检索结果-内蒙古大学图书馆

IEEE TRANSACTIONS ON SUSTAINABLE computing 2018年第2期3卷 60-72页

作者： Gai, Keke Qiu, Meikang Zhao, Hui Sun, Xiaotong Pace Univ Dept Comp Sci New York NY 10038 USA Henan Univ Software Sch Kaifeng 475000 Henan Peoples R China

The substantial growth of the distributed computing using heterogeneous computing has enabled great expansions in Cyber Physical Systems (CPS). Combining CPS with heterogeneous cloud computing is an alternative approach for increasing sustainability of the system. However, execution of resource management in cloud systems is still encountering a few challenges, including the bottlenecks of the Web server capacities and task assignments in the heterogeneous cloud. The unstable service demands often result in service delays, which embarrasses the competitiveness of the enterprises. This paper addresses the problem of the task assignment in heterogeneous clouds, which is proved as a NP-hard problem. The proposed approach is called Smart cloud-based Optimizing Workload (SCOW) Model that uses predictive cloud capacities and considers sustainable factors to assign tasks to heterogeneous clouds. To reach the optimization objective, we propose a few algorithms, which include Workload Resource Minimization Algorithm (WRM), Smart Task Assignment (STA) Algorithm, and Task Mapping Algorithm (TMA). Our experimental evaluations have examined the performance of the proposed scheme.

关键词： heterogeneous cloud computing cyber-physical systems smart switch resource management dynamic programming task assignment

来源：评论

学校读者我要写书评

暂无评论

Virtual Machine Optimizations Using Markov Chain Data Analytics in heterogeneous cloud computing

Virtual Machine Optimizations Using Markov Chain Data Analyt...

引用

IEEE International Conference on Smart cloud (IEEE Smartcloud)

作者： Sun, Xiaotong Pace Univ Dept Comp Sci New York NY 10038 USA

ISBN: (纸本)9781509052639

Capacity planning is a crucial issue in web server deployments, which has a strong impact on the performance of the network connections and service quality. Prior researches have explored this field from various perspectives in order to obtain a deployable networking connection for successful service deliveries. However, the rapid development of heterogeneous cloud computing has brought a variety of new elements that impact on the networking capacity controls, improvements, and maintenances. Accurately addressing the network capacity in the heterogeneous cloud system has become a critical problem in guaranteeing the stability of the networks. This paper concentrates on this issue and presents a research work on data analytics by using Markov chain method for network capacity management. The proposed model is called Smart Predictive Capacity Management (SPCM). The model is proposed to analyze and understand the network conditions, which is designed to reduce or avoid the rate of networking disconnections or other interferences.

关键词： Data analytics capacity management Markov chain heterogeneous cloud computing

来源：评论

学校读者我要写书评

暂无评论

Aggregated-DAG Scheduling for Job Flow Maximization in heterogeneous cloud computing

Aggregated-DAG Scheduling for Job Flow Maximization in Heter...

引用

54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Saovapakhiran, Boonyarith Michailidis, George Devetsikiotis, Michael North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA

ISBN: (纸本)9781424492688

heterogeneous computing platforms such as Grid and cloud computing are becoming prevalent and available online. As a result, resource management in these platforms is fundamentally critical to their global performance. Under the assumption of jobs comprised of subtasks forming DAG jobs, we focus on how to increase utilization and achieve near-optimal throughput performance on heterogeneous platforms. Our analysis and proposed algorithm are analytically derived and establish that, by aggregating multiple jobs using good scheduling, a near-optimal throughput can be achieved. Consequently, its limit is asymptotically converging to a certain value and can be written in the form of the service time of subtasks. Furthermore, our analysis shows how to explicitly compute the optimal throughput of computing systems, an important task for such a complex scheduling problem. In addition, we derive a simple super-job scheduling and show that its performance in term of throughput is better than the well-known heterogeneous Earliest-Finish-Time (HEFT) algorithm.

关键词： HEFT algorithm Internet Internet Optimal scheduling Optimized production technology Schedules Scheduling Scheduling algorithm Throughput aggregated-DAG scheduling cloud computing complex scheduling problem directed acyclic graph grid computing heterogeneous cloud computing heterogeneous earliest-finish-time algorithm job flow maximization near-optimal throughput performance optimisation resource management scheduling subtask service time super-job scheduling

来源：评论

学校读者我要写书评

暂无评论

Energy Aware Workload Scheduling Metrics for Execution of Parallel Application in heterogeneous cloud computing Platform

Energy Aware Workload Scheduling Metrics for Execution of Pa...

引用

computing Conference

作者： Divyaprabha, K. N. Sudarshan, T. S. B. PES Univ Dept CSE Bangalore Karnataka India

ISBN: (纸本)9783031622717;9783031622694;9783031622687

The modern workload application is generally being executed on heterogeneous (CPU-GPU) cloud computing (HCC) environment. Minimizing execution cost with high quality of service is most important considering economic perspective of cloud Service Provider (CSP). The energy consumption plays a major part in cost and also makespan, meeting application deadlines. This work addresses the challenge involved in scheduling parallel workload under heterogeneous cloud computing for minimizing energy and makespan meeting task deadline prerequisite. Existing model are efficient in minimizing either energy or makespan;thus, resulting higher service provisioning cost. In addressing the research problem, this work present Energy Optimized Scheduling (EOS) for parallel workload application in heterogeneous cloud computing platform. Experiment results shows the proposed model is efficient in minimizing energy and makespan in comparison with existing workload scheduling approach. The result shows an average energy reduction of 23.22%, overall power consumption reduction of 85.06%, and makespan reduction of 78.2% for montage workflow in comparison with energy minimized scheduling.

关键词： Deadline Energy heterogeneous cloud computing Multi-objective Parallel workflow application Workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

Dynamic heterogeneous shortest job first (DHSJF): a task scheduling approach for heterogeneous cloud computing systems

引用

International Journal of Information Technology (Singapore) 2019年第4期11卷 653-657页

作者： Seth, Sonam Singh, Nipur Kanya Gurukul Campus Dehradun UK India Department of Computer Science Kanya Gurukul Campus Dehradun UK India

Data and computational centres consume a large amount of energy and limited by power density and computational capacity. As compared with the traditional distributed system and homogeneous system, the heterogeneous system can provide improved performance and dynamic provisioning. Dynamic provisioning can reduce energy consumption and map the dynamic requests with heterogeneous resources. The problem of resource utilization in heterogeneous computing system has been studied with variations. Scheduling of independent, non-communicating, variable length tasks in the concern of CPU utilization, low energy consumption, and makespan using dynamic heterogeneous shortest job first (DHSJF) model is discussed in this paper. Tasks are scheduled in such a manner to minimize the actual CPU time and overall system execution time or makespan. During execution, the load is balanced dynamically. Dynamic heterogeneity achieves reduced makespan that increases resource utilization. Some existing methods are not designed for fully heterogeneous systems. Our proposed method considers both dynamic heterogeneities of workload and dynamic heterogeneity of resources. Our proposed algorithm provides the better results than existing algorithm. The proposed algorithm has been simulated on cloudSim. © 2018, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： cloud computing heterogeneous cloud computing heterogeneous SJF Makespan Resource allocation Resource utilization Task scheduling

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid heuristic-based list scheduling algorithm in heterogeneous cloud computing environment for makespan optimization

引用

PARALLEL computing 2021年 108卷

作者： Shirvani, Mirsaeid Hosseini Talouki, Reza Noorian Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

An efficient workflow scheduling can potentially exploit heterogeneity of resources in heterogeneous cloud computing (HCC) platform commensurate with variable requirement of dependent tasks in a given workflow. Minimizing the total scheduling length, makespan, is essential for application performance in heterogeneous computing systems especially in cloud computing environment. The problem of scheduling a set of different dependent tasks onto a set of heterogeneous computational resources is a well-known NP-Hard problem. Therefore, no polynomial scheduling algorithm for computing the optimal solution exists. For approximating a solution to this problem many algorithms have been proposed, but majority of them have low efficiency. In this paper, a novel hybrid heuristic-based list scheduling (HH-LiSch) algorithm is presented for solving the dependent task scheduling in HCC systems in a bounded number of the fully connected virtual machines (VMs). The novelty of the current paper is to present the new task priority strategy, find appropriate VM's slot time, and utilize task duplication technique. Two novel task priority strategies are applied to prioritize tasks in an efficient ordered list. Then, during the scheduling process an insertion-based procedure is called to find an appropriate potential slot time for performing task duplication technique. If it works, the task duplication is added to rudimentary scheduling scheme. In this way, the final scheduling is gradually generated. To validate the work, the experiments are based on six real-world scientific workflows and a random task graph (RTG);then, the performance is evaluated in terms of makespan, Schedule Length Ratio (SLR), speedup and efficiency. The simulation results prove a significant improvement against other counterparts in literature.

关键词： Hybrid heuristic scheduling cloud computing heterogeneous cloud computing Dependent tasks Workflow List scheduler

来源：评论

学校读者我要写书评

暂无评论

Smart data deduplication for telehealth systems in heterogeneous cloud computing

引用

Journal of Communications and Information Networks 2016年第4期1卷 93-104页

作者： GAI Keke QIU Meikang SUN Xiaotong ZHAO Hui Department of Computer Science Pace UniversityNew York 10176USA College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Software School Henan UniversityKaifeng 475000China

The widespread application of heterogeneous cloud computing has enabled enormous advances in the real-time performance of telehealth systems.A cloud-based telehealth system allows healthcare users to obtain medical data from various data sources supported by heterogeneous cloud *** data duplications in distributed cloud databases is an alternative approach for achieving data sharing among multiple data ***,this approach results in additional storage space being used,even though reducing data duplications would lead to a decrease in data acquisitions and real-time *** address this issue,this paper focuses on developing a dynamic data deduplication method that uses an intelligent blocker to determine the working mode of data duplications for each data package in heterogeneous cloud-based telehealth *** proposed approach is named the SD2M(Smart Data Deduplication Model),in which the main algorithm applies dynamic programming to produce optimal solutions to minimizing the total cost of data *** implement experimental evaluations to examine the adaptability of the proposed approach.

关键词： data deduplication telehealth heterogeneous cloud computing optimal solution dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Energy-Aware Optimal Task Assignment for Mobile heterogeneous Embedded Systems in cloud computing 3

Energy-Aware Optimal Task Assignment for Mobile Heterogeneou...

引用

3rd IEEE International Conference on Cyber Security and cloud computing (IEEE CScloud) / 2nd IEEE International Conference of Scalable and Smart cloud (IEEE SSC)

作者： Gai, Keke Qiu, Meikang Zhao, Hui Liu, Meiqin Pace Univ Dept Comp Sci New York NY 10038 USA Henan Univ Software Sch Kaifeng 475000 Henan Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781509009466

Recent quick expansions of mobile heterogeneous embedded systems have led to a remarkable hardware upgrade that support multiple core processors. The energy consumption is becoming greater along with the computation capacity grows. cloud computing is considered one of the solutions to mitigating energy costs. However, the simply offloading the computations to the remote side cannot efficiently reduce the energy consumptions when the energy costs caused by wireless communications are greater than it is on mobile devices. In this paper, we focus on the problem of energy wastes when tasks are assigned to remote cloud servers or heterogeneous core processors. Our solution aims to minimize the total energy cost of the mobile heterogeneous embedded systems by using an optimal task assignment to heterogeneous cores and mobile clouds. The propose model is named as Energy-Aware heterogeneous Resource Management Model (EA-HRM2), which is supported by a main algorithm Optimal heterogeneous Task Assignment (OHTA) algorithm. Our experimental evaluations have proved our approach is effective to save energy when deploying heterogenous embedded systems in mobile cloud systems.

关键词： Energy-aware task assignment heterogeneous cloud computing NP-hard

来源：评论

学校读者我要写书评

暂无评论

Scalable Management of heterogeneous cloud Resources Based on Evolution Strategies Algorithm

引用

IEEE ACCESS 2022年 10卷 68778-68791页

作者： Loncar, Petra Loncar, Paula Univ Split Fac Elect Engn Mech Engn & Naval Architecture Split 21000 Croatia Univ Split Fac Sci Split 21000 Croatia

The emergence and rapid development of cloud computing have intensively changed the Information Technology paradigm. cloud computing found its wide application in science and engineering. cloud-based systems provide new solutions for scientific experiments and access to data distributed across data centers. Optimization of cloud resource management is becoming a significant area of research. This research focuses on scheduling tasks and processing scientific data in a heterogeneous cloud environment where most jobs require a large number of resources and computing power. Our approach proposes a strategy for optimizing task scheduling across different virtual machines and data centers based on the metaheuristic Evolution Strategies algorithm. The Evolution Strategies algorithm was tested, which has not been used in this domain. As an essential property of the system, scalability has played an important role in selecting the algorithm. We created a model and added a Longest Job First broker policy. Compared to the standard Genetic Algorithm, our approach has shown improvements in measured metrics. After testing under different loads, the proposed strategy gave promising results and achieved a better makespan, larger average resource utilization, better throughput, less average execution time, a smaller degree of imbalance, and scalability.

关键词： cloud computing Scalability Task analysis Resource management Data centers Large Hadron Collider Scheduling cloud computing distributed management evolution strategies heterogeneous cloud computing resource management scalability task scheduling

来源：评论

学校读者我要写书评

暂无评论

An energy-efficient big data workflow scheduling algorithm under budget constraints for heterogeneous cloud environment

引用

JOURNAL OF SUPERcomputing 2021年第10期77卷 11946-11985页

作者： Ahmad, Wakar Alam, Bashir Atman, Aman Jamia Millia Islamia Dept Comp Engn Fac Engn & Technol New Delhi 110025 India Int Inst Informat Technol Hyderabad 500032 India

Infrastructure as a service model of cloud computing provides a tremendous amount of high-performance computing systems for the execution of scientific workflow applications. However, due to explosive growth in energy consumption and high charge cost of using these cloud systems, energy-efficient workflow scheduling under budget constraints becomes the most challenging issue. Very few research works have been done that consider the stated issue. Most of them mainly focus on the minimization of schedule length under user-specified budget constraints or energy consumption constraints. In this article, we propose an energy-efficient workflow scheduling algorithm named reducing energy consumption using fair pre-assignment of available budget (RECFPAB) that reduces energy consumption under client-specified budget constraints. The RECFPAB introduces a flexible mechanism to save energy consumption with the inclusion of energy and cost coefficient factor that enables fair distribution of available budget for unscheduled tasks of the workflow application. In order to compare the performance of the proposed algorithm, an energy-efficient version of the popular existing algorithms such as heterogeneous budget constrained scheduling and minimizing schedule length using budget level are introduced. The experimental evaluation based on Genome, LIGO, and Montage applications shows that RECFPAB gives significant results in comparison with considered algorithms.

关键词： Budget constraint Energy consumption heterogeneous cloud computing Workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：