Relevant bibliographies by topics / Virtualization: Scheduling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Virtualization: Scheduling'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Virtualization: Scheduling"

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Hong, Cheol-Ho, Ivor Spence, and Dimitrios S. Nikolopoulos. "GPU Virtualization and Scheduling Methods." ACM Computing Surveys 50, no. 3 (2017): 1–37. http://dx.doi.org/10.1145/3068281.

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Sharma, Gourav Prateek, Wouter Tavernier, Didier Colle, and Mario Pickavet. "Scheduling for Media Function Virtualization." Future Internet 13, no. 7 (2021): 167. http://dx.doi.org/10.3390/fi13070167.

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Broadcasters are building studio architectures based on commercial off-the-shelf (COTS) IT hardware because of advantages such as cost reduction, ease of management, and upgradation. Media function virtualization (MFV) leverages IP networking to transport media streams between virtual media functions (VMFs), where they are processed. Media service deployment in an MFV environment entails solving the VMF-FG scheduling problem to ensure that the required broadcast quality guarantees are fulfilled. In this paper, we formulate the VMF-FG scheduling problem and propose a greedy-based algorithm to solve it. The evaluation of the algorithm is carried in terms of the end-to-end delay and VMF queuing delay. Moreover, the importance of VMF-FG decomposition in upgradation to higher-quality formats is also highlighted.
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Kumari, Shabnam, Shabnam Kumari, and Shahdab Payami. "Virtualization and Scheduling in Real Time OS: A Survey." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (2017): 434–39. http://dx.doi.org/10.31142/ijtsrd172.

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Boutcher, David, and Abhishek Chandra. "Does virtualization make disk scheduling passé?" ACM SIGOPS Operating Systems Review 44, no. 1 (2010): 20–24. http://dx.doi.org/10.1145/1740390.1740396.

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Yang, Jungwoo, Hyungseok Kim, Sangwon Park, Changki Hong, and Insik Shin. "Implementation of compositional scheduling framework on virtualization." ACM SIGBED Review 8, no. 1 (2011): 30–37. http://dx.doi.org/10.1145/1967021.1967025.

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Lan, Li, Chen Jianya, Cui Hongyan, Huang Tao, and Liu Yunjie. "Resource scheduling virtualization in service-oriented future Internet architecture." Journal of China Universities of Posts and Telecommunications 22, no. 4 (2015): 92–100. http://dx.doi.org/10.1016/s1005-8885(15)60672-0.

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Zhang, Yujie, Jiabin Yuan, Xiangwen Lu, and Xingfang Zhao. "Multi-GPU Parallel Computing and Task Scheduling under Virtualization." International Journal of Hybrid Information Technology 8, no. 7 (2015): 253–66. http://dx.doi.org/10.14257/ijhit.2015.8.7.24.

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Wang, Bei, Yuxia Cheng, Wenzhi Chen, et al. "Efficient consolidation-aware VCPU scheduling on multicore virtualization platform." Future Generation Computer Systems 56 (March 2016): 229–37. http://dx.doi.org/10.1016/j.future.2015.08.007.

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Liu, Jun Jun. "Construction on Virtualized Environment for Digital Library Based on Cloud Computing Technology." Applied Mechanics and Materials 687-691 (November 2014): 3019–22. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3019.

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Cloud computing technology is emerging technology in the field of information technology, and its technical advantage has brought new opportunities and challenges for the development and service of digital library in the Internet era. Virtualization is the key technology of cloud computing, and the paper discusses the virtualization of digital library in the cloud computing environment from technical level. Firstly, the paper introduced cloud computing and virtualization technology; then created a virtualized environment for digital library based on the cloud computing technology, and described the function of various levels; finally, the capacity of virtual machines appointment scheduling can be calculated according to formulas. The paper has great significance in enhancing the efficiency and quality of library service, constructing resource sharing system of digital library.
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Zhou, Guo Hong. "Study of Cloud Computing and Advanced Manufacturing Technology under Information Technology Development." Applied Mechanics and Materials 484-485 (January 2014): 227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.484-485.227.

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Cloud computing can be regarded as the integration of grid computing and virtualization technology: namely the use of grid distributed computing processing power of IT resources and build into a resource pool, plus on a mature server virtualization, storage virtualization technology make users can real-time monitoring and deployment of resources. Cloud computing and advanced manufacturing technology is facing many challenges during the process of development in the future, including security and privacy will become a primary issue. This study focused on relations with the important areas of emerging industries, the fast scheduling network resources in the cloud computing and advanced manufacturing technology key issues, as well as cloud computing security management issues related to cloud computing and advanced manufacturing technology.
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Dissertations / Theses on the topic "Virtualization: Scheduling"

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Burns, Kevin Patrick. "Real-Time Hierarchical Scheduling of Virtualized Systems." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/51790.

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In industry there has been a large focus on system integration and server consolidation, even for real-time systems, leading to an interest in virtualization. However, many modern hypervisors do not inherently support the strict timing guarantees of real-time applications. There are several challenges that arise when trying to virtualize a real-time application. One key challenge is to maintain the guest's real-time guarantees. In a typical virtualized environment there is a hierarchy of schedulers. Past solutions solve this issue by strict resource reservation models. These reservations are pessimistic as they accommodate the worst case execution time of each real-time task. We model real-time tasks using probabilistic execution times instead of worst case execution times which are difficult to calculate and are not representative of the actual execution times. In this thesis, we present a probabilistic hierarchical framework to schedule real-time virtual machines. Our framework reduces the number CPUs reserved for each guest by up to 45%, while only decreasing the deadline satisfaction by 2.7%. In addition, we introduce an introspection mechanism capable of gathering real-time characteristics from the guest systems and present them to the host scheduler. Evaluations show that our mechanism incurs up to 21x less overhead than that of bleeding edge introspection techniques when tracing real-time events.<br>Master of Science
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Svärd, Petter. "Dynamic Cloud Resource Management : Scheduling, Migration and Server Disaggregation." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-87904.

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A key aspect of cloud computing is the promise of infinite, scalable resources, and that cloud services should scale up and down on demand. This thesis investigates methods for dynamic resource allocation and management of services in cloud datacenters, introducing new approaches as well as improvements to established technologies.Virtualization is a key technology for cloud computing as it allows several operating system instances to run on the same Physical Machine, PM, and cloud services normally consists of a number of Virtual Machines, VMs, that are hosted on PMs. In this thesis, a novel virtualization approach is presented. Instead of running each PM isolated, resources from multiple PMs in the datacenter are disaggregated and exposed to the VMs as pools of CPU, I/O and memory resources. VMs are provisioned by using the right amount of resources from each pool, thereby enabling both larger VMs than any single PM can host as well as VMs with tailor-made specifications for their application. Another important aspect of virtualization is live migration of VMs, which is the concept moving VMs between PMs without interruption in service. Live migration allows for better PM utilization and is also useful for administrative purposes. In the thesis, two improvements to the standard live migration algorithm are presented, delta compression and page transfer reordering. The improvements can reduce migration downtime, i.e., the time that the VM is unavailable, as well as the total migration time. Postcopy migration, where the VM is resumed on the destination before the memory content is transferred is also studied. Both userspace and in-kernel postcopy algorithms are evaluated in an in-depth study of live migration principles and performance.Efficient mapping of VMs onto PMs is a key problem for cloud providers as PM utilization directly impacts revenue. When services are accepted into a datacenter, a decision is made on which PM should host the service VMs. This thesis presents a general approach for service scheduling that allows for the same scheduling software to be used across multiple cloud architectures. A number of scheduling algorithms to optimize objectives like revenue or utilization are also studied. Finally, an approach for continuous datacenter consolidation is presented. As VM workloads fluctuate and server availability varies any initial mapping is bound to become suboptimal over time. The continuous datacenter consolidation approach adjusts this VM-to-PM mapping during operation based on combinations of management actions, like suspending/resuming PMs, live migrating VMs, and suspending/resuming VMs. Proof-of-concept software and a set of algorithms that allows cloud providers to continuously optimize their server resources are presented in the thesis.
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Bai, Hao. "Scheduling Design for Advance Virtual Network Services." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6461.

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Network virtualization allows operators to host multiple client services over their base physical infrastructures. Today, this technique is being used to support a wide range of applications in cloud computing services, content distribution, large data backup, etc. Accordingly, many different algorithms have also been developed to achieve efficient mapping of client virtual network (VN) requests over physical topologies consisting of networking infrastructures and datacenter compute/storage resources. However as applications continue to expand, there is a growing need to implement scheduling capabilities for virtual network demands in order to improve network resource utilization and guarantee quality of service (QoS) support. Now the topic of advance reservation (AR) has been studied for the case of scheduling point-to-point connection demands. Namely, many different algorithms have been developed to support various reservation models and objectives. Nevertheless, few studies have looked at scheduling more complex "topology-level'' demands, including virtual network services. Moreover, as cloud servers expand, many providers want to ensure user quality support at future instants in time, e.g., for special events, sporting venues, conference meetings, etc. In the light of above, this dissertation presents one of the first studies on advance reservation of virtual network services. First, the fixed virtual overlay network scheduling problem is addressed as a special case of the more generalized virtual network scheduling problem and a related optimization presented. Next, the complete virtual network scheduling problem is studied and a range of heuristic and meta-heuristic solutions are proposed. Finally, some novel flexible advance reservation models are developed to improve service setup and network resource utilization. The performance of these various solutions is evaluated using various methodologies (discrete event simulation and optimization tools) and comparisons made with some existing strategies.
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Drescher, Michael Stuart. "A Flattened Hierarchical Scheduler for Real-Time Virtual Machines." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/78125.

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The recent trend of migrating legacy computer systems to a virtualized, cloud-based environment has expanded to real-time systems. Unfortunately, modern hypervisors have no mechanism in place to guarantee the real-time performance of applications running on virtual machines. Past solutions to this problem rely on either spatial or temporal resource partitioning, both of which under-utilize the processing capacity of the host system. Paravirtualized solutions in which the guest communicates its real-time needs have been proposed, but they cannot support legacy operating systems. This thesis demonstrates the shortcomings of resource partitioning using temporally-isolated servers, presents an alternative solution to the scheduling problem called the KairosVM Flattening Scheduling Algorithm, and provides an implementation of the algorithm based on Linux and KVM. The algorithm is analyzed theoretically and an exact schedulability test for the algorithm is derived. Simulations show that the algorithm can schedule more than 90% of all randomly generated tasksets with a utilization less than 0.95. In comparison to the state-of-the-art server based approach, the KairosVM Flattening Scheduling Algorithm is able to schedule more than 20 times more tasksets with utilization of 0.95. Experimental results demonstrate that the Linux-based implementation is able to match the deadline satisfaction ratio of a state-of-the-art server-based approach when the taskset is schedulable using the state-of-the-art approach. When tasksets are unschedulable, the implementation is able to increase the deadline satisfaction ratio of Vanilla KVM by up to 400%. Furthermore, unlike paravirtualized solutions, the implementation supports legacy systems through the use of introspection.<br>Master of Science
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Venkatesh, Venkataramanan. "Optimization of CPU Scheduling in Virtual Machine Environments." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33380.

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Data centres and other infrastructures in the field of information technology suffer from the major issue of ‘server sprawl’, a term used to depict the situation wherein a number of servers consume resources inefficiently, when compared to the business value of outcome obtained from them. Consolidation of servers, rather than dedicating whole servers to individual applications, optimizes the usage of hardware resources, and virtualization achieves this by allowing multiple servers to share a single hardware platform. Server virtualization is facilitated by the usage of hypervisors, among which Xen is widely preferred because of its dual virtualization modes, virtual machine migration support and scalability. This research work involves an analysis of the CPU scheduling algorithms incorporated into Xen, on the basis of the algorithm’s performance in different workload scenarios. In addition to performance evaluation, the results obtained lay emphasis on the importance of compute intensive or I/O intensive domain handling capacity of a hypervisor’s CPU scheduling algorithm in virtualized server environments. Based on this knowledge, the selection of CPU scheduler in a hypervisor can be aligned with the requirements of the hosted applications. A new credit-based VCPU scheduling scheme is proposed, in which the credits remaining for each VCPU after every accounting period plays a significant role in the scheduling decision. The proposed scheduling strategy allows those VCPUs of I/O intensive domains to supersede others, in order to favour the reduction of I/O bound domain response times and the subsequent bottleneck in the CPU run queue. Though a small percentage of context switch overhead is introduced, the results indicate substantial improvement of I/O handling and fairness in re-source allocation between the host and guest domains.
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Wenkai, Wang. "COMPARISON OF BUDGET BORROWING AND BUDGET ADAPTATION IN HIERARCHICAL SCHEDULING FRAMEWORK." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-31091.

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System virtualization technology is widely used in computing nowadays. In embedded domain, it is used as a solution to resource sharing among independent applications. One of the areas is to apply virtualization technique to real-time embedded systems with timing constraints. Multi-level adaptive hierarchical scheduling (AdHierSched) framework is a virtualized real-time framework, which runs in the Linux operating system. Šis virtualized framework has ability to adapt the CPU partition sizes according to their need through monitoring their demand during run-time, which yields more appropriate processor assignment. However, the performance of the virtualized framework is still unknown when the budget borrowing mechanism is enabled. To this end, in this thesis, we explore a new direction for performing the adaptation of CPU partition. We design and implement a budget borrowing mechanism for dynamic adaptation of resource parameters in AdHierSched framework. Extensive simulations are performed in this thesis, which are used to study and compare di‚erent adaptation mechanisms with our approach. From the results of experiments, we conclude that when the framework works only with budget borrowing controller, the results are not as good as only running a budget controller in the AdHierSched framework. However, while running both of the controllers at the same time, the experiments results are good enough. We also analyze the overhead of the framework at the end of the evaluation. Finally, we conclude the thesis by presenting the possible future work.
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Alnowiser, Abdulaziz Mohammed. "TOWARD ENERGY-EFFICIENT SCHEDULING USING WEIGHTED ROUND-ROBIN AND VM REUSE." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1303.

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AN ABSTRACT OF THE THESIS OF Abdulaziz M. AlNowiser, for the Master of Science degree in Computer Science, presented on November 1, 2013, at Southern Illinois University Carbondale. TITLE: TOWARD ENERGY-EFFICIENT SCHEDULING USING WEIGHTED ROUND- ROBIN AND VM REUSE MAJOR PROFESSOR: Dr. Michelle M. Zhu In recent years, the rapid evolving Cloud Computing technologies multiply challenges such as minimizing power consumption and meeting Quality-of-Services (QoS) requirements in the presence of heavy workloads from a large number of users using shared computing resources. Powering a middle-sized data center normally consumes 80,000kW power every year and computer servers consume around .5% of the global power [1]. Statistics for 5000 production servers over a six-month period show that only 10-50% of the total capacity has been effectively used, and a large portion of the resources is actually wasted. In order to address the skyrocket energy cost from the high level resource management aspect, we propose an energy efficient job scheduling approach based on a modified version of Weighted Round Robin scheduler that incorporates VMs reuse and live VM migration without compromising the Service Level Agreement (SLA). The Weighted Round Robin scheduler can monitor the running VMs status for possible VM sharing for job consolidation or migration. In addition, the VMs utilization rate is observed to start live migration from the over-utilizing Processing Element (PE) to under-utilized PEs or to the hibernated PEs by sending WOL (Wake-On-LAN) signal to activate them. The simulation experiments are conducted under the CloudReports environment based on open source CloudSim simulator. The comparisons with other similar scheduling algorithms demonstrate that our enhanced Weighted Round Robin algorithm (EWRR) can achieve considerable better performance in terms of energy consumption and resource utilization rate.
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Lee, Min. "Memory region: a system abstraction for managing the complex memory structures of multicore platforms." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50398.

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The performance of modern many-core systems depends on the effective use of their complex cache and memory structures, and this will likely become more pronounced with the impending arrival of on-chip 3D stacked and non-volatile off-chip byte-addressable memory. Yet to date, operating systems have not treated memory as a first class schedulable resource, embracing memory heterogeneity. This dissertation presents a new software abstraction, called ‘memory region’, which denotes the current set of physical memory pages actively used by workloads. Using this abstraction, memory resources can be scheduled for applications to fully exploit a platform's underlying cache and memory system, thereby gaining improved performance and predictability in execution, particularly for the consolidated workloads seen in virtualized and cloud computing infrastructures. The abstraction's implementation in the Xen hypervisor involves the run-time detection of memory regions, the scheduled mapping of these regions to caches to match performance goals, and maintaining region-to-cache mappings using per-cache page tables. This dissertation makes the following specific contributions. First, its region scheduling method proposes that the location of memory blocks rather than CPU utilization is the principal determinant where workloads are run. It proposes a new scheduling method, the region scheduling that the location of memory blocks determines where the workloads are run. Second, treating memory blocks as first-class resources, new methods for efficient cache management are shown to improve application performance as well as the performance of certain operating system functions. Third, explicit memory scheduling makes it possible to disaggregate operating systems, without the need to change OS sources and with only small markups of target guest OS functionality. With this method, OS functions can be mapped to specific desired platform components, such as file system confined to running on specific cores and using only certain memory resources designated for its use. This can improve performance for applications heavily dependent on certain OS functions, by dynamically providing those functions with the resources needed for their current use, and it can prevent performance-critical application functionality from being needlessly perturbed by OS functions used for other purposes or by other jobs. Fourth, extensions of region scheduling can also help applications deal with the heterogeneous memory resources present in future systems, including on-chip stacked DRAM and NUMA or even NVRAM memory modules. More generally, regions scheduling is shown to apply to memory structures with well-defined differences in memory access latencies.
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Smith, James William. "Investigating performance and energy efficiency on a private cloud." Thesis, University of St Andrews, 2014. http://hdl.handle.net/10023/6540.

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Organizations are turning to private clouds due to concerns about security, privacy and administrative control. They are attracted by the flexibility and other advantages of cloud computing but are wary of breaking decades-old institutional practices and procedures. Private Clouds can help to alleviate these concerns by retaining security policies, in-organization ownership and providing increased accountability when compared with public services. This work investigates how it may be possible to develop an energy-aware private cloud system able to adapt workload allocation strategies so that overall energy consumption is reduced without loss of performance or dependability. Current literature focuses on consolidation as a method for improving the energy-efficiency of cloud systems, but if consolidation is undesirable due to the performance penalties, dependability or latency then another approach is required. Given a private cloud in which the machines are constant, with no machines being powered down in response to changing workloads, and a set of virtual machines to run, each with different characteristics and profiles, it is possible to mix the virtual machine placement to reduce energy consumption or improve performance of the VMs. Through a series of experiments this work demonstrates that workload mixes can have an effect on energy consumption and the performance of applications running inside virtual machines. These experiments took the form of measuring the performance and energy usage of applications running inside virtual machines. The arrangement of these virtual machines on their hosts was varied to determine the effect of different workload mixes. The insights from these experiments have been used to create a proof-of- concept custom VM Allocator system for the OpenStack private cloud computing platform. Using CloudMonitor, a lightweight monitoring application to gather data on system performance and energy consumption, the implementation uses a holistic view of the private cloud state to inform workload placement decisions.
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Delgado, Javier. "Scheduling Medical Application Workloads on Virtualized Computing Systems." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/633.

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This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of “cloud computing” services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves. In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions: An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments. A performance prediction methodology applicable to the target environment. A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees. In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia. Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20-30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.
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Book chapters on the topic "Virtualization: Scheduling"

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Quesnel, Flavien. "Contributions of Virtualization." In Scheduling of Large-Scale Virtualized Infrastructures. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118790335.ch2.

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Quesnel, Flavien. "Distributed Infrastructures Before the Rise of Virtualization." In Scheduling of Large-Scale Virtualized Infrastructures. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118790335.ch1.

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Xu, Daoqiang, Yefei Li, Ming Yin, Xin Li, Hao Li, and Zhuzhong Qian. "A Reliable Resource Scheduling for Network Function Virtualization." In Security, Privacy, and Anonymity in Computation, Communication, and Storage. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72395-2_24.

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Jatzkowski, Jan, Marcio Kreutz, and Achim Rettberg. "Hierarchical Multicore-Scheduling for Virtualization of Dependent Real-Time Systems." In System Level Design from HW/SW to Memory for Embedded Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-90023-0_9.

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Yang, Chen, Xudong Chai, and Faguang Zhang. "Research on Co-simulation Task Scheduling Based on Virtualization Technology under Cloud Simulation." In AsiaSim 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34390-2_48.

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"Dynamic Resource Scheduling Framework with Heterogeneous Virtualization Platforms Supported." In International Conference on Computer Technology and Development, 3rd (ICCTD 2011). ASME Press, 2011. http://dx.doi.org/10.1115/1.859919.paper45.

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Suthakar, K. Indira, and M. K. Kavitha Devi. "Resource Scheduling for Big Data on Cloud." In Advances in Data Mining and Database Management. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9767-6.ch013.

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Cloud computing is based on the concepts of distributed computing, grid computing, utility computing and virtualization. It is a virtual pool of resources which are provided to users via Internet. It gives users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. Cloud computing service providers' one of the goals is to use the resources efficiently and gain maximum profit. This leads to task scheduling as a core and challenging issue in cloud computing. This paper gives different scheduling strategies and algorithms in cloud computing.
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Mehta, Shikha, and Parmeet Kaur. "Scheduling Data Intensive Scientific Workflows in Cloud Environment Using Nature Inspired Algorithms." In Nature-Inspired Algorithms for Big Data Frameworks. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5852-1.ch008.

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Workflows are a commonly used model to describe applications consisting of computational tasks with data or control flow dependencies. They are used in domains of bioinformatics, astronomy, physics, etc., for data-driven scientific applications. Execution of data-intensive workflow applications in a reasonable amount of time demands a high-performance computing environment. Cloud computing is a way of purchasing computing resources on demand through virtualization technologies. It provides the infrastructure to build and run workflow applications, which is called ‘Infrastructure as a Service.' However, it is necessary to schedule workflows on cloud in a way that reduces the cost of leasing resources. Scheduling tasks on resources is a NP hard problem and using meta-heuristic algorithms is an obvious choice for the same. This chapter presents application of nature-inspired algorithms: particle swarm optimization, shuffled frog leaping algorithm and grey wolf optimization algorithm to the workflow scheduling problem on the cloud. Simulation results prove the efficacy of the suggested algorithms.
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Kashyap, Rekha, and Deo Prakash Vidyarthi. "A Secured Real Time Scheduling Model for Cloud Hypervisor." In Cloud Security. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8176-5.ch026.

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Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work, SA-RT-CreditScheduler (Security-aware and Real-time) is a dual objective scheduler, which maximizes the success rate of VMs in best possible security range as specified by the VM owner. Though the algorithms can be used by any hypervisor, for the current work they have been implemented on Xen hypervisor. Their effectiveness is validated by comparing it with Xen's, Credit and SEDF scheduler, for security demanding tasks with stringent deadline constraints.
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Mao, Ming, and Marty Humphrey. "Resource Provisioning in the Cloud." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6178-3.ch023.

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It is a challenge to provision and allocate resources in the Cloud so as to meet both the performance and cost goals of Cloud users. For a Cloud consumer, the ability to acquire and release resources dynamically and trivially in the Cloud, while being a powerful and useful aspect, complicates the resource provisioning and allocation task in the Cloud. While on the one hand, resource under-provisioning may hurt application performance and deteriorate service quality; on the other hand, resource over-provisioning could cost users more and offset Cloud advantages. Although resource management and job scheduling have been studied extensively in the Grid environments and the Cloud shares many common features with the Grid, the mapping from user objectives to resource provisioning and allocation in the Cloud has many challenges due to the seemingly unlimited resource pools, virtualization, and isolation features provided by the Cloud. This chapter focuses on surveying the research trends in resource provisioning in the Cloud based on several factors such as the type of the workload, the VM heterogeneity, data transfer requirements, solution methods, and optimization goals and constraints, and attempts to provide guidelines for future research.
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Conference papers on the topic "Virtualization: Scheduling"

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Lee, Jaewoo, Sisu Xi, Sanjian Chen, et al. "Realizing Compositional Scheduling through Virtualization." In 2012 IEEE 18th Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 2012. http://dx.doi.org/10.1109/rtas.2012.20.

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Hu, Chunsheng, Chengdong Xu, Xiaobo Cao, and Pengfei Zhang. "Study on the Multi-Granularity Virtualization of Manufacturing Resources." In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1174.

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Abstract:
As a new kind of networked manufacturing mode, Cloud Manufacturing needs to construct a large-scale virtual manufacturing resources pool firstly. For a reasonable and effective construction of the virtual manufacturing resources pool, the point of multi-granularity virtualization is proposed. Firstly, by analyzing the process of resources virtualization, the meanings of manufacturing resources, virtualization modeling and virtualization accessing are stated, and the relationships between them are illustrated; secondly, by analyzing the compositionality of resources, two resources categories are deduced; thirdly, the granularity factor, which have serious impacts on the resources-virtualization, resources-matching and resources-scheduling, are discussed; finally, a multi-granularity virtualization method of manufacturing resources is proposed.
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Etsion, Yoav, Tal Ben-Nun, and Dror G. Feitelson. "A global scheduling framework for virtualization environments." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161228.

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Nicod, Jean-Marc, Laurent Philippe, Veronika Rehn-Sonigo, and Lamiel Toch. "Using Virtualization and Job Folding for Batch Scheduling." In 2011 10th International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, 2011. http://dx.doi.org/10.1109/ispdc.2011.15.

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Tian, Jia, Yuyang Du, and Hongliang Yu. "Characterizing SMP Virtual Machine Scheduling in Virtualization Environment." In 4th IEEE Int'l Conference on Cyber, Physical and Social Computing (CPSCom). IEEE, 2011. http://dx.doi.org/10.1109/ithings/cpscom.2011.19.

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Chang, Zhibo, Jian Li, Ruhui Ma, Zhiqiang Huang, and Haibing Guan. "Adjustable Credit Scheduling for High Performance Network Virtualization." In 2012 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2012. http://dx.doi.org/10.1109/cluster.2012.27.

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Qu, Long, Chadi Assi, and Khaled Shaban. "Network function virtualization scheduling with transmission delay optimization." In NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2016. http://dx.doi.org/10.1109/noms.2016.7502870.

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Zhao, Qian, Masahiro Iida, and Toshinori Sueyoshi. "A Study of FPGA Virtualization and Accelerator Scheduling." In ASPLOS '17: Architectural Support for Programming Languages and Operating Systems. ACM, 2017. http://dx.doi.org/10.1145/3129457.3129503.

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Kamel, Mahmoud I., Long Bao Le, and Andre Girard. "LTE Wireless Network Virtualization: Dynamic Slicing via Flexible Scheduling." In 2014 IEEE 80th Vehicular Technology Conference (VTC Fall). IEEE, 2014. http://dx.doi.org/10.1109/vtcfall.2014.6966044.

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Jiang, Xiaofeng, Hongsheng Xi, Qing Chen, Jun Li, and Fengbin Li. "Resources Scheduling Optimization for Multi-service Cloud Computing Based on Queuing Networks." In Annual International Conference on Cloud Computing and Virtualization. Global Science & Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-5837-7_219.

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