A Framework of Price Bidding Configurations for Resource Usage in Cloud Computing

Li, Kenli; Liu, Chubo; Li, Keqin; Zomaya, Albert Y.

doi:10.1109/tpds.2015.2495120

Cited by 69 publications

(35 citation statements)

References 29 publications

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“…The obtained near-equilibrium solution is close to the equilibrium one [23] A cloud service request model with SLA constraints is established and based on the request model a cost-aware service request scheduling using genetic approach is presented…”

Section: Analytical Solutionmentioning

confidence: 70%

Resource Scheduling in Cloud Environmet: A Survey

Mangla¹,

Singh²,

Rana³

2016

Adv. Sci. Technol. Res. J.

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Cloud Computing offers the avant-garde services at a stretch that are too attractive for any cloud user to ignore. With its growing application and popularization, IT companies are rapidly deploying distributed data centers globally, posing numerous challenges in terms of scheduling of resources under different administrative domains. This perspective brings out certain vital factors for efficient scheduling of resources providing a wide genre of characteristics, diversity in context of level of service agreements and that too with user-contingent elasticity. In this paper, a comprehensive survey of research related to various aspects of cloud resource scheduling is provided. A comparative analysis of various resource scheduling techniques focusing on key performance parameters like Energy efficiency, Virtual Machine allocation and migration, Cost-effectiveness and Service-Level Agreement is also presented.

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Section: Analytical Solutionmentioning

confidence: 70%

Resource Scheduling in Cloud Environmet: A Survey

Mangla¹,

Singh²,

Rana³

2016

Adv. Sci. Technol. Res. J.

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“…Van et al 18 took the requirement of tasks on CPU and RAM into consideration and proposed a resource allocation scheme to improve the resource usage. Li et al 19 described a framework of price bidding configuration to achieve efficient resource usage. Apart from them, some other researchers put forward resource allocation algorithms based on cost or profit.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For convenience, in later part, call the running time of an application executed on the VM combination selected according to a strategy as the RTA (running time of the application) of the strategy. In formula (19),…”

Section: Performance Analysismentioning

confidence: 99%

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

Yao

Chen

et al. 2019

Concurrency and Computation

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Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs.Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification-based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP-based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang-scheduling-based, Makespan-based, and Max-Min-based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication-sensitive PADS applications, the advantage of ERPA is more significant. KEYWORDS execution revivification, parallel and distributed simulation, performance prediction, resource allocation INTRODUCTIONWith the development of simulation technology, the scale of parallel and distributed simulation (PADS) applications is expanding, and the demand for computing power increases as well. Currently, the parallel platforms or clusters are widely used to improve the execution efficiency, but the high cost of setting up, operating, and maintaining the basic hardware and software infrastructures severely limits the proliferation of simulation technology. The emergence of cloud computing paradigm, which receives considerable atttention in both academic and industry, 1 makes it possible to run PADS applications with reasonable cost. 2,3 In clouds, customers can access unlimited resources (eg, VMs) on demand at a ''pay-as-you-go'' model, and just pays for their actual use. Therefore, deploying PADS applications in clouds is attractive.In PADS, simulation entities are mapped on different machines/cores, and their states are modified by executing events at discrete time.These entities communicate with each other by sending and receiving time-stamped messages. During parallel execution, these entities are synchronized periodically to yield the same result as the sequential execution. As a result, the execution performance goes down. Due to the frequent synchronizations among entities, the running time of PADS is determined by the slowest entities. Besides, the network latency among entities can place a great influence on the runni...

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“…To tackle these challenges, virtualization technology is widely applied into cloud computing systems . Cloud virtualization software allows a physical computing resource to be electronically separated into one or more virtual machines (VMs) and also allows that more hardware resources are electronically combined into one VM.…”

Section: Introductionmentioning

confidence: 99%

“…2 These features make the cloud resource management is even complexity and difficulty.To tackle these challenges, virtualization technology is widely applied into cloud computing systems. [2][3][4] Cloud virtualization software allows a physical computing resource to be electronically separated into one or more virtual machines (VMs) and also allows that more hardware resources are electronically combined into one VM. Virtual machines are allocated to the user based on their application tasks.…”

mentioning

confidence: 99%

Budget‐constraint stochastic task scheduling on heterogeneous cloud systems

Tang

2017

Concurrency and Computation

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Summary In the past few years, more and more business‐to‐consumer and enterprise applications run in the heterogeneous clouds. Such cloud bag‐of‐tasks applications are usually budget constrained, and their scheduling is an essential problem for cloud provider. The problem is even more complex and challenging when the accurate knowledge about task execution time is unknown in advance. Focusing on these challenges, we first build a cloud resource management architecture and stochastic task model, which divides cloud task into two execution parts. Then, we deduce bag‐of‐tasks applications' schedule length (Makespan) and total cost according to heterogeneous clouds' online feedback information of task first part execution. Thirdly, we formulate this stochastic scheduling problem as a linear programming problem. Lastly, we propose a time and cost multiobjective stochastic task scheduling genetic algorithm, in which can find Pareto optimal schedules for stochastic cloud task that meet its budget constraint. The extensive simulation experiments were carried out on a heterogeneous cloud platform with 400 virtual machines, and tasks were derived from Parallel Workloads Archive and the analysis data of real‐world cloud systems. The experimental results show that our proposed stochastic task scheduling genetic algorithm can get shorter schedule length and lower cost with task budget constraints.

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A Framework of Price Bidding Configurations for Resource Usage in Cloud Computing

Cited by 69 publications

References 29 publications

Resource Scheduling in Cloud Environmet: A Survey

Resource Scheduling in Cloud Environmet: A Survey

An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

Budget‐constraint stochastic task scheduling on heterogeneous cloud systems

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