摘要：
Tasks scheduling problem is the key challenge in cloud computing system. For reducing the execution cost of workflow tasks scheduling under the deadline and the budget constraint, a workflow tasks scheduling algorithm based on genetic algorithm in cloud computing is proposed. In our algorithm, each task is assigned priority by an top-down leveling method. By this top-down leveling method, all workflow tasks are divided into the different levels, which can promote the parallel execution of workflow tasks. When code the solution of tasks scheduling, we design a two dimension coding method. And, we design a new genetic crossover and mutation operation to produce new different offsprings for increasing the population diversity. Through the fitness function synchronously considering the scheduling time and the scheduling cost, we can evaluate the individual fitness of population. Through the simulation experiments, we evaluate the performance of our algorithm based on realistic workflows model. The results show that our algorithm has a better performance in reducing the workflow scheduling cost.
摘要：
An algorithm of task scheduling with data dependent is proposed. The algorithm consists of two steps: determining the priority of tasks and selecting the resources for tasks scheduling. In the step of prioritizing tasks, a new definition of task rank is introduced, which improves the traditional definition by taking the sum instead of the maximum of the upper and the lower ranks, therefore better representing the residual load of tasks in the workflow. In the step of selecting resources, a new method based on the fastest computation time is designed. In addition, the selection of critical path used in task scheduling depends on the new definition of task ranks, and comparison is given with the traditional methods. Finally, we analysis and compare the results of several task scheduling methods and prove that our algorithm outperforms better than the existing algorithms.
摘要：
Load balance problem has become a research hot spot problem of distributed computing field, whose objective is to ensure that each physical resource is allocated efficiently and fairly. As a typical distributed computing environment, the load balance of resource allocation in cloud computing environment is implemented by the live migration of virtual machines. A virtual machine resource provision genetic algorithm based on load balance, LB-GA is presented. Combined with the advantages of genetic algorithm, the proposed algorithm has an excellent outcome in balance of resource allocation. The simulation experimental results show that LB-GA can not only get fewer virtual machine migration amount, but also can implement load balance of cloud resource allocation. In the end, an application scenario of load balance in cloud computing environment is constructed to demonstrate the importance of our algorithm.
摘要：
Notice of Violation of IEEE Publication Principles “Energy-aware Virtual Machine Management Optimization in Clouds” by Xiaoqing Zhang in 2017 3rd IEEE International Conference on Computer and Communications, December 2017, pp.2434-2438 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This paper copied content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. “Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers” by Anton Beloglazov and Rajkumar Buyya in Concurrency and Computation: Practice and Experience, October 2011, pp.1397-1420 “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing” by Anton Beloglazov, Jemal Abawajy, and Rajkumar Buyya in Future Generation Computer Systems, May 2011, pp.755-768 Cloud computing provides a kind of dynamic and scalable service on demand. However, clouds consume huge amounts of electrical energy. Meanwhile, delivering the negotiated QoS defined as Service Level Agreement (SLA) to users is necessary. A virtual machine placement strategy based on the equilibrium between energy and SLA is proposed. Aiming at dynamical changes of application workloads, an adaptive placement strategy RLWR based on robust local weight regression is presented, which decides the overload time of hosts dynamically according to the historical resource occupation of application workload. Then, two virtual machine migration selection algorithms, MPM and MNM are presented. The migrated virtual machines are deployed using bin-packing algorithm PBFDH. Contrasting to static algorithms such as STH, MPA and DVFS, virtual machines are not only deployed on fewer hosts in our algorithm, which promotes energy efficiency, but the load prediction can bring high-reliable QoS delivery and avoid overmuch SLA violations. Experimental results show that our strategy has an obvious effect on decreasing SLA violation under ensuring energy efficiency.
期刊：
International Journal of Grid and Distributed Computing,2016年9(1):237-248 ISSN：2005-4262
通讯作者：
Zhang, Xiaoqing(zxqtzy@126.com)
作者机构：
[Xu, Lijun; Wang, Weifeng] School of Computer and Information Engineering, Xinxiang University, Xinxiang, China;[Zhang, Xiaoqing] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
通讯机构：
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
摘要：
Meeting users’ Quality of Service (QoS) requirements is a key problem of tasks scheduling in cloud computing. A cloud tasks scheduling algorithm CTS_QoS based on maximal QoS satisfaction and minimal QoS distance between tasks and resources is presented in this paper. Under meeting maximal QoS satisfaction of user’s tasks, CTS_QoS can select the resources with minimal QoS distance to map. Experimental results show that though CTS_QoS cannot guarantee a high resource utilization, it can gain users’ QoS satisfaction maximization.
期刊：
Journal of Computational Information Systems,2015年11(16):6037-6045 ISSN：1553-9105
通讯作者：
Zhang, Xiaoqing(51449902@qq.com)
作者机构：
[Liu, Renfeng] School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China;[Qian, Qiongfen] Department 4, Air Force Early Warning Academy, Wuhan, China;[Zhang, Xiaoqing] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China;[Xu, Lijun] School of Computer and Information Engineering, Xinxiang University, Xinxiang, China
通讯机构：
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China
作者：
Zhang, Xiaoqing;Qiu, Lan*;Qian, Qiongfen;Li, Yaqin
期刊：
Journal of Computational Information Systems,2015年11(14):5251-5258 ISSN：1553-9105
通讯作者：
Qiu, Lan
作者机构：
[Li, Yaqin; Zhang, Xiaoqing] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China;[Qian, Qiongfen] Department 4, Air Force Early Warning Academy, Wuhan, China;[Qiu, Lan] Library, Wuhan University, Wuhan, China
通讯机构：
Library, Wuhan University, Wuhan, China
关键词：
Bin Packing;Cloud Computing;Constraint Satisfaction;Virtual Machine Placement
