Table 2 - uploaded by Teck Chaw Ling
Content may be subject to copyright.
Source publication
The use of grid technology and web services for resource sharing has received tremendous attention in recent years. The merging of these 2 technologies is able to provide additional multiple types of services and functionalities. However, the problem of scheduling services to meet quality of service (QoS) requirements remains challenging. This pape...
Context in source publication
Context 1
... deadline value was added as the QoS requirement to each service request. Table 2 shows the results for reliability for different job lengths with Poisson arrival and the percentage of improvement. These results show that AQoS outperformed the MIN-MIN and MAX-MIN algorithms by 5%-20% in terms of reliability. ...
Citations
... In [7], the authors proposed a fault-tolerant extension of the classic HEFT algorithm [14], which is based on replication redundancy. The works in [15,16] proposed scheduling algorithms for service-oriented environments. In [4], the authors proposed an algorithm for scheduling deadline-constrained applications for an IaaS cloud environment. ...
Most of the existing work in the area of task graph scheduling considers resources with fixed processing capacity. The algorithms in these works rely on an estimation of the execution times of tasks on different resources. However, in practice, due to fluctuations in performance of cloud resources, these algorithms have challenges in these environments. In this paper, we focus on the problem of fault-tolerant scheduling of task graphs in the presence of performance fluctuations of computational resources. With the aim of reducing the adverse impacts of both soft errors and resource performance degradations, we propose an opportunistic task replication scheme that uses idle durations of resources for replicating tasks. Unlike the previous works, the proposed algorithm does not rely on estimation of task execution times for finding idle resources. We introduce the notion of concurrency graphs and propose a graph theory-based algorithm for finding the number of idle resources during the execution of a set of tasks. The appropriate redundancy for each task is chosen with respect to the number of idle resources and the characteristics of the set of tasks that are being processed concurrently. Simulation experiments show that, in most situations, the proposed algorithm outperforms the previous algorithms in terms of average execution time and cost.
... There exist quite a number of researches that enhanced the research allocation in cloud [16][17][18][19][20][21][22][23]. A matchmaking strategy that base on job execution time was proposed for resource allocation in [16]. ...
Cloud computing has taken the IT and business world by storm through its cost savings and quick-and-easy adoption. Currently, fixed price model is dominating the pricing schemes in the cloud market. However, this model is unable to reflect the current market needs for cost savings as the number of cloud provider and user increases. As a result, a dynamic pricing scheme has emerged as an attractive strategy to better cope with the unpredictable cloud demand. This paper proposed a dynamic pricing scheme that provides fairness among the service providers in a multi-cloud environment. The scheme adjusts the price accordingly to encourage the low utilized resources' usage and discourage over utilized resources' usage. The evaluation results showed that the proposed scheme is able to reduce the cost of the end user when running compute-intensive and data-intensive jobs in the multi-cloud environment.
... In [10] heuristics have been used to provide the required QoS. An adaptive QoS (AQoS) scheduling algorithm in service-oriented Grid environments has been presented in [11]. The authors of [12] offer to perform so-called meta-scheduling preliminary to achieve the desired QoS. ...
The structure of the centralized distributed computer system (DCS) task scheduler, which uses the adaptive resource security management mechanism was developed. By interacting with local agents of the given compute nodes, the scheduler defines the system node parameters and selects the resources with the specified security and performance requirements. Ensuring an optimum combination of mutually exclusive security and performance parameters is a non-trivial task, requiring the development of new approaches to solving it. The research found that the adaptive distributed system resource security management mechanism increases the DCS performance in comparison with the classical resource security management mechanism. In particular, the research shows that the average task time in the queue and the average task time in the system with the adaptive security level management mechanism is 2.8 and 2.1 times lower, respectively, in comparison with the classical security level management mechanism. At the same time, the adaptive security management introduction requires additional software on the DCS compute nodes for the CN status parameters monitoring. The experiments demonstrate that the monitoring system can significantly reduce the DCS performance. Thus, according to the experiments, in case of 25 % load on the DCS CN from the monitoring system, the average task time in the queue and the average task time in the system increase by 62 % compared with a situation where monitoring is not performed. The research results need to be considered when introducing the secure data processing mechanisms in DCS to prevent a substantial decrease in the distributed system performance. © Hu Zhenbing, V. Mukhin, Ya. Kornaga, O. Herasymenko, Yu. Bazaka, 2017.
... Determining which, when, and how the resources will be used to complete some certain tasks by considering these elements is called scheduling. It is possible to do specific activities using fewer resources in a shorter time by efficient scheduling [17][18][19]. ...
Artificial immune systems (AISs) are one of the artificial intelligence techniques studied a lot in recent years. AISs are based on the principles and mechanisms of the natural immune system. In this study, the clonal selection algorithm, which is used commonly in AISs, is modified. This algorithm is applied to job shop scheduling problems, which are one of the most difficult optimization problems. For applying application results to the optimum solution, parameter values giving the optimum solution are determined by analyzing the parameters in the algorithm. The obtained results are given in detail in the tables and figures. The best makespan values are reached in 7 out of 10 test problems (FT06, LA01, LA02, LA03, LA04, LA05, and ABZ6). Reasonable makespan values are reached for the remaining 3 problems (FT10, LA16, and ABZ5). The obtained results demonstrate that the developed system can be applied successfully to job shop scheduling problems.
This article considers the ensuring of secure data processing in distributed computer systems (DCSs), which is important for a certain class of computing tasks. An approach to the resource management in DCSs is proposed that makes it possible to take into account, according to user requirements, both the time spent on the execution of a task and the security level of the system resources involved in its execution.
Grid computing solves high-performance and high-throughput computing problems through sharing nodes ranging from personal computers to supercomputers distributed around the world. As the grid environments facilitate distributed computation, the scheduling of grid jobs has become an important issue. In this paper, an investigation on implementing Two-Phase Variable Neighborhood Search (TPVNS) algorithm for scheduling independent jobs on computational grid is carried out. The proposed algorithm consists of two modules with General Variable Neighborhood Search and Basic Variable Neighborhood Search algorithms in order to find a good mapping of grid jobs with grid nodes. The performance of the proposed algorithm has been evaluated with deterministic heuristic and evolutionary algorithms. Simulation results show that TPVNS algorithm generally performs better than the existing methods.
Grid systems are the federation of resources from multiple locations to reach a common goal. In previous researches, the focused resources were only CPU, electronic devices, storage devices, network, and software applications, but till now, human resource (HR) has not been considered as Grid resources in details, despite its importance in many fields of science and society. HR is one of the important assets of organizations and plays a significant role in their success. Common and local methods to share and manage the human’s skills and knowledge in large geographically dispersed organizations are almost centralized, not reconfigurable and are based on the structure of organization; therefore, the optimal trade-off between the HR and job demands is a challenging problem. In addition, these methods did not consider some important parameters such as security issues, load balancing, organizations costs, unnecessary overtime, and employment issues. In order to overcome these defects, in this paper the new Grid architecture, named Expert Grid (EG), is proposed to find, employ, exploit, share, and manage the HR, its skills and knowledge effectively. EG provides infrastructure to increase the customer satisfactions by optimal distributions of requested jobs from customers. Experimental results show that the EG decreases the HR free time, customers delay time, and HR overtime in order to achieve high level of customers satisfaction and better performance of organizations.
