Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/527583
Title: Dynamic Scheduling and Pricing in Wireless Cloud Computing
Authors: Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL, USA;Shaolei Ren ; Van der Schaar, Mihaela
subject: cloud computing; computer centres; cooling; decision making; demand side management; dynamic scheduling; pricing; queueing theory; Dyn-SP algorithm; asymptotic analysis; close-to-optimal average profit; cloud services; control parameter tuning; cooling system energy consumption; data center; delay-tolerant batch services; demand-side management; dynamic scheduling and pricing algorithm; job queue length; optimal offline algorithm; pricing decision optimization; queue length information; queueing delay; scheduling decision optimization; service provider long-term profit; static analysis; trace-based simulation; wireless cloud computing system; Cloud computing; Pricing; Wireless networks; Cloud computing; Communication/Networking and Information Technology; Computer Systems Organization; Emerging technologies; General; Mobile Computing; Support services; System architectures; integration and modeling; pricing; profit maximization; scheduling; stochastic optimization;
Year: 2014
Publisher: IEEE
Abstract: In this paper, we consider a wireless cloud computing system in which the service provider operates a data center and provides cloud services to its subscribers at dynamic prices. We propose a joint optimization of scheduling and pricing decisions for delay-tolerant batch services to maximize the service provider's long-term profit. Unlike the existing research on jointly scheduling and pricing that focuses on static or asymptotic analysis, we focus on a dynamic setting and develop a provably-efficient Dynamic Scheduling and Pricing (Dyn-SP) algorithm which, without the necessity of predicting the future information, can be applied to an arbitrarily random environment that may follow an arbitrary trajectory overtime. We prove that, compared to the optimal offline algorithm with future information, Dyn-SP produces a close-to-optimal average profit while bounding the job queue length in the data center. We perform a trace-based simulation study to validate Dyn-SP. In particular, we show both analytically and numerically that a desired tradeoff between the profit and queueing delay can be obtained by appropriately tuning the control parameter. Our results also indicate that, compared to the existing algorithms which neglect demand-side management, cooling system energy consumption, and/or the queue length information, Dyn-SP achieves a higher average profit while incurring (almost) the same average queueing delay.
URI: http://localhost/handle/Hannan/241498
http://localhost/handle/Hannan/527583
ISSN: 1536-1233
volume: 13
issue: 10
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6512487.pdf852.2 kBAdobe PDF
Title: Dynamic Scheduling and Pricing in Wireless Cloud Computing
Authors: Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL, USA;Shaolei Ren ; Van der Schaar, Mihaela
subject: cloud computing; computer centres; cooling; decision making; demand side management; dynamic scheduling; pricing; queueing theory; Dyn-SP algorithm; asymptotic analysis; close-to-optimal average profit; cloud services; control parameter tuning; cooling system energy consumption; data center; delay-tolerant batch services; demand-side management; dynamic scheduling and pricing algorithm; job queue length; optimal offline algorithm; pricing decision optimization; queue length information; queueing delay; scheduling decision optimization; service provider long-term profit; static analysis; trace-based simulation; wireless cloud computing system; Cloud computing; Pricing; Wireless networks; Cloud computing; Communication/Networking and Information Technology; Computer Systems Organization; Emerging technologies; General; Mobile Computing; Support services; System architectures; integration and modeling; pricing; profit maximization; scheduling; stochastic optimization;
Year: 2014
Publisher: IEEE
Abstract: In this paper, we consider a wireless cloud computing system in which the service provider operates a data center and provides cloud services to its subscribers at dynamic prices. We propose a joint optimization of scheduling and pricing decisions for delay-tolerant batch services to maximize the service provider's long-term profit. Unlike the existing research on jointly scheduling and pricing that focuses on static or asymptotic analysis, we focus on a dynamic setting and develop a provably-efficient Dynamic Scheduling and Pricing (Dyn-SP) algorithm which, without the necessity of predicting the future information, can be applied to an arbitrarily random environment that may follow an arbitrary trajectory overtime. We prove that, compared to the optimal offline algorithm with future information, Dyn-SP produces a close-to-optimal average profit while bounding the job queue length in the data center. We perform a trace-based simulation study to validate Dyn-SP. In particular, we show both analytically and numerically that a desired tradeoff between the profit and queueing delay can be obtained by appropriately tuning the control parameter. Our results also indicate that, compared to the existing algorithms which neglect demand-side management, cooling system energy consumption, and/or the queue length information, Dyn-SP achieves a higher average profit while incurring (almost) the same average queueing delay.
URI: http://localhost/handle/Hannan/241498
http://localhost/handle/Hannan/527583
ISSN: 1536-1233
volume: 13
issue: 10
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6512487.pdf852.2 kBAdobe PDF
Title: Dynamic Scheduling and Pricing in Wireless Cloud Computing
Authors: Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL, USA;Shaolei Ren ; Van der Schaar, Mihaela
subject: cloud computing; computer centres; cooling; decision making; demand side management; dynamic scheduling; pricing; queueing theory; Dyn-SP algorithm; asymptotic analysis; close-to-optimal average profit; cloud services; control parameter tuning; cooling system energy consumption; data center; delay-tolerant batch services; demand-side management; dynamic scheduling and pricing algorithm; job queue length; optimal offline algorithm; pricing decision optimization; queue length information; queueing delay; scheduling decision optimization; service provider long-term profit; static analysis; trace-based simulation; wireless cloud computing system; Cloud computing; Pricing; Wireless networks; Cloud computing; Communication/Networking and Information Technology; Computer Systems Organization; Emerging technologies; General; Mobile Computing; Support services; System architectures; integration and modeling; pricing; profit maximization; scheduling; stochastic optimization;
Year: 2014
Publisher: IEEE
Abstract: In this paper, we consider a wireless cloud computing system in which the service provider operates a data center and provides cloud services to its subscribers at dynamic prices. We propose a joint optimization of scheduling and pricing decisions for delay-tolerant batch services to maximize the service provider's long-term profit. Unlike the existing research on jointly scheduling and pricing that focuses on static or asymptotic analysis, we focus on a dynamic setting and develop a provably-efficient Dynamic Scheduling and Pricing (Dyn-SP) algorithm which, without the necessity of predicting the future information, can be applied to an arbitrarily random environment that may follow an arbitrary trajectory overtime. We prove that, compared to the optimal offline algorithm with future information, Dyn-SP produces a close-to-optimal average profit while bounding the job queue length in the data center. We perform a trace-based simulation study to validate Dyn-SP. In particular, we show both analytically and numerically that a desired tradeoff between the profit and queueing delay can be obtained by appropriately tuning the control parameter. Our results also indicate that, compared to the existing algorithms which neglect demand-side management, cooling system energy consumption, and/or the queue length information, Dyn-SP achieves a higher average profit while incurring (almost) the same average queueing delay.
URI: http://localhost/handle/Hannan/241498
http://localhost/handle/Hannan/527583
ISSN: 1536-1233
volume: 13
issue: 10
Appears in Collections:2014

Files in This Item:
File SizeFormat 
6512487.pdf852.2 kBAdobe PDF