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Relevant bibliographies by topics / Dynamic CPU resource allocation

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Academic literature on the topic 'Dynamic CPU resource allocation'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Dynamic CPU resource allocation"

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Hong, Cheol-Ho, Kyungwoon Lee, Hyunchan Park, and Chuck Yoo. "ANCS: Achieving QoS through Dynamic Allocation of Network Resources in Virtualized Clouds." Scientific Programming 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4708195.

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To meet the various requirements of cloud computing users, research on guaranteeing Quality of Service (QoS) is gaining widespread attention in the field of cloud computing. However, as cloud computing platforms adopt virtualization as an enabling technology, it becomes challenging to distribute system resources to each user according to the diverse requirements. Although ample research has been conducted in order to meet QoS requirements, the proposed solutions lack simultaneous support for multiple policies, degrade the aggregated throughput of network resources, and incur CPU overhead. In this paper, we propose a new mechanism, called ANCS (Advanced Network Credit Scheduler), to guarantee QoS through dynamic allocation of network resources in virtualization. To meet the various network demands of cloud users, ANCS aims to concurrently provide multiple performance policies; these include weight-based proportional sharing, minimum bandwidth reservation, and maximum bandwidth limitation. In addition, ANCS develops an efficient work-conserving scheduling method for maximizing network resource utilization. Finally, ANCS can achieve low CPU overhead via its lightweight design, which is important for practical deployment.

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S., Karthikeyan, Hari Seetha, and Manimegalai R. "Efficient Dynamic Resource Allocation in Hadoop Multiclusters for Load- Balancing Problem." Recent Advances in Computer Science and Communications 13, no. 4 (October 19, 2020): 686–93. http://dx.doi.org/10.2174/2213275912666190430161947.

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Background: ‘Map-Reduce’ is the framework and its processing of data by rationalizing the distributed servers. also its running the various tasks in parallel way. The most important problem in map reduce environment is Resource Allocation in distributed environments and data locality to its corresponding slave nodes. If the applications are not scheduled properly then it leads to load unbalancing problems in the cloud environments. Objective: This Research suggests a new dynamic way of allocating the resources in hadoop multi cluster environment in order to effectively monitor the nodes for faster computation, load balancing and data locality issues. The dynamic slot allocation is explained theoretically in order to address the problems of pre configuration, speculative execution, delay in scheduling and pre slot allocation in hadoop environments. Experiment is done with Hadoop cluster which increases the efficiency of the nodes and solves the load balancing problem. Methods: The Current design of Map Reduce Hadoop systems are affected by underutilization of slots. The reason is the number of maps and reducer is allotted is smaller than the available number of maps and reducers. In Hadoop most of the times its noticed that dynamic slot allocation policy, the mapper or reducers are idle. So we can use those unused map tasks to overloaded reducer tasks in-order to increase the efficiency of MR jobs and vice versa. Results: The real time experiment was implemented with Multinode Hadoop cluster map reduce jobs of file size 1giga bytes to 5 giga bytes and various performance test has been taken. Conclusion: This paper focused on Hadoop map reduce resource allocation management techniques for multi cluster environments. It proposes a novel dynamic slot allocation policy to improve the performance of yarn scheduler and eliminates the load balancing problem. This work proves that dynamic slot allocation is outperforms more than yarn framework. In future it considered to concentrate on CPU bandwidth and processing time.

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Somasundaram, K., and S. Radhakrishnan. "Task Resource Allocation in Grid using Swift Scheduler." International Journal of Computers Communications & Control 4, no. 2 (June 1, 2009): 158. http://dx.doi.org/10.15837/ijccc.2009.2.2423.

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In nature, Grid computing is the combination of parallel and distributed computing where running computationally intensive applications like sequence alignment, weather forecasting, etc are needed a proficient scheduler to solve the problems awfully fast. Most of the Grid tasks are scheduled based on the First come first served (FCFS) or FCFS with advanced reservation, Shortest Job First (SJF) and etc. But these traditional algorithms seize more computational time due to soar waiting time of jobs in job queue. In Grid scheduling algorithm, the resources selection is NPcomplete. To triumph over the above problem, we proposed a new dynamic scheduling algorithm which is the combination of heuristic search algorithm and traditional SJF algorithm called swift scheduler. The proposed algorithm takes care of Job’s memory and CPU requirements along with the priority of jobs and resources. Our experimental results shows that our scheduler reduces the average waiting time in the job queue and reduces the over all computational time.

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Krishna, M. Bala. "A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment." International Academic Journal of Science and Engineering 8, no. 1 (March 15, 2021): 19–30. http://dx.doi.org/10.9756/iajse/v8i1/iajse0803.

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An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.

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Ashish, Maheta, and Samrat V. O. Khanna. "Resource Provisioning Based Scheduling Framework for Execution of Virtual Machine in Heterogeneous Environment in Cloud Computing." Advanced Engineering Forum 37 (September 2020): 59–68. http://dx.doi.org/10.4028/www.scientific.net/aef.37.59.

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Cloud computing is provides resource allocation which facilitates the cloud resource provider responsible to the cloud consumers. The main objective of resource manager is to assign the dynamic resource to the task in the execution and measures response time, execution cost, resource utilization and system performance. The resource manager is optimizing the resource and measure the completion time for assign resource. The resource manager is also measure to execute the resource in the optimal way to complete the task in minimum completion time. The virtualization is techniques mandatory to allocate the dynamic resource depends on the users need. There are also green computing techniques involved for enhanced the no of server. The skewness is basically used to enhance the quality of service using the various parameters. The proposed algorithms are considered to allocate the cloud resource as per the users requirement. The advantage of proposed algorithm is to view the analysis of cpu utilization and also reduced the memory usage.

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Hu, Rongdong, Jingfei Jiang, Guangming Liu, and Lixin Wang. "Efficient Resources Provisioning Based on Load Forecasting in Cloud." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/321231.

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Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements.

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Joshi, Aparna Shashikant, and Shayamala Devi Munisamy. "Enhancement of cloud performance metrics using dynamic degree memory balanced allocation algorithm." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (June 1, 2021): 1697. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1697-1707.

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In cloud computing, load balancing among the resources is required to schedule a task, which is a key challenge. This paper proposes a dynamic degree memory balanced allocation (D2MBA) algorithm which allocate virtual machine (VM) to a best suitable host, based on availability of random-access memory (RAM) and microprocessor without interlocked pipelined stages (MIPS) of host and allocate task to a best suitable VM by considering balanced condition of VM. The proposed D2MBA algorithm has been simulated using a simulation tool CloudSim by varying number of tasks and keeping number of VMs constant and vice versa. The D2MBA algorithm is compared with the other load balancing algorithms viz. Round Robin (RR) and dynamic degree balance with central processing unit (CPU) based (D2B_CPU based) with respect to performance parameters such as execution cost, degree of imbalance and makespan time. It is found that the D2MBA algorithm has a large reduction in the performance parameters such as execution cost, degree of imbalance and makespan time as compared with RR and D2B CPU based algorithms

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V. Sajitha, A., and A. C. Subhajini. "Dynamic VM Consolidation Enhancement for Designing and Evaluation of Energy Efficiency in Green Data Centers Using Regression Analysis." International Journal of Engineering & Technology 7, no. 3.6 (July 4, 2018): 179. http://dx.doi.org/10.14419/ijet.v7i3.6.14966.

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Enhancement of dynamic Virtual Machines (VM) consolidation is an efficient means to improve the energy efficiency via effective resources utilization in Cloud data centers. In this paper, we propose an algorithm, Energy Conscious Greeny Cloud Dynamic Algorithm, which considers multiple factors such as CPU, memory and bandwidth utilization of the node for empowering VM consolidation by using regression analysis model. This algorithm is the combination of several adaptive algorithms such as EnCoReAn (UPReAn) for Predicting the Utility of a host), Overload and Under-load detection), VM Selection and Allocation algorithms, which helps to achieve live VM migration by switching-off unused servers to low-power mode (i.e., sleep or hibernation), thus saves energy and efficient resource utilization. This approach reduces the operational cost, computation time and increase the scalability. The experimental result proves that, the proposed algorithm attains significant percentage in reduction of energy consumption rather than existing VM consolidation strategies.

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Dad, Djouhra, and Ghalem Belalem. "Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds." International Journal of Cloud Applications and Computing 10, no. 3 (July 2020): 81–95. http://dx.doi.org/10.4018/ijcac.2020070105.

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Cloud computing offers a variety of services, including the dynamic availability of computing resources. Its infrastructure is designed to support the accessibility and availability of various consumer services via the Internet. The number of data centers allow the allocation of the applications, and the process of data in the cloud is increasing over time. This implies high energy consumption, thus contributing to large emissions of CO2 gas. For this reason, solutions are needed to minimize this power consumption, such as virtualization, migration, consolidation, and efficient traffic-aware virtual machine scheduling. In this article, the authors propose two efficient strategies for VM scheduling. SchedCT approach is based on dynamic CPU utilization and temperature thresholds. SchedCR approach takes into consideration dynamic CPU utilization, RAM capacity, and temperature thresholds. These approaches have efficiently decreased the energy consumption of the data centers, the number of VM migrations, and SLA violations, and this reduces, therefore, the emission of CO2 gas.

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Verma, Manish, G. R. Gangadharan, Nanjangud C. Narendra, Ravi Vadlamani, Vidyadhar Inamdar, Lakshmi Ramachandran, Rodrigo N. Calheiros, and Rajkumar Buyya. "Dynamic resource demand prediction and allocation in multi-tenant service clouds." Concurrency and Computation: Practice and Experience 28, no. 17 (January 28, 2016): 4429–42. http://dx.doi.org/10.1002/cpe.3767.

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Dissertations / Theses on the topic "Dynamic CPU resource allocation"

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Vijayakumar, Smita. "A Framework for Providing Automatic Resource and Accuracy Management in a Cloud Environment." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1274194090.

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Eriksson, Kristoffer. "Dynamic Resource Allocation in Wireless Networks." Thesis, Linköping University, Communication Systems, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56776.

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In this thesis we investigate different algorithms for dynamic resource allocation in wireless networks. We introduce a general framework for modeling systems whichis applicable to many scenarios. We also analyze a specific scenario with adaptivebeamforming and show how it fits into the proposed framework. We then studytwo different resource allocation problems: Quality-of-Service (QoS) constraineduser scheduling and sum-rate maximization. For user scheduling, we select some“good” set of users that is allowed to use a specific resource. We investigatedifferent algorithms with varying complexities. For the sum-rate maximizationwe find the global optimum through an algorithm that takes advantage of thestructure of the problem by reformulating it as a D.C. program, i.e., a minimizationover a difference of convex functions. We validate this approach by showing that itis more efficient than an exhaustive search at exploring the space of solutions. Thealgorithm provides a good benchmark for more suboptimal algorithms to comparewith. The framework in which we construct the algorithm, apart from being verygeneral, is also very flexible and can be used to implement other low complexitybut suboptimal algorithms.

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Zhang, Peter Yun. "Dynamic and robust network resource allocation." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123565.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 139-150).
Networks are essential modeling tools in engineering, business, and public policy research. They can represent physical connections, such as manufacturing processes. They can be relationships among people, such as patient treatment in healthcare. They can also represent abstract interactions, such as the biological reaction between a certain vaccine and a certain virus. In this work, we bring several seemingly disparate problems under the same modeling framework, and show their thematic coherence via the angle of dynamic optimization on networks. Our research problems are drawn from business risk management, public health security, and public policy on vaccine selection. A common theme is the integrative design of (1) strategic resource placement on a network, and (2) operational deployment of such resources. We outline the research questions, challenges, and contributions as follows.
Modern automotive manufacturing networks are complex and global, comprising tens of thousands of parts and thousands of plants and suppliers. Such interconnection leaves the network vulnerable to disruptive events. A good risk mitigation decision support system should be data-driven, interpretable, and computational efficient. We devise such a tool via a linear optimization model, and integrate the model into the native information technology system at Ford Motor Company. In public security, policymakers face decisions regarding the placement of medical resources and training of healthcare personnel, to minimize the social and economic impact of potential large scale bio-terrorism attacks. Such decisions have to integrate the strategic positioning of medical inventories, understanding of adversary's behavior, and operational decisions that involve the deployment and dispensing of medicines.
We formulate a dynamic robust optimization model that addresses this decision question, apply a tractable solution heuristic, and prove theoretical guarantees of the heuristic's performance. Our model is calibrated with publicly available data to generate insights on how the policymakers should balance investment between medical inventory and personnel training. The World Health Organization and regional public health authorities decide on the influenza (flu) vaccine type ahead of flu season every year. Vaccine effectiveness has been limited by the long lead time of vaccine production - during the production period, flu viruses may evolve and vaccines may become less effective. New vaccine technologies, with much shorter production lead times, have gone through clinical trials in recent years. We analyze the question of optimal vaccine selection under both fast and slow production technologies. We formulate the problem as a dynamic distributionally robust optimization model.
Exploiting the network structure and using tools from discrete convex analysis, we prove some structural properties, which leads to informative comparative statics and tractable solution methods. With publicly available data, we quantify the societal benefit of current and future vaccine production technologies. We also explore the reduction in disease burden if WHO expand vaccine portfolio to include more than one vaccine strain per virus subtype. In each of the applications, our main contributions are four-fold. First, we develop mathematical models that capture the decision process. Second, we provide computational technology that can efficiently process these models and generate solutions. Third, we develop theoretical tools that guarantee the performance of these computational technology. Last, we calibrate our models with real data to generate quantitative and implementable insights.
by Peter Yun Zhang.
Ph. D. in Engineering Systems
Ph.D.inEngineeringSystems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

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Hashmi, Ziaul Hasan. "Dynamic resource allocation for cognitive radio systems." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/961.

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Cognitive Radio (CR) is considered to be a novel approach to improve the underutilization of precious radio resources by exploiting the unused licensed spectrum in dynamically changing environments. Designing efficient resource allocation algorithms for dynamic spectrum sharing and for power allocation in OFDM-CR networks is still a challenging problem. In this thesis, we specifically deal with these two problems. Dynamic spectrum sharing for the unlicensed secondary users (SU)s with device coordination could minimize the wastage of the spectrum. But this is a feasible approach only if the network considers the fairness criterion. We study the dynamic spectrum sharing problem for device coordinated cognitive radio networks with respect to fairness. We propose a simple modified proportional fair algorithm for a dynamic spectrum sharing scenario with two constraints, time and utility. Utility is measured by the amount of data processed and time is measured as the duration of a slot. This algorithm could result in variable or fixed length time slots. We will discuss the several controls possible on the algorithm and the possible extension of this algorithm for multicarrier OFDM based CR systems. Traditional water-filling algorithm is inefficient for OFDM-CR networks due to the interaction with primary users (PU)s. We consider reliability/availability of subcarriers or primary user activity for power allocation. We model this aspect mathematically with a risk-return model by defining a general rate loss function. We then propose optimal and suboptimal algorithms to allocate power under a fixed power budget for such a system with linear rate loss. These algorithms as we will see allocate more power to more reliable subcarriers in a water-filling fashion with different water levels. We compare the performance of these algorithms for our model with respect to water-filling solutions. Simulations show that suboptimal schemes perform closer to optimal scheme although they could be implemented with same complexity as water-filling algorithm. We discuss the linearity of loss function and guidelines to choose its coefficients by obtaining upper bounds on them. Finally we extend this model for interference-limited OFDM-CR systems.

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Su, Guan-Ming. "Dynamic resource allocation for multiuser video streaming." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3982.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Usaha, Wipawee. "Resource allocation in networks with dynamic topology." Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405658.

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Tuli, Gaurav 1978. "Dynamic QoS resource allocation in Bluetooth piconet." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86754.

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Ferreira, Pena Do Amaral J. A. "Aspects of optimal sequential resource allocation." Thesis, University of Oxford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370266.

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Västfält, Anders, and Matthias Erll. "A Dynamic Resource Allocation Framework for IT Consultancies." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-15710.

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This Master thesis provides a framework for analysis of the resource planning and allocation processes within an IT consultant firm. The aim is, to identify information, which can be reflected in an information system. The framework has been developed using multi-grounded theory method, considering theories from the areas of information systems design, project business performance, enterprise planning, and project planning. Based on a main process view and hypothesized information requirements, the dynamic processes of sales, project resource planning, miscellaneous activity planning, project portfolio planning, resource allocation and general management are discussed, along with their underlying concepts. A case study has been conducted, to test the validity of the framework and to evaluate its applicability. The findings are compared and contrasted to our frame of reference during analysis. From a reflection on the analysis, changes are proposed to the firm under study, as well as our framework.

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Ivan-Roşu, Daniela. "Dynamic resource allocation for adaptive real-time applications." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/9200.

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Books on the topic "Dynamic CPU resource allocation"

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Benmammar, Badr, and Asma Amraoui. Radio Resource Allocation and Dynamic Spectrum Access. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118575116.

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G, Khachii͡an L., ed. Skolʹzi͡ashchee raspredelenie resursa na seti metodom dinamicheskogo programmirovanii͡a. Moskva: Vychislitelʹnyĭ t͡sentr AN SSSR, 1986.

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K, Kokula Krishna Hari, ed. Implementing Virtual Machines for Dynamic Resource Allocation in Cloud Computing Environment: ICIEMS 2014. India: Association of Scientists, Developers and Faculties, 2014.

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Dynamic economic theory: A viability approach. Berlin: Springer-Verlag, 1997.

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Cognitive Radio Networks: Dynamic Resource Allocation Schemes. Springer, 2014.

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Benmammar, Badr, and Asma Amraoui. Radio Resource Allocation and Dynamic Spectrum Access. Wiley & Sons, Incorporated, John, 2013.

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Radio Resource Allocation And Dynamic Spectrum Access. ISTE Ltd and John Wiley & Sons Inc, 2013.

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Benmammar, Badr, and Asma Amraoui. Radio Resource Allocation and Dynamic Spectrum Access. Wiley & Sons, Incorporated, John, 2013.

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Schwind, Michael. Dynamic Pricing and Automated Resource Allocation for Complex Information Services. Springer, 2008.

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Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing. River Publishers, 2016.

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Book chapters on the topic "Dynamic CPU resource allocation"

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Modi, Pragnesh Jay, Hyuckchul Jung, Milind Tambe, Wei-Min Shen, and Shriniwas Kulkarni. "A Dynamic Distributed Constraint Satisfaction Approach to Resource Allocation." In Principles and Practice of Constraint Programming — CP 2001, 685–700. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45578-7_56.

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Weik, Martin H. "dynamic resource allocation." In Computer Science and Communications Dictionary, 473. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_5747.

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Wang, Shaowei. "Dynamic Resource Allocation." In Cognitive Radio Networks, 9–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08936-2_2.

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Avni, Guy, Thomas A. Henzinger, and Orna Kupferman. "Dynamic Resource Allocation Games." In Algorithmic Game Theory, 153–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-53354-3_13.

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Nascimento, Alberto, and Jonathan Rodriguez. "Dynamic Resource Allocation for IEEE802.16e." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 147–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03819-8_15.

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Capone, Antonio, Jocelyne Elias, Fabio Martignon, and Guy Pujolle. "Dynamic Resource Allocation in Communication Networks." In NETWORKING 2006. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems, 892–903. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11753810_74.

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Johansson, Stefan, Paul Davidsson, and Bengt Carlsson. "Coordination Models for Dynamic Resource Allocation." In Coordination Languages and Models, 182–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45263-x_12.

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Benmammar, Badr, and Asma Amraoui. "Dynamic Spectrum Access." In Radio Resource Allocation and Dynamic Spectrum Access, 53–66. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118575116.ch4.

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G.M., Siddesh, and Srinivasa K.G. "SLA - Driven Dynamic Resource Allocation on Clouds." In Lecture Notes in Computer Science, 9–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29280-4_2.

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Benesty, Jacob, Tomas Gänsler, Dennis R. Morgan, M. Mohan Sondhi, and Steven L. Gay. "Dynamic Resource Allocation for Network Echo Cancellation." In Digital Signal Processing, 65–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04437-7_4.

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Conference papers on the topic "Dynamic CPU resource allocation"

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Monsalve, Jose, Aaron Landwehr, and Michela Taufer. "Dynamic CPU Resource Allocation in Containerized Cloud Environments." In 2015 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2015. http://dx.doi.org/10.1109/cluster.2015.99.

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Hyunku Jeong and Sung-Min Lee. "Dynamic CPU resource allocation for multicore CE devices running multiple operating systems." In 2012 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2012. http://dx.doi.org/10.1109/icce.2012.6161997.

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Yuxin Xu, E. Agrell, and M. Brandt-Pearce. "Static Resource Allocation for Dynamic Traffic." In 45th European Conference on Optical Communication (ECOC 2019). Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/cp.2019.1115.

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Ismail, Haydar Ali, and Mardhani Riasetiawan. "CPU and memory performance analysis on dynamic and dedicated resource allocation using XenServer in Data Center environment." In 2016 2nd International Conference on Science and Technology-Computer (ICST). IEEE, 2016. http://dx.doi.org/10.1109/icstc.2016.7877341.

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Chao, H. "A dynamic resource allocation method for HSDPA in WCDMA system." In Fifth IEE International Conference on 3G Mobile Communication Technologies (3G 2004) The Premier Technical Conference for 3G and Beyond. IEE, 2004. http://dx.doi.org/10.1049/cp:20040739.

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Gaojun Song and Hongwu Liu. "Dynamic resource allocation in relay-aided celluar OFDMA systems [celluar read cellular]." In Symposium on ICT and Energy Efficiency and Workshop on Information Theory and Security (CIICT 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.1861.

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Wujie Hu, Haiming Wang, and Tao Chen. "A novel dynamic resource allocation algorithm for future wideband mobile communication systems." In IEE Mobility Conference 2005. The Second International Conference on Mobile Technology, Applications and Systems. IEE, 2005. http://dx.doi.org/10.1049/cp:20051449.

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Fujii, S., H. Tode, and Y. Hirota. "Dynamic Resource Allocation with Virtual Grid for Space Division Multiplexed Elastic Optical Network." In 39th European Conference and Exhibition on Optical Communication (ECOC 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.1653.

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Al-Fawwaz, Abdullah A., Yousif M. Al-Dhafiri, Muhammad N. Akhtar, Samad Ali, Muhammad Ibrahim, Marie Ann Giddins, and Aimen Amer. "First Time Utilization of Cloud-Based Technology to Fast Track A 521 Million Cell Gas Condensate Reservoir Dynamic Model: A Case Study from Saudi Arabia." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31194-ms.

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Abstract:

Abstract The main objective of this study is to run a high-resolution dynamic simulation on a 521-million cell gas condensate field model for 50 years and capture the effects of gas condensate dropout. Two challenges were encountered on the user and service provider levels. The former is performing such work in a remote location with limited processing hardware resources. The latter is related to resolving memory, CPU allocation, technical support, system resources availability, integration between providers, and simulation needs on user demand. The approach adopted in this field development planning study was to utilize the latest cloud technology to run the 521 million cell simulation on cloud clusters as well as two upscaled versions (5 and 21 million cells). Such procedures can save significant processing time and money. As opposed to direct purchase and installation of clusters that require maintenance, updates, and become outdated over time, with a cloud cluster that is kept updated and maintained by service providers, significant cost overheads (in millions) could be saved. Using such technology allows operators to get global technical support making executing such simulations viable even in the most remote locations. The field under study is a gas condensate field that on its own can present multiple challenges including the gas condensate banking impact and compositional modeling. The main strategy adopted in this study was to utilize the static model with no upscaling, to capture the geological details. With the utilization of cloud technology, all simulations were completed in record time. The 5 million cell model was executed in 23 min, while the 21 million cell model executed in 4 hours, and the 521 million executed in 65 hours. The results of the simulations showed that the gas condensate banking effect was captured clearly after the implementation of local grid refinement (LGR) on the upscaled models. A good match was observed in the production profiles for all key parameters, such as gas rates, oil and condensate rates and their cumulative productions. Using cloud technology saved the operating company over 5 million dollars in cluster hardware direct purchase, support and maintenance costs, making the utilization of the cloud computing technology not only economical, but also bringing about operational efficiencies. This is the first time a cloud-based dynamic simulation is performed on a 521 million cell model in the world and the first time, an on-demand reservoir simulation based on cloud computing technology has been conducted in the Middle East region. This paper will also show that, given the right model parameters, carefully built smaller models can yield results similar to larger models, highlighting the importance of efficiency.

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Nascimento, A., J. Rodriguez, A. Gameiro, and C. Politis. "Dynamic Resource Allocation for IEEE802.16e." In 3rd International ICST Conference on Mobile Multimedia Communications. ICST, 2007. http://dx.doi.org/10.4108/icst.mobimedia2007.1992.

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Reports on the topic "Dynamic CPU resource allocation"

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Hansen, Jeff, Scott Hissam, B. C. Meyers, Gabriel Moreno, Daniel M. Plakosh, Joseph Seibel, and Lutz Wrage. Resource Allocation in Dynamic Environments. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada609913.

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Kozlowski, Steve W., and Richard P. DeShon. Optimizing Dynamic Resource Allocation in Teamwork. Fort Belvoir, VA: Defense Technical Information Center, February 2008. http://dx.doi.org/10.21236/ada478848.

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Kozlowski, Steve W., Richard P. DeShon, Guihyun Park, Paul Curran, Goran Kuljanin, and Brady Firth. Dynamic Resource Allocation and Adaptability in Teamwork. Fort Belvoir, VA: Defense Technical Information Center, August 2007. http://dx.doi.org/10.21236/ada475399.

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Castanon, David A., and Jerry M. Wohletz. Model Predictive Control for Dynamic Unreliable Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada409519.

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Luo, Zhi-Quan. Optimization Algorithms and Equilibrium Analysis for Dynamic Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada565198.

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Pang, Jong-Shi. Optimization Algorithms and Equilibrium Analysis for Dynamic Resource Allocation. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada577088.

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Gue, Kevin R. Dynamic Resource Allocation to Improve Service Performance in Order Fulfillment Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada505183.

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Gue, Kevin R. Dynamic Resource Allocation to Improve Service Performance in Order Fulfillment Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada553764.

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Gentile, Ann C., James M. Brandt, Thomas Tucker, and David Thompson. Develop feedback system for intelligent dynamic resource allocation to improve application performance. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1029818.

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Liu, Youming, Shanjun Li, and Caixia Shen. The Dynamic Efficiency in Resource Allocation: Evidence from Vehicle License Lotteries in Beijing. Cambridge, MA: National Bureau of Economic Research, March 2020. http://dx.doi.org/10.3386/w26904.

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