Cloud auto-scaling with deadline and budget constraints

@article{Mao2010CloudAW,
  title={Cloud auto-scaling with deadline and budget constraints},
  author={Ming Mao and Jie Li and Marty Humphrey},
  journal={2010 11th IEEE/ACM International Conference on Grid Computing},
  year={2010},
  pages={41-48}
}
  • Ming MaoJie LiM. Humphrey
  • Published 1 October 2010
  • Computer Science
  • 2010 11th IEEE/ACM International Conference on Grid Computing
Clouds have become an attractive computing platform which offers on-demand computing power and storage capacity. Its dynamic scalability enables users to quickly scale up and scale down underlying infrastructure in response to business volume, performance desire and other dynamic behaviors. However, challenges arise when considering computing instance non-deterministic acquisition time, multiple VM instance types, unique cloud billing models and user budget constraints. Planning enough… 

Figures and Tables from this paper

Auto-scaling to minimize cost and meet application deadlines in cloud workflows

  • Ming MaoM. Humphrey
  • Computer Science
    2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
  • 2011
This paper presents an approach whereby the basic computing elements are virtual machines (VMs) of various sizes/costs, jobs are specified as workflows, users specify performance requirements by assigning (soft) deadlines to jobs, and the goal is to ensure all jobs are finished within their deadlines at minimum financial cost.

Cost-Time Performance of Scaling Applications on the Cloud

A measurement-driven analytical modeling approach to determine the largest Pareto-optimal problem size and its corresponding cloud configuration for execution and investigates fixed-cost-time scaling of applications and shows that using resources with higher PCR yields better cost-time performance.

HEFT based Cloud Auto-Scaling Algorithm with Budget Constraints

Results have shown that proposed algorithm optimizes the application performance with limited budget compared to other four approaches and works well even if the budget is not enough to make sure all jobs finished within their deadline.

Dynamic Resource Provisioning and Scheduling with Deadline Constraint in Elastic Cloud

This paper proposes adaptive resource management policy to handle requests of deadline-bound application with elastic cloud, and design analytical provision model for adaptive provision based on queuing theory by introducing a key metric named average interval time.

Scaling and Scheduling to Maximize Application Performance within Budget Constraints in Cloud Workflows

  • Ming MaoM. Humphrey
  • Computer Science
    2013 IEEE 27th International Symposium on Parallel and Distributed Processing
  • 2013
This paper designs, implements and evaluates two auto-scaling solutions to minimize job turnaround time within budget constraints for cloud workflows, and shows better performance when the budget is low while the scheduling-first algorithm performs better when theudget is high.

Virtual Machine based Hybrid Auto-Scaling for Large Scale Scientific Workflows in Cloud Computing

  • V. K.S. D. Kumar
  • Computer Science
    2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)
  • 2019
Experimental results reveal the promising potential on the proposed algorithm with regard to minimization in makespan and cost of SWf under deadline and budget constraint in cloud environment.

Virtual Machine Provisioning for Applications with Multiple Deadlines in Resource-Constrained Clouds

  • R. BegamWei WangDakai Zhu
  • Computer Science
    2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
  • 2017
The evaluation results show that, compared to the single deadline schemes, the proposed VM provisioning schemes that consider multiple deadlines and distributed mapping can significantly improve the achieved system benefit and resource utilization.

Job Schedule and Cloud Auto-Scaling for Repetitive Computation

A promising multilevel queue scheduling and a set of autoscaling rules to fulfil computation deadlines and job prioritization and lower server cost is presented and an investigation to find an optimal VM size in the sense of cost and performance points out further areas of cloud service optimization.

BATS: Budget-Constrained Autoscaling for Cloud Performance Optimization

  • A. MahmudYuxiong HeShaolei Ren
  • Computer Science
    2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
  • 2015
This paper proposes a new autoscaling system, BATS, which optimizes delay performance while meeting long-term budget constraints using only past and instantaneous workload information, and demonstrates the effectiveness, scalability and robustness of BATS for reducing both average and tail latency under various workload scenarios.
...

References

SHOWING 1-10 OF 78 REFERENCES

Auto-scaling to minimize cost and meet application deadlines in cloud workflows

  • Ming MaoM. Humphrey
  • Computer Science
    2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
  • 2011
This paper presents an approach whereby the basic computing elements are virtual machines (VMs) of various sizes/costs, jobs are specified as workflows, users specify performance requirements by assigning (soft) deadlines to jobs, and the goal is to ensure all jobs are finished within their deadlines at minimum financial cost.

Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment

A novel architecture for the dynamic scaling of web applications based on thresholds in a virtualized Cloud Computing environment is described and a dynamic scaling algorithm for automated provisioning of virtual machine resources based on threshold number of active sessions will be introduced.

Time and Cost Sensitive Data-Intensive Computing on Hybrid Clouds

A modeling-driven resource allocation framework to support both time and cost sensitive execution for data-intensive applications executed in a hybrid cloud setting and results show that the system is capable of meeting execution deadlines within a 3.6% margin of error.

Cost-Optimal Scheduling in Hybrid IaaS Clouds for Deadline Constrained Workloads

This work analyzes and proposes a binary integer program formulation of the scheduling problem and finds that this approach results in a tractable solution for scheduling applications in the public cloud, but that the same method becomes much less feasible in a hybrid cloud setting due to very high solve time variances.

Cost-Effective Provisioning and Scheduling of Deadline-Constrained Applications in Hybrid Clouds

This paper presents an architecture for coordinated dynamic provisioning and scheduling that is able to cost-effectively complete applications within their deadlines by considering the whole organization workload at individual tasks level when making decisions and an accounting mechanism to determine the share of the cost of utilization of public Cloud resources to be assigned to each user.

A Performance Study on the VM Startup Time in the Cloud

  • Ming MaoM. Humphrey
  • Computer Science
    2012 IEEE Fifth International Conference on Cloud Computing
  • 2012
This paper studies the startup time of cloud VMs across three real-world cloud providers -- Amazon EC2, Windows Azure and Rackspace and analyzes the relationship between the VM startup time and different factors, such as time of the day, OS image size, instance type, data center location and the number of instances acquired at the same time.

Resource Provisioning with Budget Constraints for Adaptive Applications in Cloud Environments

The key component of the framework is a multi-input-multi-output feedback control model-based dynamic resource provisioning algorithm which adopts reinforcement learning to adjust adaptive parameters to guarantee the optimal application benefit within the time constraint.

CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.

Automated control in cloud computing: challenges and opportunities

This paper addresses the challenge of building an effective controller as a customer add-on outside of the cloud utility service itself, and explores proportional thresholding, a policy enhancement for feedback controllers that enables stable control across a wide range of guest cluster sizes using the coarse-grained control offered by popular virtual compute cloud services.

Tradeoffs Between Profit and Customer Satisfaction for Service Provisioning in the Cloud

This paper uses utility theory leveraged from economics and develops a new utility model for measuring customer satisfaction in the cloud based on the utility model, and designs a mechanism to support utility-based SLAs in order to balance the performance of applications and the cost of running them.
...