Optimal Bids for Spot VMs in a Cloud for Deadline Constrained Jobs

Chen

2014

Wireless Pers Commun

158

As mobile computing has been developed for decades, a new model for mobile computing, namely, mobile cloud computing, emerges resulting from the marriage of powerful yet affordable mobile devices and cloud computing. In this paper we survey existing mobile cloud computing applications, as well as speculate future generation mobile cloud computing applications. We provide insights for the enabling technologies and challenges that lie ahead for us to move forward from mobile computing to mobile cloud computing for building the next generation mobile cloud applications. For each of the challenges, we provide a survey of existing solutions, identify research gaps, and suggest future research areas.

Section: Cloud Pricingmentioning

confidence: 99%

A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

Chen

2014

Wireless Pers Commun

158

“…Their "AMAZING" tool can intelligently adapt the bid from detected state pattern. Zafer et al [20] study dynamic bidding policy for spot instances in the context of deadline constrained jobs. Mattess et al [21] investigate the strategies of extending local cluster using spot instances in an economic way when peak load coming.…”

Section: Related Workmentioning

confidence: 99%

CAP3: A Cloud Auto-Provisioning Framework for Parallel Processing Using On-Demand and Spot Instances

Huang

2013 IEEE Sixth International Conference on Cloud Computing

Tak

et al. 2013

Abstract-Cloud computing has drawn increasing attention from the scientific computing community due to its ease of use, elasticity, and relatively low cost. Because a high-performance computing (HPC) application is usually resource demanding, without careful planning, it can incur a high monetary expense even in Cloud. We design a tool called CAP 3 (Cloud AutoProvisioning framework for Parallel Processing) to help a user minimize the expense of running an HPC application in Cloud, while meeting the user-specified job deadline. Given an HPC application, CAP 3 automatically profiles the application, builds a model to predict its performance, and infers a proper cluster size that can finish the job within its deadline while minimizing the total cost. To further reduce the cost, CAP 3 intelligently chooses the Cloud's reliable on-demand instances or low-cost spot instances, depending on whether the remaining time is tight in meeting the application's deadline. Experiments on Amazon EC2 show that the execution strategy given by CAP 3 is costeffective, by choosing a proper cluster size and a proper instance type (on-demand or spot).

“…If e equals 1, the remaining time to the deadline equals the required time if T j is processed by VM i . We can learn from the definition that the acceptable price is in proportion to e. We design a weak equilibrium operator to fit the mapping f in (18), which uses an exponential function and a non-uniform mutation operator [25]. The exponential function can control the overall shape of its curve, and the non-uniform mutation operator is used to locally fine-tune the value of the operator.…”

Section: Acceptable Price Calculationmentioning

confidence: 99%

“…Moreover, in order to achieve high performance and reduce the overhead of repeated execution, the work proposes an optimal price prediction algorithm. Zafer et al [18] considered the profit maximization problem for parallel and serial job under dynamic virtual resource pricing model. A discrete-time stochastic dynamic programming formulation is used to format the problem.…”

Section: Introductionmentioning

confidence: 99%

Maximizing the profits of cloud service providers via dynamic virtual resource renting approach

Zhou

Sun

et al. 2015

J Wireless Com Network

Promoted by the leading industrial companies, cloud computing has gained widespread concern recently. With an increasing number of cloud service providers (CSPs) delivering services to customers from the cloud, maximizing the profits of CSPs becomes a critical problem. Existing approaches are difficult to solve the problem because they do not make full use of temporal price differences. This paper introduces a dynamic virtual resource renting approach that attempts to dynamically adjust the virtual resource rental strategy according to price distribution and task urgency. Considering task urgency and price distribution, we design a weak equilibrium operator to calculate the acceptable price for each type of virtual resource. All types of virtual resources that are at an acceptable price are inserted into a set. Then, a price prediction algorithm is presented to predict the price of virtual resources at the next price interval. Finally, we design a novel rental decision-making algorithm to select the most profitable resource from the set. We have implemented our approach and conducted experiments on both real and synthetic datasets. The results demonstrate that our approach obtain the better profit than other five approaches.