are classified based on preference. Resources are dedicated to a job if a deadline failure would cause severe results or critical business losses. In case of only minor discomfort or slight functional impairment, the job is scheduled using a best effort approach. On the second level, jobs are allocated to adequate resources through their priorities that are calculated by the analytic hierarchic process model. We then analyze the proposed algorithm and make a scheduling example to confirm its efficiency.

In the data scheduling process of wireless sensor network (WSN), node data fusion is one of the main methods to reduce network communication volume. The decrease in network communication volume contributes to the network energy consumption reduction, which is crucial for improving the WSN lifecycle. Hence, we propose an energy-saving hybrid-optimization scheduling algorithm based on fat tree (FT) for WSN, which is referred to as the FTEBHSA algorithm. In the scheduling algorithm, multiple strategies are adopted to optimize for saving energy, involving shortest path tree optimization, fusion tree load balance, a fusion node rotation mechanism, and a sleep mechanism for monitoring nodes. More importantly, we introduce the FT structure to organically integrate these strategies for reducing the energy consumption and boosting the WSN lifecycle. The simulation experiment results verify that the proposed hybrid-optimization scheduling algorithm performs optimally in optimizing the energy consumption.

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

The round robin algorithm is regarded as one of the most efficient and effective CPU scheduling techniques in computing. It centres on the processing time required for a CPU to execute available jobs. Although there are other CPU scheduling algorithms based on processing time which use different criteria, the round robin algorithm has gained much popularity due to its optimal time-shared environment. The effectiveness of this algorithm depends strongly on the choice of time quantum. This paper presents a new effective round robin CPU scheduling algorithm. The effectiveness here lies in the fact that the proposed algorithm depends on a dynamically allocated time quantum in each round. Its performance is compared with both traditional and enhanced round robin algorithms, and the findings demonstrate an improved performance in terms of average waiting time, average turnaround time and context switching.

The objective of cloud task scheduling is to partition tasks on shared resources to minimize energy consumption and makespan. Recently, several meta-heuristics for task scheduling were proposed and achieved encouraging results. However, their performance is far from the ideal state and needs more improvement. This paper introduces an enhanced sunflower optimization (ESFO) algorithm for improving the performance of existing task scheduling. It finds optimal scheduling in a polynomial time. The experiments show that ESFO outperformed its counterparts. The amount of improvement in comparison with the best counterpart is 0.73% and 2.24% respectively in terms of makespan and energy consumption.

Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

The primary objective of task scheduling involves scheduling the task on resources and minimizing the objective of the schedule. In this study, we proposed mean grey wolf optimization algorithm to enhance the system performance there by depleting the scheduling issues. The main objective of this method is minimizing the makespan and energy consumption. The objective of the proposed algorithms has been evaluated using CloudSim toolkit for standard workload (left-skewed & right-skewed). The outcome of the simulation result shows that the proposed Mean GWO algorithm renders comparatively ample result than the other existing algorithms.

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

Beacon scheduling is considered to be one of the most significant challenges for energy-efficient Low-Rate Wireless Personal Area Network (LR-WPAN) multi-hop networks. The emerging new standard, IEEE802.15.4e, contains a distributed beacon scheduling functionality that utilizes a specific bitmap and multi-superframe structure. However, this new standard does not provide a critical recipe for superframe duration (SD) allocation in beacon scheduling. Therefore, in this paper, we first introduce three different SD allocation approaches, LSB first, MSB first, and random. Via experiments we show that IEEE802.15.4e DSME beacon scheduling performs differently for different SD allocation schemes. Based on our experimental results we propose an adaptive SD allocation (ASDA) algorithm. It utilizes a single indicator, a distributed neighboring slot incrementer (DNSI). The experimental results demonstrate that the ASDA has a superior performance over other methods from the viewpoint of resource efficiency.

In the recent years, non-preemptive job shop scheduling problems have been applied to a wide variety of academic and industrial fields. In comparison, preemptive job shop scheduling problems have received almost no attention in the both fields. Motivated by the needs of a specific application, we presented an algorithm for dealing with preemptive job shop scheduling problem. First, we considered constraint programming techniques to preemptive scheduling problems. Second, we applied genetic algorithm to these problems. In proposed genetic algorithm, we developed a new concept for representing of genetic algorithm. In case study, we applied the proposed algorithm to several job shop problems. Experiment results show that the proposed algorithm considered by preemptive problems outperforms non-preemptive case and other conventional algorithms.

전력소모를 고려한 테스트 스케줄링은 회로의 복잡도가 높은 SoC 시스템을 테스트할 경우 제한된 전력 소모량 내에서 고장 검출율을 높일 수 있고 테스트 시간을 단축 할 수 있는 효과적인 방법이다. 본 논문에서는 제한된 전력소모량 내에서 효율적으로 테스트를 수행하기 위한 테스트 자원의 모델링 방법 및 테스트 스케줄링 알고리듬을 제안하고 그 유효성을 검증한다. 테스트 자원의 모델링 방법으로는 전력사용량의 최고점과 차고점을 이용한 방법 및 소모 전력의 변화량에 따라 테스트 자원을 분할하는 방법을 제시한다. 또한 테스트 자원과 코어의 상관관계를 이용하여 동시 사용가능한 최대 코어 수를 생성하는 확장나무성장 그래프 생성 알고리듬 및 전력의 최적화가 가능한 전력 소모량 변이 그래프 생성 알고리듬으로 구성된 휴리스틱(heuristic) 테스트 스케줄링 알고리듬을 제안하고 이전의 알고리듬과 비교한다.

Test scheduling considering power dissipation is an effective technique to reduce the testing time of complex SoCs and to enhance fault coverage under limitation of allowed maximum power dissipation. In this paper, a modeling technique of test resources and a test scheduling algorithm for efficient test procedures are proposed and confirmed. For test resources modeling, two methods are described. One is to use the maximum point and next maximum point of power dissipation in test resources, the other one is to model test resources by partitioning of them. A novel heuristic test scheduling algorithm, using the extended-tree-growing-graph for generation of maximum embedded cores usable simultaneously by using relations between test resources and cores and power-dissipation-changing-graph for power optimization, is presented and compared with conventional algorithms to verify its efficiency.

Scheduling parallel tasks, represented as a Directed Acyclic Graph (DAG) or task graph, on a multiprocessor system has been an important research area in the past decades. List scheduling algorithms assign priorities to a node or an edge in an input DAG, and then generate a schedule according to the assigned priorities. This appear proposes a list scheduling algorithms with effective method of priority assignments. The paper also analyzes the worst case performance and optimality condition for the proposed algorithm. The performance comparison study shows that the proposed algorithms outperforms existing scheduling algorithms especially for input DAGs with high communication overheads. The performance improvement over existing algorithms becomes larger as the input DAG becomes more dense and the level of parallelism in the DAG is increased.

This paper contains the multi-mission scheduling optimization of UAV within a given operating time. Mission scheduling optimization problem is one of combinatorial optimization, and it has been shown to be NP-hard(non-deterministic polynomial-time hardness). In this problem, as the size of the problem increases, the computation time increases dramatically. So, we applied the genetic algorithm to this problem. For the application, we set the mission scenario, objective function, and constraints, and then, performed simulation with MATLAB. After 1000 case simulation, we evaluate the optimality and computing time in comparison with global optimum from MILP(Mixed Integer Linear Programming).

실시간 시스템에서 시간적 제약의 불이행의 커다란 손실을 가져오며, 이를 위한 동적 스케쥴링은 유연성을 제공하는 대신 스케쥴링 오버헤드와 분석작업의 복잡성으로 인하여 스케쥴성을 예측하기 어려움이 있다. 반면, 정적 스케쥴링은 수행 중 오버헤드가 없으므로 정확한 시간을 예측할 수 있는 장점이 있다. 따라서 명령어 수준의 정적 스케쥴링과 시간 분석을 통하여 시스템의 시간적 정확도를 보장할 수 있다. 본 논문에서는 확정된 시간 분석을 위하여 befor와 after의 시간 제약을 고급 언어에 표현하고 이를 근거로 시간적 분석에 기반을 둔 컴파일러의 명령어 수준의 스케쥴링 알고리즘을 제안하였다. 이 스케쥴링의 특징은 명령어 수준의 스케쥴링을 위한 도메인이 지나치게 과대하므로 향상된 적합 유전자 알고리즘을 적용한 것이다.

In hard real-time systems, a timing fault may yield catastrophic results. Dynamic scheduling provides the flexibility to compensate for unexpected events at runtime; however, scheduling overhead at runtime is relatively large, constraining both the accuracy of the timing and the complexity of the scheduling analysis. In contrast, static scheduling need not have any runtime overhead. Thus, it has the potential to guarantee the precise time at which each instruction implementing a control action will execute. This paper presents a new approach to the problem of analyzing high-level language code, augmented by arbitrary before and after timing constraints, to provide a valid static schedule. Our technique is based on instruction-level complier code scheduling and timing analysis, and can ensure the timing of control operations to within a single instruction clock cycle. Because the search space for a valid static schedule is very large, a novel adaptive genetic search algorithm was developed.

This paper proposes an algorithm for the Unrelated Parallel Machine Scheduling Problem(UPMSP) without setup times, aiming to minimize total tardiness. As an NP-hard problem, the UPMSP is hard to get an optimal solution. Consequently, practical scenarios are solved by relying on operator's experiences or simple heuristic approaches. The proposed algorithm has adapted two methods: a policy network method, based on Transformer to compute the correlation between individual jobs and machines, and another method to train the network with a reinforcement learning algorithm based on the REINFORCE with Baseline algorithm. The proposed algorithm was evaluated on randomly generated problems and the results were compared with those obtained using CPLEX, as well as three scheduling algorithms. This paper confirms that the proposed algorithm outperforms the comparison algorithms, as evidenced by the test results.

기존 무선에너지 하비스팅시스템에서 유저는 하나의 Hybirid-AP (H-AP)로부터 에너지와 정보를 동시에 받았다. 하지만 무선에너지 하비스팅은 거리가 멀수록 감쇄가 심하기 때문에 H-AP에서 거리가 먼 유저들은 낮은 하비스팅 효율 가진다 (이중 근거리/원거리 문제). 이러한 문제를 해결하기 위해서 본 논문은 Power beacon (PB)을 통해 유저에게 별도의 파워를 공급하는 비결합 무선에너지 하비스팅 네트워크를 시스템 모델로 사용하였다. 이 논문의 주된 목적은 다양한 제약 조건과 Quality of service (QoS), 그리고 Quality of harvested power (QoP)를 만족하면서 목적 함수인 Energy efficiency (EE)를 최대화 하는 것이다. 제안된 시스템은라그랑지안 쌍대 분해법 이론을 기반으로 EE 최대화를 위한 최적의 시간 스케줄링 알고리즘을 제안한다. 이 알고리즘을 통해 프레임 분해 요소, H-AP의 송신파워, 그리고 PB의 송신파워의 최적 값과 EE의 최대값을 구할 수 있다. 모의 실험 결과는 제안된 알고리즘으로 파라미터가 최적 값으로 빠르게 수렴하고 제안된 모델의 성능이 기존의 시스템 모델보다 우수하다는 것을 증명한다.

Conventional RF energy harvesting systems can harvest energy and decode information from same source as an Hybirid-AP (H-AP). However, harvesting efficiency is seriously dependent on distance between users and H-AP. Therefore, in this paper, we proposed a transmission model for RF harvesting consisting of information and power source separately called Decoupled RF Energy harvesting networks. Main purpose of this paper is to maximize energy efficiency under various constraints of transmit power from H-AP and power beacon (PB), minimum quality of service and quality of harvested power of each users. To measure proposed model’s performance, we proposed optimal time scheduling algorithms for energy efficiency (EE) maximization using Lagrangian dual decomposition theory that locally maximizes the EE by obtaining suboptimal values of three arguments : transmit power of H-AP, transmit power of PB, frame splitting factor. Experiment results show that the proposed energy-efficient algorithms converge within a few iterations with its optimality and greatly improve the EE compared to that of baseline schemes.