• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권4호
• /
• pp.1258-1275
• /
• 2023

Cloud computing is a new technology that has adapted to the traditional way of service providing. Service providers are responsible for managing the allocation of resources. Selecting suitable containers and bandwidth for job scheduling has been a challenging task for the service providers. There are several existing systems that have introduced many algorithms for resource allocation. To overcome these challenges, the proposed system introduces an Optimized Task Scheduling Algorithm with Deep Learning (OTS-DL). When a job is assigned to a Cloud Service Provider (CSP), the containers are allocated automatically. The article segregates the containers as' Long-Term Container (LTC)' and 'Short-Term Container (STC)' for resource allocation. The system leverages an 'Optimized Task Scheduling Algorithm' to maximize the resource utilisation that initially inquires for micro-task and macro-task dependencies. The bottleneck task is chosen and acted upon accordingly. Further, the system initializes a 'Deep Learning' (DL) for implementing all the progressive steps of job scheduling in the cloud. Further, to overcome container attacks and errors, the system formulates a Container Convergence (Fault Tolerance) theory with high-level security. The results demonstrate that the used optimization algorithm is more effective for implementing a complete resource allocation and solving the large-scale optimization problem of resource allocation and security issues.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권9호
• /
• pp.3663-3679
• /
• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• International Journal of Computer Science & Network Security
• /
• 제22권4호
• /
• pp.89-100
• /
• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• 한국IT서비스학회지
• /
• 제16권3호
• /
• pp.103-111
• /
• 2017

The real time scheduling is a key research area in high performance computing and has been a source of challenging problems. A periodic task is an infinite sequence of task instance where each job of a task comes in a regular period. The RMS (Rate Monotonic Scheduling) algorithm has the advantage of a strong theoretical foundation and holds out the promise of reducing the need for exhaustive testing of the scheduling. Many real-time systems built in the past based their scheduling on the Cyclic Executive Model because it produces predictable schedules which facilitate exhaustive testing. In this work we propose hybrid scheduling method which combines features of both of these scheduling algorithms. The original rate monotonic scheduling algorithm didn't consider the uniform sampling tasks in the real time systems. We have enumerated some issues when the RMS is applied to our hybrid scheduling method. We found the scheduling bound for the hard real-time systems which include the uniform sampling tasks. The suggested hybrid scheduling algorithm turns out to have some advantages from the point of view of the real time system designer, and is particularly useful in the context of large critical systems. Our algorithm can be useful for real time system designer who must guarantee the hard real time tasks.

• 한국정보과학회논문지:시스템및이론
• /
• 제27권6호
• /
• pp.549-557
• /
• 2000

최근 응용 프로그램들은 복잡한 데이타로 구성되어 있기 때문에 이를 효율적으로 처리할 수 있는 분산 메모리 기계(Distributed Memory Machine : DMM)의 필요성이 대두되었다. 특히 태스크 스케줄은 태스크 사이의 통신 시간을 최소화하여 응용 프로그램 전체의 실행 시간을 단축시키는 기법으로서, DMM의 성능을 향상시키는 매우 중요한 요소이다. 기존의 태스크 중복 스케줄(Task Duplicated based Scheduling : TDS) 기법은 두 개의 태스크 사이에 통신 시간이 많이 소요되는 것들을 하나의 클러스터(cluster)로 스케줄함으로써 통신 시간을 단축하여 실행 시간을 향상시키는 기법이다. 그러나 데이타를 전달하는 태스크와 이 태스크로 데이타를 전달받는 태스크 사이의 통신 시간을 최적화 하지 못하는 단점을 가진다. 따라서 본 논문에서는 이 두 태스크 사이의 최적화에 근접한 통신 시간을 갖는 개선된 중복 스케줄 (Modified Task Duplicated based Scheduling : MTDS) 기법을 제안하였다. 이 기법은 데이타를 전달한 태스크들을 클러스터링하기 위해 데이타를 전달받은 태스크에서 최적화 조건을 적용하여 검사한다. 그 결과 태스크 사이의 통신 시간을 단축하여 전체 태스크 실행 시간을 최소화하였다. 또한 시스템의 모델링을 통하여 MTDS 기법이 최상의 경우 TDS 기법보다 태스크 실행 시간을 70% 단축 시켰고 최악의 경우 TDS 기법과 동일한 실행 시간을 얻으므로 제안된 기법이 기존의 기법보다 우수함을 입증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권11호
• /
• pp.3523-3543
• /
• 2022

Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

• Journal of Computing Science and Engineering
• /
• 제7권1호
• /
• pp.30-43
• /
• 2013

Multicore and multiprocessor systems with dynamic voltage scaling architectures are being used as one of the solutions to satisfy the growing needs of high performance applications with low power constraints. An important aspect that has propelled this solution is effective task/application scheduling and mapping algorithms for multiprocessor systems. This work proposes an energy aware, offline, probability-based unified scheduling and mapping algorithm for multiprocessor systems, to minimize the number of processors used, maximize the utilization of the processors, and optimize the energy consumption of the multiprocessor system. The proposed algorithm is implemented, simulated and evaluated with synthetic task graphs, and compared with classical scheduling algorithms for the number of processors required, utilization of processors, and energy consumed by the processors for execution of the application task graphs.

• 대한전자공학회논문지SD
• /
• 제46권7호
• /
• pp.22-28
• /
• 2009

멀티프로세서가 임베디드 시스템에서 널리 쓰임에 따라 지원되는 전력 최소화 기법을 이용하여 태스크를 수행하기 위해 필요한 에너지의 소모량을 줄여야 할 필요성이 대두된다. 본 논문은 동적 전압 스케일링 및 전력 셧다운을 이용하여 에너지 소모를 최소화 하는 태스크 스케줄링 알고리즘을 멀티프로세서 환경을 위해 제안하였다. 제안된 알고리즘에서는 전력 셧다운시의 에너지 및 타이밍 오버헤드를 고려하여 반복적으로 태스크 할당 및 태스크 순서화를 수행한다. 제안된 반복적인 태스크 스케줄링을 통해 전체 에너지 소모를 줄이는 가장 좋은 해를 얻을 수 있었다. 전체 에너지 소모는 리니어 프로그래밍 모델 및 전력 셧다운의 임계 시간을 고려하여 계산되었다. 실제 어플리케이션으로부터 추출된 표준 태스크 그래프에 기반을 둔 실험 결과를 통해 하드웨어 자원 및 시간제한에 따른 에너지 소모 관계를 분석하였다. 실험 결과를 볼 때 제안된 알고리즘은 기존의 우선권 기반의 태스크 스케줄링에 대해서 의미 있는 성능 향상을 얻을 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제5권9호
• /
• pp.1544-1572
• /
• 2011

Task placement and scheduling are traditionally studied in following aspects: resource utilization, application throughput, application execution latency and starvation, and recently, the studies are more on application scalability and application performance. A methodology for task placement and scheduling centered on tasks based on virtual machines is studied in this paper to improve the performances of systems and dynamic adaptability in applications development and deployment oriented parallel computing. For parallel applications with no real-time constraints, we describe a thought of feature model and make a formal description for four layers of task placement and scheduling. To place the tasks to different layers of virtual computing systems, we take the performances of four layers as the goal function in the model of task placement and scheduling. Furthermore, we take the personal preference, the application scalability for a designer in his (her) development and deployment, as the constraint of this model. The workflow of task placement and scheduling based on virtual machines has been discussed. Then, an algorithm TPVM is designed to work out the optimal scheme of the model, and an algorithm TEVM completes the execution of tasks in four layers. The experiments have been performed to validate the effectiveness of time estimated method and the feasibility and rationality of algorithms. It is seen from the experiments that our algorithms are better than other four algorithms in performance. The results show that the methodology presented in this paper has guiding significance to improve the efficiency of virtual computing systems.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권4호
• /
• pp.1100-1122
• /
• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.