Relevant bibliographies by topics / Dynamic Capacity Provisioning

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Academic literature on the topic 'Dynamic Capacity Provisioning'

Author: Grafiati
Published: 3 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Dynamic Capacity Provisioning"

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Ramamurthy, R., Z. Bogdanowicz, S. Samieian, et al. "Capacity performance of dynamic provisioning in optical networks." Journal of Lightwave Technology 19, no. 1 (2001): 40–48. http://dx.doi.org/10.1109/50.914483.

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M., G. Saravanan, and Natarajan M. "FUZZY ALGORITHM USING VIRTUAL MACHINES SCHEDULING IN DISTRIBUTER SYSTEM AUTOMATIC OVERLOADED IN DISTRIBUTE DATABASE." International Journal of Engineering Research and Modern Education 3, no. 2 (2018): 4–7. https://doi.org/10.5281/zenodo.1402725.

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In present day virtualization based register mists, applications share the hidden equipment by running in disconnected Virtual Machines (VMs). Each VM, amid its underlying creation, is arranged with a specific measure of processing assets, (for example, CPU, memory and I/O). A key factor for accomplishing economies of scale in a register cloud is asset provisioning, which alludes to apportioning assets to VMs to coordinate their workload. Commonly, effective provisioning is accomplished by two operations: (1) static asset provisioning. VMs are made with indicated size and after that united onto an arrangement of physical servers. The VM limit does not change; and (2) dynamic asset provisioning. VM limit is powerfully changed in accordance with coordinate workload vacillations. In both static and dynamic provisioning, VM estimating is maybe the most fundamental stride. VM measuring alludes to the estimation of the measure of assets that ought to be allotted to a VM. The target of VM estimating is to guarantee that VM limit is comparable with the workload. While over-provisioning squanders exorbitant assets, under-provisioning debases application execution and may lose clients. In this venture proposed gathered VM provisioning approach in which various VMs are merged and provisioned in view of a gauge of their total limit needs.
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Pham-Nguyen, Hoang-Nam, and Quang Tran-Minh. "Dynamic Resource Provisioning on Fog Landscapes." Security and Communication Networks 2019 (May 2, 2019): 1–15. http://dx.doi.org/10.1155/2019/1798391.

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A huge amount of smart devices which have capacity of computing, storage, and communication to each other brings forth fog computing paradigm. Fog computing is a model in which the system tries to push data processing from cloud servers to “near” IoT devices in order to reduce latency time. The execution orderings and the deployed places of services make significant effect on the overall response time of an application. Beside new research directions in fog computing, e.g., fog-cloud collaboration, service scalability, fog scalability, mobile fog computing, fog federation, trade-off between energy consumption and communication efficiency, duration of storing data locally, storage security and communication security, and semantic-aware fog computing, the service deployment problem is one of the attractive research fields of fog computing. The service deployment is a multiobjective optimization problem; there are so many proposed solutions for various targets, such as response time, communication cost, and energy consumption. In this paper, we focus on the optimization problem which minimizes the overall response time of an application with awareness of network usage and server usage. Then, we have conducted experiments on two service deployment strategies, called cloudy and foggy strategies. We analyze numerically the overall response time, network usage, and server usage of those two strategies in order to prove the effectiveness of our proposed foggy service deployment strategy.
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Sood, Sandeep K. "A Value Based Dynamic Resource Provisioning Model in Cloud." International Journal of Cloud Applications and Computing 3, no. 1 (2013): 1–12. http://dx.doi.org/10.4018/ijcac.2013010101.

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Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service (QoS) and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.
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Sood, Sandeep K. "A Value Based Dynamic Resource Provisioning Model in Cloud." International Journal of Cloud Applications and Computing 3, no. 2 (2013): 35–46. http://dx.doi.org/10.4018/ijcac.2013040104.

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Cloud computing has become an innovative computing paradigm, which aims at providing reliable, customized, Quality of Service (QoS) and guaranteed computing infrastructures for users. Efficient resource provisioning is required in cloud for effective resource utilization. For resource provisioning, cloud provides virtualized computing resources that are dynamically scalable. This property of cloud differentiates it from the traditional computing paradigm. But the initialization of a new virtual instance causes a several minutes delay in the hardware resource allocation. Furthermore, cloud provides a fault tolerant service to its clients using the virtualization. But, in order to attain higher resource utilization over this technology, a technique or a strategy is needed using which virtual machines can be deployed over physical machines by predicting its need in advance so that the delay can be avoided. To address these issues, a value based prediction model in this paper is proposed for resource provisioning in which a resource manager is used for dynamically allocating or releasing a virtual machine depending upon the resource usage rate. In order to know the recent resource usage rate, the resource manager uses sliding window to analyze the resource usage rate and to predict the system behavior in advance. By predicting the resource requirements in advance, a lot of processing time can be saved. Earlier, a server has to perform all the calculations regarding the resource usage that in turn wastes a lot of processing power thus decreasing its overall capacity to handle the incoming request. The main feature of the proposed model is that a lot of load is being shifted from the individual server to the resource manager as it performs all the calculations and therefore the server is free to handle the incoming requests to its full capacity.
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Jeyarani, R., N. Nagaveni, and R. Vasanth Ram. "Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud." International Journal of Intelligent Information Technologies 7, no. 2 (2011): 25–44. http://dx.doi.org/10.4018/jiit.2011040102.

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Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum that represents target servers for VM placement. The experimental results of SAPSO was compared with Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) and the results show that SAPSO outperforms the latter for power aware adaptive VM provisioning in a large scale, heterogeneous and dynamic cloud environment.
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M., Rakesh Chowdary, Yashwanth Reddy A., and Abhishek N. "RESOURCE MANAGEMENT IN DEALING WITH SECURITY CHALLENGES IN CLOUD BASED ENVIRONMENT." International Journal of Applied and Advanced Scientific Research 1, no. 2 (2016): 152–55. https://doi.org/10.5281/zenodo.1034457.

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Cloud Service Brokers includes technology consultants, business professional service organizations, registered brokers and agents, and influencers that help guide consumers in the selection of cloud computing solutions. Service brokers concentrate on the negotiation of the relationships between consumers and providers without owning or managing the whole Cloud infrastructure. Moreover, they add extra services on top of a Cloud provider’s infrastructure to make up the user’s Cloud environment.With the emergence of many new data centers around the globe; energy consumption by those data centers has been tremendously increased. Dynamic capacity provisioning is a promising approach for reducing energy consumption by dynamically adjusting the number of active machines to match resource demands.
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Goswami, Veena, S. S. Patra, and G. B. Mund. "Dynamic Provisioning and Resource Management for Multi-Tier Cloud Based Applications." Foundations of Computing and Decision Sciences 38, no. 3 (2013): 175–91. http://dx.doi.org/10.2478/fcds-2013-0008.

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Abstract Dynamic capacity provisioning is a useful technique for handling the workload variations seen in cloud environment. In this paper, we propose a dynamic provisioning technique for multi-tier applications to allocate resources efficiently using queueing model. It dynamically increases the mean service rate of the virtual machines to avoid congestion in the multi-tier environments. An optimization model to minimize the total number of virtual machines for computing resources in each tier has been presented. Using the supplementary variable and the recursive techniques, we obtain the system-length distributions at pre-arrival and arbitrary epochs. Some important performance indicators such as blocking probability, request waiting time and number of tasks in the system and in the queue have also been investigated. Finally, computational results showing the effect of model parameters on key performance indicators are presented.
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Guerin, Roch, Kartik Hosanagar, Xinxin Li, and Soumya Sen. "Shared or Dedicated Infrastructures: On the Impact of Reprovisioning Ability." MIS Quarterly 43, no. 4 (2019): 1059–79. http://dx.doi.org/10.25300/misq/2019/14857.

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New technologies, such as virtualization, are transforming the way in which software and services are deployed and delivered to their users. They are behind the emergence of IT offerings such as cloud computing and converged networks, and manifest themselves through two important trends: (1) lower the cost of sharing a common infrastructure across multiple services with disparate resource requirements, and (2) dynamic provisioning of capacity in response to demand. Conventional wisdom is that both of these capabilities are synergistic, with greater provisioning flexibility improving the benefits derived from sharing computing or network resources. Consequently, a service operator should now always favor the use of a shared infrastructure over dedicated solutions when hosting multiple services. In this paper, we ask whether this is indeed the case, and investigate the dual impact of lower costs of sharing and provisioning flexibility on shared and dedicated infrastructures. The investigation reveals that while lower costs are always expected to favor infrastructure sharing, dynamic provisioning plays an ambiguous role. Reprovisioning improves both shared and dedicated solutions, but can do so differently and can sometimes favor a dedicated infrastructure. Our findings help illustrate that the technology trends, such as virtualization, behind cloud computing need not always favor the deployment of services on a shared infrastructure.
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S, Suriya, Madhvesh V S, and Mrudhhula V S. "Cost Efficient Resource Provisioning using ACO." Journal of Soft Computing Paradigm 6, no. 4 (2025): 365–77. https://doi.org/10.36548/jscp.2024.4.003.

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Cloud computing has revolutionized the way computational resources are provisioned and managed, offering scalable and flexible services to meet diverse user demands. However, cost-effective resource management is a very challenging process because of the dynamism and diversity of the aspects of cloud environments that changes in terms of load and resources. The traditional sources of resource acquisition do not have the capacity to deliver the alternatives expected on their cost without having a negative impact on the performance of the resources. This work describes the new approach of utilizing the ACO for resource management in cloud computing. The method that is proposed contains the potential to incorporate pheromone-based heuristics for controlling the process of resource allocation such that reduced operational costs are ensured as well as the performance of the process is maintained at the optimal rate. ACO explains the behaviour of the search process where the allocation of the tasks is done based on the values of the pheromone trails and the heuristic information. An ACO model that includes dynamic measurements for the diverse cloud environment and several adaptive mechanisms for creating more tasks and virtual machines (VMs) can be considered a helpful solution for actual cloud applications. The results of the experiments are high in terms of cost-effectiveness compared to other approaches and reflect the ACO’s ability to function in dynamic cloud environments.
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Books on the topic "Dynamic Capacity Provisioning"

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Nelson, Rebecca, and Richard Coe. Agroecological Intensification of Smallholder Farming. Edited by Ronald J. Herring. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780195397772.013.006.

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The smallholder farmers who cultivate many of the planet’s diverse production systems are faced with numerous challenges, including poverty, shrinking farm sizes, degrading natural resources, and climate variability and change. Efforts to improve the performance of smallholder farming systems focus on improving access to input and output markets, improving farm resource use efficiency, and improving resources invested in smallholder farming. In order to support market-oriented production and self-provisioning, there is a need for greater focus on agroecological intensification (AEI) of smallholder production systems. This chapter provides an overview of some of the research frontiers supporting AEI. Market-oriented and agroecological approaches may or may not conflict, and more effort should be made to ensure that they are mutually reinforcing. To be reliable, value chains must be founded on sound production ecology. Agroecological options may be limited if farmers cannot participate in markets that support investment in the intensification and diversification of these systems. Because options must be adapted to farmers’ heterogeneous and dynamic contexts, successful AEI will require that specifics be optimized locally. Researchers must therefore understand and communicate relevant agroecological principles, and farmers and intermediaries must develop their capacity to adapt the principles to local needs and realities.
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Book chapters on the topic "Dynamic Capacity Provisioning"

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Chang Yenchih, Wang Kuochen, and Hsu Yi-Huai. "Dynamic Resource Provisioning with Hotspot Anticipation for MMOG Clouds." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2015. https://doi.org/10.3233/978-1-61499-484-8-2053.

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We propose a dynamic resource provisioning with hotspot anticipation scheme, called NN-Player+DRP-HA that employs a finite state machine model to monitor the movement of avatars in a virtual world. Furthermore, we use a finite state machine to represent possible avatar states and state transitions. By combining the state of each avatar in a game zone with a neural network (NN) predictor, we may figure out potential workload produced by hotspots, and then allocate appropriate computing resources to support the game zone. Experimental results support that the proposed NN-Player+DRP-HA scheme can avoid most of under-allocation events with an acceptable over-allocation rate. Compared with a representative dynamic resource provisioning method, called NN-Player+DRP, the proposed NN-Player+DRP-HA reduces the probability of under-allocation events from 2.16% to 0.42% (80% improvement) in terms of CPU capacity of a VM, under the premise of controlling the CPU over-allocation rate within the CPU capacity of one VM.
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Akar, N., and C. Sahin. "Dynamic capacity provisioning in virtual path-based networks using reinforcement learning." In Providing Quality of Service in Heterogeneous Environments, Proceedings of the 18th International Teletraffic Congress - ITC-18. Elsevier, 2003. http://dx.doi.org/10.1016/s1388-3437(03)80219-6.

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Kong Lingjun, Xu Wensheng, and Cha Jianzhong. "Provisioning Service Resources for Cloud Manufacturing." In 20th ISPE International Conference on Concurrent Engineering. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-302-5-216.

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Cloud manufacturing is a new service-oriented networked manufacturing paradigm which can integrate various physical manufacturing resources and manufacturing capacities and provide manufacturing services of the lifecycle for the product. It is a new research direction in the field of the advanced manufacturing. In this paper, a provisioning method of service resources for cloud manufacturing is studied. Firstly, the service-oriented architectures are investigated to decide the service architecture for the encapsulation of manufacturing resources. Then a three-step provisioning method of manufacturing services are proposed, a function-classification based manufacturing service interface is defined, and the encapsulation strategies for four categories of manufacturing resources including intelligence resource, knowledge resource, tool resource and manufacturing capacity are put forward, and the dynamic provisioning process of manufacturing service is described. By the proposed method, the four categories of manufacturing resources can be dynamically provisioned as services with well-defined service interfaces for cloud manufacturing.
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Oyo, Benedict, Billy Mathias Kalema, and Isdore Paterson Guma. "Re-Conceptualizing Smallholders' Food Security Resilience in Sub-Saharan Africa." In Advances in System Dynamics and Control. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4077-9.ch018.

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Smallholder African systems operate in harsh environments of climate changes, resource scarcity, environmental degradation, market failures, and weak public and/or donor support. The smallholders must therefore be prepared to survive by self-provisioning. This chapter examines the nature of vulnerability of smallholders' food security caused by above conditions in the context of system dynamics modelling. The results show that smallholders co-exist whereby the non-resilient households offer labor to the resilient households for survival during turbulent seasons irrespective of the magnitude of external shocks and stressors. In addition, non-resilient households cannot be liberated by external handouts but rather through building their capacity for self-reliance. Using simulation evidence, this chapter supports the claim that in the next decade only resilient households will endure the extreme situations highlighted above. Future research that employs similar systems-based methods are encouraged to explore how long-term food security among smallholders can be sustained.
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Oyo, Benedict, Billy Mathias Kalema, and Isdore Paterson Guma. "Re-Conceptualizing Smallholders' Food Security Resilience in Sub-Saharan Africa." In Research Anthology on Strategies for Achieving Agricultural Sustainability. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5352-0.ch016.

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Smallholder African systems operate in harsh environments of climate changes, resource scarcity, environmental degradation, market failures, and weak public and/or donor support. The smallholders must therefore be prepared to survive by self-provisioning. This chapter examines the nature of vulnerability of smallholders' food security caused by above conditions in the context of system dynamics modelling. The results show that smallholders co-exist whereby the non-resilient households offer labor to the resilient households for survival during turbulent seasons irrespective of the magnitude of external shocks and stressors. In addition, non-resilient households cannot be liberated by external handouts but rather through building their capacity for self-reliance. Using simulation evidence, this chapter supports the claim that in the next decade only resilient households will endure the extreme situations highlighted above. Future research that employs similar systems-based methods are encouraged to explore how long-term food security among smallholders can be sustained.
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Gawanmeh, Amjad, Ahmad Alomari, Alain April, Ali Alwadi, and Sazia Parvin. "Green Evolutionary-Based Algorithm for Multiple Services Scheduling in Cloud Computing." In Advances in Business Information Systems and Analytics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3038-1.ch003.

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The era of cloud computing allowed the instant scale up of provided services into massive capacities without the need for investing in any new on site infrastructure. Hence, the interest of this type of services has been increased, in particular, by medium scale entities who can afford to completely outsource their data-center and their infrastructure. In addition, large companies may wish to provide support for wide range of load capacities, including peak ones, however, this will require very higher costs in order to build larger data centers internally. Cloud services can provide services for these companies according to their need whether in peak load capacity of low ones. Therefore, resource sharing and provisioning is considered one of the most challenging problems in cloud based services since these services have become more numerous and dynamic. As a result, assigning tasks and services requests into available resources has become a persistent problem in cloud computing, given the large number of variables, and the increasing types of services, demand, and requirement. Scheduling services using a limited number of resources is problem that has been under study since the evolution of cloud computing. However, there are several open areas for improvements due to the large number of optimization variables. In general, the scheduling of services on available resources is considered NP complete. As a result, several heuristic based methods were proposed in order to enhance the efficiency of cloud systems. Since the problem has several optimization parameters, there are still several improvements that can be done in this area. This chapter discusses the formalization of the problem of scheduling multiple tasks by single user and multiple users, and then presents a proposed solution for each individual case. First, an algorithm is presented and evaluated for optimum schedule that allocates a number of subtasks on a given number of resources; the algorithm was shown to be linear vs. number of users. Then, an algorithm is presented to address the problem of multiple users allocations, each, with multiple subtasks. The algorithm was design using the single user allocation algorithm as a selection function. Since, this problem is known to be NP complete, heuristic based methods are usually used in order to provide better solutions. Therefore, a green evolutionary based algorithm is proposed in order to address the problem of resource allocation with large number of users. In addition, the algorithm presents allocation schedule with better utility, while the execution time is linear vs. different parameters. The results obtained in this work show that it overcomes the outcome of one of the most efficient algorithms presented in this regard that was based on game theory. Further, this method works with no restrictions on the problem parameters as opposed to game theory methods that require certain parameters restrictions on cost vector or compaction time matrix. On the other hand, the main limitation of the proposed algorithm is that it is only applicable to the scheduling problem of multiple tasks that has one price vector and one execution time vector. However, scheduling multiple users, each with subtasks that have their own price and execution time vector, is very complex problem and beyond the scope of this work, hence it will be addressed in future work.
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Conference papers on the topic "Dynamic Capacity Provisioning"

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Lin, Minghong, Adam Wierman, Lachlan L. H. Andrew, and Eno Thereska. "Online dynamic capacity provisioning in data centers." In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2011. http://dx.doi.org/10.1109/allerton.2011.6120298.

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Zhang, Qi, Mohamed Faten Zhani, Shuo Zhang, Quanyan Zhu, Raouf Boutaba, and Joseph L. Hellerstein. "Dynamic energy-aware capacity provisioning for cloud computing environments." In the 9th international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2371536.2371562.

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Balanici, Mihail, Behnam Shariati, Pooyan Safari, Geronimo Bergk, and Johannes Karl Fischer. "Autonomous Capacity Adjustment with Dynamic Margin Allocation for Optical Enterprise Links." In Optical Fiber Communication Conference. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/ofc.2024.m1h.2.

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This work presents a novel machine learning-based dynamic capacity allocation scheme for efficient bandwidth provisioning of optical links. It offers an average hourly capacity saving of over 75% compared to traditional static capacity allocation mechanisms.
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Huang, Shu, and Rudra Dutta. "Spare Capacity Provisioning for Dynamic Traffic Grooming in Optical Networks." In 2006 3rd International Conference on Broadband Communications, Networks and Systems. IEEE, 2006. http://dx.doi.org/10.1109/broadnets.2006.4374369.

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Tian, Wenhong, and Harry Perros. "Modeling approaches and provisioning algorithms for dynamic bandwidth adjustment in multi-media networks." In 2006 International Symposium on High Capacity Optical Networks and Enabling Technologies (HONET). IEEE, 2006. http://dx.doi.org/10.1109/honet.2006.5338404.

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Chen, Li-wei, and Eytan Modiano. "A Geometric Approach to Capacity Provisioning in WDM Networks with Dynamic Traffic." In 2006 40th Annual Conference on Information Sciences and Systems. IEEE, 2006. http://dx.doi.org/10.1109/ciss.2006.286404.

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Adnan, Muhammad Abdullah, Ryo Sugihara, Yan Ma, and Rajesh K. Gupta. "Energy-optimized dynamic deferral of workload for capacity provisioning in data centers." In 2013 International Green Computing Conference (IGCC). IEEE, 2013. http://dx.doi.org/10.1109/igcc.2013.6604515.

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Balanici, Mihail, Pooyan Safari, Behnam Shariati, Aydin Jafari, Johannes Karl Fischer, and Ronald Freund. "Live Demonstration of Autonomous Link-Capacity Adjustment in Optical Metro-Aggregation Networks." In Optical Fiber Communication Conference. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/ofc.2024.m3z.3.

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We demonstrate a real-time ML-assisted network automation pipeline for dynamic, autonomous link-capacity allocation based on traffic-flow forecasting for optical metro aggregation networks. Its performance is compared to that of a classic, static bandwidth provisioning scheme.
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Shuo Zhang, Yaping Liu, Baosheng Wang, and Ruixin Zhang. "Analysis and modeling of dynamic capacity provisioning problem for a heterogeneous data center." In 2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2013. http://dx.doi.org/10.1109/icufn.2013.6614927.

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Yang, Xi, Chris Tracy, Jerry Sobieski, and Tom Lehman. "GMPLS-Based Dynamic Provisioning and Traffic Engineering of High-Capacity Ethernet Circuits in Hybrid Optical/Packet Networks." In Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications. IEEE, 2006. http://dx.doi.org/10.1109/infocom.2006.34.

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