The Experts below are selected from a list of 9969 Experts worldwide ranked by ideXlab platform
N. Kandasamy - One of the best experts on this subject based on the ideXlab platform.
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risk aware limited lookahead control for dynamic Resource Provisioning in enterprise computing systems
Cluster Computing, 2007Co-Authors: D. Kusic, N. KandasamyAbstract:Utility or on-demand computing, a Provisioning model where a service provider makes computing infrastructure available to customers as needed, is becoming increasingly common in enterprise computing systems. Realizing this model requires making dynamic, and sometimes risky, Resource Provisioning and allocation decisions in an uncertain operating environment to maximize revenue while reducing operating cost. This paper develops an optimization framework wherein the Resource Provisioning problem is posed as one of sequential decision making under uncertainty and solved using a limited lookahead control scheme. The proposed approach accounts for the switching costs incurred during Resource Provisioning and explicitly encodes risk in the optimization problem. Simulations using workload traces from the Soccer World Cup 1998 web site show that a computing system managed by our controller generates up to 20% more profit than a system without dynamic control while incurring low control overhead.
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Risk-Aware Limited Lookahead Control for Dynamic Resource Provisioning in Enterprise Computing Systems
2006 IEEE International Conference on Autonomic Computing, 2006Co-Authors: D. Kusic, N. KandasamyAbstract:Utility or on-demand computing, a Provisioning model where a service provider makes computing infrastructure available to customers as needed, is becoming increasingly common in enterprise computing systems. Realizing this model requires making dynamic and sometimes risky, Resource Provisioning and allocation decisions in an uncertain operating environment to maximize revenue while reducing operating cost. This paper develops an optimization framework wherein the Resource Provisioning problem is posed as one of sequential decision making under uncertainty and solved using a limited lookahead control scheme. The proposed approach accounts for the switching costs incurred during Resource Provisioning and explicitly encodes risk in the optimization problem. Simulations using workload traces from the Soccer World Cup 1998 web site show that a computing system managed by our controller generates up to 20% more revenue than a system without dynamic control while incurring low control overhead.
D. Kusic - One of the best experts on this subject based on the ideXlab platform.
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risk aware limited lookahead control for dynamic Resource Provisioning in enterprise computing systems
Cluster Computing, 2007Co-Authors: D. Kusic, N. KandasamyAbstract:Utility or on-demand computing, a Provisioning model where a service provider makes computing infrastructure available to customers as needed, is becoming increasingly common in enterprise computing systems. Realizing this model requires making dynamic, and sometimes risky, Resource Provisioning and allocation decisions in an uncertain operating environment to maximize revenue while reducing operating cost. This paper develops an optimization framework wherein the Resource Provisioning problem is posed as one of sequential decision making under uncertainty and solved using a limited lookahead control scheme. The proposed approach accounts for the switching costs incurred during Resource Provisioning and explicitly encodes risk in the optimization problem. Simulations using workload traces from the Soccer World Cup 1998 web site show that a computing system managed by our controller generates up to 20% more profit than a system without dynamic control while incurring low control overhead.
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Risk-Aware Limited Lookahead Control for Dynamic Resource Provisioning in Enterprise Computing Systems
2006 IEEE International Conference on Autonomic Computing, 2006Co-Authors: D. Kusic, N. KandasamyAbstract:Utility or on-demand computing, a Provisioning model where a service provider makes computing infrastructure available to customers as needed, is becoming increasingly common in enterprise computing systems. Realizing this model requires making dynamic and sometimes risky, Resource Provisioning and allocation decisions in an uncertain operating environment to maximize revenue while reducing operating cost. This paper develops an optimization framework wherein the Resource Provisioning problem is posed as one of sequential decision making under uncertainty and solved using a limited lookahead control scheme. The proposed approach accounts for the switching costs incurred during Resource Provisioning and explicitly encodes risk in the optimization problem. Simulations using workload traces from the Soccer World Cup 1998 web site show that a computing system managed by our controller generates up to 20% more revenue than a system without dynamic control while incurring low control overhead.
Dusit Niyato - One of the best experts on this subject based on the ideXlab platform.
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optimization of Resource Provisioning cost in cloud computing
IEEE Transactions on Services Computing, 2012Co-Authors: Sivadon Chaisiri, Busung Lee, Dusit NiyatoAbstract:In cloud computing, cloud providers can offer cloud consumers two Provisioning plans for computing Resources, namely reservation and on-demand plans. In general, cost of utilizing computing Resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total Resource Provisioning cost. However, the best advance reservation of Resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' Resource prices. To address this problem, an optimal cloud Resource Provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing Resources for being used in multiple Provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of Resource Provisioning in cloud computing environments.
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SOCA - Robust cloud Resource Provisioning for cloud computing environments
2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2010Co-Authors: Sivadon Chaisiri, Dusit NiyatoAbstract:Cloud providers can offer cloud consumers two plans to provision Resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total Resource Provisioning cost. However, this Resource Provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' Resource prices can be fluctuated. Moreover, inefficiency of Resource Provisioning leads to either overProvisioning or underProvisioning problem. In this paper, we propose a robust cloud Resource Provisioning (RCRP) algorithm to minimize the total Resource Provisioning cost (i.e., overProvisioning and underProvisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total Resource Provisioning cost is close to the optimality (i.e., solution-robustness), and the overProvisioning and underProvisioning costs are significantly reduced (i.e., model-robustness).
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Robust cloud Resource Provisioning for cloud computing environments
2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2010Co-Authors: Sivadon Chaisiri, Dusit NiyatoAbstract:Cloud providers can offer cloud consumers two plans to provision Resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total Resource Provisioning cost. However, this Resource Provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' Resource prices can be fluctuated. Moreover, inefficiency of Resource Provisioning leads to either overProvisioning or underProvisioning problem. In this paper, we propose a robust cloud Resource Provisioning (RCRP) algorithm to minimize the total Resource Provisioning cost (i.e., overProvisioning and underProvisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total Resource Provisioning cost is close to the optimality (i.e., solution-robustness), and the overProvisioning and underProvisioning costs are significantly reduced (i.e., model-robustness).
Tho Le-ngoc - One of the best experts on this subject based on the ideXlab platform.
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Joint Resource Provisioning and admission control in wireless virtualized networks
2015 IEEE Wireless Communications and Networking Conference (WCNC), 2015Co-Authors: Saeedeh Parsaeefard, Mahsa Derakhshani, Vikas Jumba, Tho Le-ngocAbstract:This paper studies joint Resource Provisioning and admission control in wireless virtualized networks (WVN), where one base station of an OFDMA-based wireless network is virtualized into two types of slices with Resource-based and rate-based reservations. Aiming to maximize the total rate of WVN, first, the Resource Provisioning optimization problems are formulated by guaranteeing a minimum requirement for each slice. Via constraint relaxation and variable transformations, an iterative algorithm is developed for power and sub-carrier allocation. Due to the channel variations, WVN suffers from non-zero outage probability, i.e., slice requirements cannot always be met. To prevent this issue, we present an admission control algorithm in which slice requirements are dynamically adjusted based on channel state information. The simulation results demonstrate the effectiveness of our proposed algorithms.
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Dynamic Resource Provisioning with stable queue control for wireless virtualized networks
2015 IEEE 26th Annual International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2015Co-Authors: Vikas Jumba, Mahsa Derakhshani, Saeedeh Parsaeefard, Tho Le-ngocAbstract:This paper investigates the dynamic Resource Provisioning with queue stability in wireless virtualized networks (WVN). Aiming to maximize the total average rate of WVN over a transmission frame, a dynamic Resource Provisioning policy is proposed, while a minimum average required rate of each slice and a stable-queue constraint of WVN are preserved. Based on Lyapunov drift-plus-penalty algorithm and variable transformation techniques, an iterative algorithm is proposed for joint power and sub-carrier allocation. Performance of the proposed algorithm is evaluated by simulations performed investigate the effects of various system parameters on the average rate of WVN and queue stability.
Sivadon Chaisiri - One of the best experts on this subject based on the ideXlab platform.
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optimization of Resource Provisioning cost in cloud computing
IEEE Transactions on Services Computing, 2012Co-Authors: Sivadon Chaisiri, Busung Lee, Dusit NiyatoAbstract:In cloud computing, cloud providers can offer cloud consumers two Provisioning plans for computing Resources, namely reservation and on-demand plans. In general, cost of utilizing computing Resources provisioned by reservation plan is cheaper than that provisioned by on-demand plan, since cloud consumer has to pay to provider in advance. With the reservation plan, the consumer can reduce the total Resource Provisioning cost. However, the best advance reservation of Resources is difficult to be achieved due to uncertainty of consumer's future demand and providers' Resource prices. To address this problem, an optimal cloud Resource Provisioning (OCRP) algorithm is proposed by formulating a stochastic programming model. The OCRP algorithm can provision computing Resources for being used in multiple Provisioning stages as well as a long-term plan, e.g., four stages in a quarter plan and twelve stages in a yearly plan. The demand and price uncertainty is considered in OCRP. In this paper, different approaches to obtain the solution of the OCRP algorithm are considered including deterministic equivalent formulation, sample-average approximation, and Benders decomposition. Numerical studies are extensively performed in which the results clearly show that with the OCRP algorithm, cloud consumer can successfully minimize total cost of Resource Provisioning in cloud computing environments.
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SOCA - Robust cloud Resource Provisioning for cloud computing environments
2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2010Co-Authors: Sivadon Chaisiri, Dusit NiyatoAbstract:Cloud providers can offer cloud consumers two plans to provision Resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total Resource Provisioning cost. However, this Resource Provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' Resource prices can be fluctuated. Moreover, inefficiency of Resource Provisioning leads to either overProvisioning or underProvisioning problem. In this paper, we propose a robust cloud Resource Provisioning (RCRP) algorithm to minimize the total Resource Provisioning cost (i.e., overProvisioning and underProvisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total Resource Provisioning cost is close to the optimality (i.e., solution-robustness), and the overProvisioning and underProvisioning costs are significantly reduced (i.e., model-robustness).
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Robust cloud Resource Provisioning for cloud computing environments
2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), 2010Co-Authors: Sivadon Chaisiri, Dusit NiyatoAbstract:Cloud providers can offer cloud consumers two plans to provision Resources, namely reservation and on-demand plans. With the reservation plan, the consumer can reduce the total Resource Provisioning cost. However, this Resource Provisioning is challenging due to the uncertainty. For example, consumers' demand and providers' Resource prices can be fluctuated. Moreover, inefficiency of Resource Provisioning leads to either overProvisioning or underProvisioning problem. In this paper, we propose a robust cloud Resource Provisioning (RCRP) algorithm to minimize the total Resource Provisioning cost (i.e., overProvisioning and underProvisioning costs). Various types of uncertainty are considered in the algorithm. To obtain the optimal solution, a robust optimization model is formulated and solved. Numerical studies are extensively performed in which the results show that the solution obtained from the RCRP algorithm achieves both solution-and model-robustness. That is, the total Resource Provisioning cost is close to the optimality (i.e., solution-robustness), and the overProvisioning and underProvisioning costs are significantly reduced (i.e., model-robustness).
