Log in

Relevant bibliographies by topics / Dynamic CPU resource allocation / Journal articles

To see the other types of publications on this topic, follow the link: Dynamic CPU resource allocation.

Journal articles on the topic 'Dynamic CPU resource allocation'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Dynamic CPU resource allocation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Gangadhar, Pvss, Ashok Kumar Hota, Mandapati Venkateswara Rao, and Vedula Venkateswara Rao. "Performance of Memory Virtualization Using Global Memory Resource Balancing." International Journal of Cloud Applications and Computing 9, no. 1 (January 2019): 16–32. http://dx.doi.org/10.4018/ijcac.2019010102.

Full text

Abstract:

Virtualization has become a universal generalization layer in contemporary data centers. By multiplexing hardware resources into multiple virtual machines and facilitating several operating systems to run on the same physical platform at the same time, it can effectively decrease power consumption and building size or improve security by isolating virtual machines. In a virtualized system, memory resource supervision acts as a decisive task in achieving high resource employment and performance. Insufficient memory allocation to a virtual machine will degrade its performance drastically. On the contrasting, over allocation reasons ravage of memory resources. In the meantime, a virtual machine's memory stipulates may differ drastically. As a consequence, effective memory resource management calls for a dynamic memory balancer, which, preferably, can alter memory allocation in a timely mode for each virtual machine-based on their present memory stipulate and therefore realize the preeminent memory utilization and the best possible overall performance. Migrating operating system instances across discrete physical hosts is a helpful tool for administrators of data centers and clusters: It permits a clean separation among hardware and software, and make easy fault management. In order to approximate the memory, the stipulate of each virtual machine and to adjudicate probable memory resource disagreement, an extensively planned approach is to build an Least Recently Used based miss ratio curve which provides not only the current working set size but also the correlation between performance and the target memory allocation size. In this paper, the authors initially present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL based Least Recently Used association, dynamic hot set sizing. This assessment outcome confirms that, for the complete SPEC CPU 2006 benchmark set, subsequent to pertaining the 3 optimizing techniques, the mean overhead of MRC construction are lowered from 173% to only 2%. Based on current WSS, the authors then predict its trend in the near future and take different tactics for different forecast results. When there is an adequate amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. These experimental results show that this design achieves 49% center-wide speedup.

APA, Harvard, Vancouver, ISO, and other styles

12

Zeng, Chenxi. "Optimal resource allocation in mobile satellite networks: A noncooperative dynamic game–based approach." Concurrency and Computation: Practice and Experience 31, no. 10 (October 3, 2018): e4857. http://dx.doi.org/10.1002/cpe.4857.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Li, Ji Chu, Hui Xia Jin, and Jun Tang. "Architecture Design of Distributed Computing System Based on SOA Model." Applied Mechanics and Materials 29-32 (August 2010): 2509–15. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2509.

Full text

Abstract:

With the sharp increase of data amount in modern computing process, it needs more powerful general computing ability to process and compute data. Web Services--based Distributed Universal Computing Platform (WSDCP) provides an inexpensive solution for large-scale computing tasks in a loosely coupled manner with high flexibility and node autonomy. System adopts Web Services, XML as technical support, through installation of intelligent probe on nodes to real-time collect the situation of node, and the disequilibrium task allocation and dynamical task regulation can improve system reliability. WSDCP system takes green computing concept as guiding concept, and takes advantage of idle CPU resources on the Internet, which have positive significant for establishing resource-conserving society.

APA, Harvard, Vancouver, ISO, and other styles

14

Mahadevan, Ravi, and Neelamegam Anbazhagan. "An Efficient Framework to Improve QoS of CSP using Enhanced Minimal Resource Optimization based Scheduling Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (December 1, 2018): 1179. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp1179-1186.

Full text

Abstract:

<span>Online Nowadays, the enterprises & individuals are contributing their workloads on cloud service providers which are going to increase on daily basis. There are large amount CSP are available to offer virtualized and dynamic resource on pay and use basis. However, there are almost CSP failed to maintain quality of service (QOS) and minimal resource optimization. Some of the existing approaches are highly dedicated on scheduling policy but, it does not considered reliable services with optimized QOS. To offer best solution of above problem, the framework proposes Enhanced Minimal Resource Optimization based Scheduling Algorithm to minimize the resources and maintain the QOS. The method avoids delay in Request-Response model in cloud environment. To avoid overload for resource allocation, the proposed design utilized optimized scheduling policy. Proposed mechanisms utilized optimized service brokering policy to reduce the delay response in cloud environment. The framework also help cloud user to prefer best CSP according to their prior services. The method offers rising trend of resource based structure to reduce the placement churn extensively. Proposed system utilized efficient scheduling policy to transmit data request to CSP with minimal data processing time. The entire utilization is to improve the QOS of cloud service provider in the features of multi-dimensional resource. Based on experimental evaluations, proposed technique improves the CPT (Computation Processing Time) 301.72 milliseconds, BU (Bandwidth Utilization) 20 Mbps, CPUU (CPU Utilization) 5% & MRU (Memory Resource Utilization) 3% on given input parameters compare than existing methodology.</span>

APA, Harvard, Vancouver, ISO, and other styles

15

LEE, MALREY, and KEUN-KWANG LEE. "A DYNAMIC LOAD BALANCING MODEL FOR CONCURRENTLY CONNECTED USERS IN U-HEALTHCARE MONITORING SYSTEMS." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 08 (December 2010): 1329–46. http://dx.doi.org/10.1142/s0218001410008378.

Full text

Abstract:

U-healthcare systems are based on a ubiquitous and wireless computing and communication environment. They are comprised of the U-healthcare management center, electronic medical records (EMR) system, and the associated services for users and patients. The U-healthcare management center performs continuous monitoring and provides support services in multiple areas, requiring careful allocation of the limited service resources to provide customized healthcare services for users of the mobile distributed system. When the number of locally connected users increases rapidly, a mobile allocation and distribution server can be imbalanced by the load on service resources, resulting in delayed services. This study proposes a dynamic load balancing model for reducing the load of users on service resources and supporting efficient response services in a mobile distributed system. The proposed dynamic load balancing model clusters the system resources of servers dynamically, according to each users' movement and time. The dynamic clustering of system resources uses wFCM (weighted Fuzzy C-Means), which changes the cluster center by transforming existing FCM (Fuzzy C-Means) from a fixed weight to a dynamic one. Using wFCM, the load balance can be maintained, based on the usage rate of service resources, such as CPU, memory, and network. In addition, the balance between QoS (Quality of Service) requests and network response times can be maintained by adding an abstraction layer between application services and network infrastructure. Therefore, when the proposed model is applied to a U-healthcare monitoring system, the system can perform near real-time monitoring of service users in the mobile distributed environment, and effectively address emergent situations. This study evaluates the response time of the implemented model in relation to the number of concurrently connected users, and confirms that the proposed model is faster in response and service processing than existing WLC (Weighted Least-Connection Scheduling) and FCM (Fuzzy C-Means).

APA, Harvard, Vancouver, ISO, and other styles

16

KUMAR, VIJAY, SUNIL KUMAR KHATRI, HITESH DUA, MANISHA SHARMA, and PARIDHI MATHUR. "AN ASSESSMENT OF TESTING COST WITH EFFORT-DEPENDENT FDP AND FCP UNDER LEARNING EFFECT: A GENETIC ALGORITHM APPROACH." International Journal of Reliability, Quality and Safety Engineering 21, no. 06 (December 2014): 1450027. http://dx.doi.org/10.1142/s0218539314500272.

Full text

Abstract:

Software testing involves verification and validation of the software to meet the requirements elucidated by customers in the earlier phases and to subsequently increase software reliability. Around half of the resources, such as manpower and CPU time are consumed and a major portion of the total cost of developing the software is incurred in testing phase, making it the most crucial and time-consuming phase of a software development lifecycle (SDLC). Also the fault detection process (FDP) and fault correction process (FCP) are the important processes in SDLC. A number of software reliability growth models (SRGM) have been proposed in the last four decades to capture the time lag between detected and corrected faults. But most of the models are discussed under static environment. The purpose of this paper is to allocate the resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. An elaborate optimization policy based on optimal control theory for resource allocation with the objective to minimize the cost is proposed. Further, genetic algorithm is applied to obtain the optimum value of detection and correction efforts which minimizes the cost. Numerical example is given in support of the above theoretical result. The experimental results help the project manager to identify the contribution of model parameters and their weight.

APA, Harvard, Vancouver, ISO, and other styles

17

Chetty, Swarna Bindu, Hamed Ahmadi, Sachin Sharma, and Avishek Nag. "Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning." Future Internet 13, no. 3 (March 23, 2021): 82. http://dx.doi.org/10.3390/fi13030082.

Full text

Abstract:

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by virtualizing network functions and placing them on shared commodity servers. However, one of the critical issues in NFV is the resource allocation for the highly complex services; moreover, this problem is classified as an NP-Hard problem. To solve this problem, our work investigates the potential of Deep Reinforcement Learning (DRL) as a swift yet accurate approach (as compared to integer linear programming) for deploying Virtualized Network Functions (VNFs) under several Quality-of-Service (QoS) constraints such as latency, memory, CPU, and failure recovery requirements. More importantly, the failure recovery requirements are focused on the node-outage problem where outage can be either due to a disaster or unavailability of network topology information (e.g., due to proprietary and ownership issues). In DRL, we adopt a Deep Q-Learning (DQL) based algorithm where the primary network estimates the action-value function Q, as well as the predicted Q, highly causing divergence in Q-value’s updates. This divergence increases for the larger-scale action and state-space causing inconsistency in learning, resulting in an inaccurate output. Thus, to overcome this divergence, our work has adopted a well-known approach, i.e., introducing Target Neural Networks and Experience Replay algorithms in DQL. The constructed model is simulated for two real network topologies—Netrail Topology and BtEurope Topology—with various capacities of the nodes (e.g., CPU core, VNFs per Core), links (e.g., bandwidth and latency), several VNF Forwarding Graph (VNF-FG) complexities, and different degrees of the nodal outage from 0% to 50%. We can conclude from our work that, with the increase in network density or nodal capacity or VNF-FG’s complexity, the model took extremely high computation time to execute the desirable results. Moreover, with the rise in complexity of the VNF-FG, the resources decline much faster. In terms of the nodal outage, our model provided almost 70–90% Service Acceptance Rate (SAR) even with a 50% nodal outage for certain combinations of scenarios.

APA, Harvard, Vancouver, ISO, and other styles

18

Ogbuachi, Michael Chima, Anna Reale, Péter Suskovics, and Benedek Kovács. "Context-Aware Kubernetes Scheduler for Edge-native Applications on 5G." Journal of communications software and systems 16, no. 1 (March 30, 2020): 85–94. http://dx.doi.org/10.24138/jcomss.v16i1.1027.

Full text

Abstract:

This paper is an extension of work originally presented in SoftCOM 2019 [1]. The novelty of this work reside in its focused improvement of our scheduling algorithm towards its usage on a real 5G infrastructure. Industrial IoT applications are often designed to run in a distributed way on the devices and controller computers with strict service requirements for the nodes and the links between them. 5G, especially in concomitance with Edge Computing, will provide the desired level of connectivity for these setups and it will permit to host application run-time components in edge clouds. However, allocation of the edge cloud resources for Industrial IoT (IIoT) applications, is still commonly solved by rudimentary scheduling techniques (i.e. simple strategies based on CPU usage and device readiness, employing very few dynamic information). Orchestrators inherited from the cloud computing, like Kubernetes, are not satisfying to the requirements of the aforementioned applications and are not optimized for the diversity of devices which are often also limited in capacity. This design is especially slow in reacting to the environmental changes. In such circumstances, in order to provide a proper solution using these tools, we propose to take the physical, operational and network parameters (thus the full context of the IIoT application) into consideration, along with the software states and orchestrate the applications dynamically.

APA, Harvard, Vancouver, ISO, and other styles

19

Avni, Guy, Thomas A. Henzinger, and Orna Kupferman. "Dynamic resource allocation games." Theoretical Computer Science 807 (February 2020): 42–55. http://dx.doi.org/10.1016/j.tcs.2019.06.031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Krakow, Lucas W., Louis Rabiet, Yun Zou, Guillaume Iooss, Edwin K. P. Chong, and Sanjay Rajopadhye. "Optimizing Dynamic Resource Allocation." Procedia Computer Science 29 (2014): 1277–88. http://dx.doi.org/10.1016/j.procs.2014.05.115.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Gul, Beenish, Imran Ali Khan, Saad Mustafa, Osman Khalid, and Atta ur Rehman Khan. "CPU–RAM-based energy-efficient resource allocation in clouds." Journal of Supercomputing 75, no. 11 (August 19, 2019): 7606–24. http://dx.doi.org/10.1007/s11227-019-02969-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Jordan, Michael Guilherme, Guilherme Korol, Mateus Beck Rutzig, and Antonio Carlos Schneider Beck. "Resource-Aware Collaborative Allocation for CPU-FPGA Cloud Environments." IEEE Transactions on Circuits and Systems II: Express Briefs 68, no. 5 (May 2021): 1655–59. http://dx.doi.org/10.1109/tcsii.2021.3066309.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Tang, Xiaochun, Ying Fu, and Xuefeng Fan. "Fine-Grained Allocation Algorithm for Sharing Heterogeneous Resources in Data Center." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 3 (June 2020): 589–95. http://dx.doi.org/10.1051/jnwpu/20203830589.

Full text

Abstract:

Data in a data center are stored dispersively. The data-oriented task computing disperses big data analysis tasks to different computing nodes. The extensive use of graphics processing unit (GPU) makes it urgent and important to study how to reasonably assign heterogeneous resources to different computing frameworks. We investigate the existing big data computing framework and the GPU computing. Based on the existing cluster resource management model and the GPU management model, we propose a hybrid heterogeneous resource management model that combines CPU resources with GPU resources. The computing nodes manage local resources and implement tasks; the resource management center concertedly manage various computing frameworks. We design and implement a hybrid domain resource sharing and allocation algorithm, which allocates the hybrid domain resources to computing frameworks according to the coordinated use of them so as to fairly share the hybrid domain resources among various computing frameworks and prevent the CPU from too many tasks but the GPU or CPU from resource "hunger". The experimental results show that the allocation algorithm can increase the use of heterogeneous resources and the number of completed tasks by around 15%.

APA, Harvard, Vancouver, ISO, and other styles

24

Zareena, M. S. Mumtaj, M. Mahil, and N. Rupavathy. "Dynamic Resource Allocation for Green Clouds." i-manager's Journal on Cloud Computing 1, no. 3 (July 15, 2014): 8–15. http://dx.doi.org/10.26634/jcc.1.3.3155.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Farias, Vivek F., and Benjamin Van Roy. "Approximation algorithms for dynamic resource allocation." Operations Research Letters 34, no. 2 (March 2006): 180–90. http://dx.doi.org/10.1016/j.orl.2005.02.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Asker, John, Allan Collard-Wexler, and Jan De Loecker. "Dynamic Inputs and Resource (Mis)Allocation." Journal of Political Economy 122, no. 5 (October 2014): 1013–63. http://dx.doi.org/10.1086/677072.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Gunduz, Deniz, and Elza Erkip. "Opportunistic cooperation by dynamic resource allocation." IEEE Transactions on Wireless Communications 6, no. 4 (April 2007): 1446–54. http://dx.doi.org/10.1109/twc.2007.348341.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Filipčič, A., D. Cameron, and J. K. Nilsen. "Dynamic Resource Allocation with the arcControlTower." Journal of Physics: Conference Series 664, no. 6 (December 23, 2015): 062015. http://dx.doi.org/10.1088/1742-6596/664/6/062015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Saito, H. "Dynamic resource allocation in ATM networks." IEEE Communications Magazine 35, no. 5 (May 1997): 146–53. http://dx.doi.org/10.1109/35.592109.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Stephens, Kenneth R., William R. Hutchison, Sharon S. Hormby, and Thomas M. Bell. "Dynamic resource allocation using adaptive networks." Neurocomputing 2, no. 1 (June 1990): 9–16. http://dx.doi.org/10.1016/0925-2312(90)90012-g.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Priyanka H. and Mary Cherian. "Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment." International Journal of Cloud Applications and Computing 11, no. 3 (July 2021): 72–91. http://dx.doi.org/10.4018/ijcac.2021070105.

Full text

Abstract:

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

APA, Harvard, Vancouver, ISO, and other styles

32

GAJJAR, HASMUKH K., and GAJENDRA K. ADIL. "A DYNAMIC PROGRAMMING HEURISTIC FOR RETAIL SHELF SPACE ALLOCATION PROBLEM." Asia-Pacific Journal of Operational Research 28, no. 02 (April 2011): 183–99. http://dx.doi.org/10.1142/s0217595911003120.

Full text

Abstract:

Shelf space allocation to products greatly impacts the profitability in a retail store. In this paper, we consider a retail shelf-space allocation problem where retailer wishes to allocate the available spaces of different shelves to a large number of products considering direct space elasticity in the product's demand. There is a great interest to develop efficient heuristics due to NP-hard nature of this problem. We propose a dynamic programming heuristic (DPH) to obtain near optimal solution in a reasonable time to solve this problem. The empirical results found that DPH obtained near optimal solutions for randomly generated instances of problems with size (products, shelves) varying from (100, 30) to (200, 50) within a few seconds of CPU time. The performance of DPH is benchmarked against an existing local search heuristic (LSH). It was found that DPH takes substantially less CPU time and attains a solution close to that obtained by LSH. Thus, DPH has great potential to solve the problem of realistic size within reasonable time. The proposed DPH is also applied to a case of an existing retail store.

APA, Harvard, Vancouver, ISO, and other styles

33

Abdullah, Muhammad, Waheed Iqbal, Faisal Bukhari, and Abdelkarim Erradi. "Diminishing Returns and Deep Learning for Adaptive CPU Resource Allocation of Containers." IEEE Transactions on Network and Service Management 17, no. 4 (December 2020): 2052–63. http://dx.doi.org/10.1109/tnsm.2020.3033025.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Zou, Fangyuan, Jing Li, and Weidong Min. "Distributed Face Recognition Based on Load Balancing and Dynamic Prediction." Applied Sciences 9, no. 4 (February 24, 2019): 794. http://dx.doi.org/10.3390/app9040794.

Full text

Abstract:

With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.

APA, Harvard, Vancouver, ISO, and other styles

35

Hameurlain, Abdelkader, and Franck Morvan. "CPU and incremental memory allocation in dynamic parallelization of SQL queries." Parallel Computing 28, no. 4 (April 2002): 525–56. http://dx.doi.org/10.1016/s0167-8191(02)00074-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Works, Karen, and Elke A. Rundensteiner. "Preferential Resource Allocation in Stream Processing Systems." International Journal of Cooperative Information Systems 23, no. 04 (December 2014): 1450006. http://dx.doi.org/10.1142/s0218843014500063.

Full text

Abstract:

Overloaded data stream management systems (DSMS) cannot process all tuples within their response time. For some DSMS it is crucial to allocate the precious resources to process the most significant tuples. Prior work has applied shedding and spilling to permanently drop or temporarily place to disk insignificant tuples. However neither approach considers that tuple significance can be multi-tiered nor that significance determination can be costly. These approaches consider all tuples not dropped to be equally significant. Unlike these prior works, we take a fresh stance by pulling the most significant tuples forward throughout the query pipeline. Proactive Promotion (PP), a new DSMS methodology for preferential CPU resource allocation, selectively pulls the most significant tuples ahead of less significant tuples. Our optimizer produces an optimal PP plan that minimizes the processing latency of tuples in the most significant tiers in this multi-tiered precedence scheme by strategically placing significance determination operators throughout the query pipeline at compile-time and by agilely activating them at run-time. Our results substantiate that PP lowers the latency and increases the throughput for significant results when compared to the state-of-the-art shedding and traditional DSMS approaches (between 2 and 18 fold for a rich diversity of datasets) with negligible overhead.

APA, Harvard, Vancouver, ISO, and other styles

37

Ahn, TaeHyoung, Yena Kim, and SuKyoung Lee. "Dynamic Resource Allocation in Distributed Cloud Computing." Journal of Korea Information and Communications Society 38B, no. 7 (July 31, 2013): 512–18. http://dx.doi.org/10.7840/kics.2013.38b.7.512.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Saraswathi, A. T., Y. R. A. Kalaashri, and S. Padmavathi. "Dynamic Resource Allocation Scheme in Cloud Computing." Procedia Computer Science 47 (2015): 30–36. http://dx.doi.org/10.1016/j.procs.2015.03.180.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Leung, Yiu-Wing. "Dynamic resource-allocation for software-module testing." Journal of Systems and Software 37, no. 2 (May 1997): 129–39. http://dx.doi.org/10.1016/s0164-1212(96)00109-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Xiaoying, Tan, Huang Dan, Guo Yuchun, and Chen Changjia. "Dynamic resource allocation in cloud download service." Journal of China Universities of Posts and Telecommunications 24, no. 5 (October 2017): 53–59. http://dx.doi.org/10.1016/s1005-8885(17)60233-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Ayesta, U., M. Erausquin, E. Ferreira, and P. Jacko. "Optimal dynamic resource allocation to prevent defaults." Operations Research Letters 44, no. 4 (July 2016): 451–56. http://dx.doi.org/10.1016/j.orl.2016.04.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Zhang, Rui, Ying-chang Liang, and Shuguang Cui. "Dynamic Resource Allocation in Cognitive Radio Networks." IEEE Signal Processing Magazine 27, no. 3 (May 2010): 102–14. http://dx.doi.org/10.1109/msp.2010.936022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Sastry, Anitha S., and Akhila S. "Resource Allocation using Dynamic Fractional Frequency Reuse." International Journal of Wireless Networks and Broadband Technologies 6, no. 1 (January 2017): 34–44. http://dx.doi.org/10.4018/ijwnbt.2017010103.

Full text

Abstract:

This article describes how a multi user cellular system insists on having increase in the spectral efficiency for the number of users and base stations. As far as cellular structures are concerned, the users at the edges experience inter cellular interference (ICI) than the users at the cell center. This is due to lack of resource allocation at cell edges. To improve the throughput at the edges a technique called Fractional Frequency Reuse (FFR) is employed. This article explores the Dynamic FFR(DFFR) in OFDMA system to improve the overall throughput.

APA, Harvard, Vancouver, ISO, and other styles

44

Petraki, Dionysia K., Markos P. Anastasopoulos, and Panayotis G. Cottis. "Dynamic resource allocation for DVB-RCS networks." International Journal of Satellite Communications and Networking 26, no. 3 (May 2008): 189–210. http://dx.doi.org/10.1002/sat.908.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Ciocan, Dragos Florin, and Vivek Farias. "Model Predictive Control for Dynamic Resource Allocation." Mathematics of Operations Research 37, no. 3 (August 2012): 501–25. http://dx.doi.org/10.1287/moor.1120.0548.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Ghobadi, Seaid. "A dynamic DEA model for resource allocation." International Journal of Mathematics in Operational Research 1, no. 1 (2019): 1. http://dx.doi.org/10.1504/ijmor.2019.10029985.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Ghobadi, Saeid. "A dynamic DEA model for resource allocation." International Journal of Mathematics in Operational Research 17, no. 1 (2020): 50. http://dx.doi.org/10.1504/ijmor.2020.109053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Xu, Kuang, and Yuan Zhong. "Information and Memory in Dynamic Resource Allocation." Operations Research 68, no. 6 (November 2020): 1698–715. http://dx.doi.org/10.1287/opre.2019.1940.

Full text

Abstract:

We propose a general framework, dubbed stochastic processing under imperfect information (SPII), to study the impact of information constraints and memories on dynamic resource allocation. The framework involves a stochastic processing network (SPN) scheduling problem in which the scheduler may access the system state only through a noisy channel, and resource allocation decisions must be carried out through the interaction between an encoding policy (that observes the state) and an allocation policy (that chooses the allocation). Applications in the management of large-scale data centers and human-in-the-loop service systems are among our chief motivations. We quantify the degree to which information constraints reduce the size of the capacity region in general SPNs and how such reduction depends on the amount of memories available to the encoding and allocation policies. Using a novel metric, capacity factor, our main theorem characterizes the reduction in capacity region (under “optimal” policies) for all nondegenerate channels and across almost all combinations of memory sizes. Notably, the theorem demonstrates, in substantial generality, that (1) the presence of a noisy channel always reduces capacity, (2) more memory for the allocation policy always improves capacity, and (3) more memory for the encoding policy has little to no effect on capacity. Finally, all of our positive (achievability) results are established through constructive, implementable policies.

APA, Harvard, Vancouver, ISO, and other styles

49

Marin, Andrea, Sabina Rossi, and Matteo Sottana. "Dynamic Resource Allocation in Fork-Join Queues." ACM Transactions on Modeling and Performance Evaluation of Computing Systems 5, no. 1 (February 7, 2020): 1–28. http://dx.doi.org/10.1145/3372376.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Zhang, H., and C. M. Tam. "Fuzzy decision-making for dynamic resource allocation." Construction Management and Economics 21, no. 1 (January 2003): 31–41. http://dx.doi.org/10.1080/0144619032000065108.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!