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

@article{Mao2011AutoscalingTM,
  title={Auto-scaling to minimize cost and meet application deadlines in cloud workflows},
  author={Ming Mao and Marty Humphrey},
  journal={2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC)},
  year={2011},
  pages={1-12}
}
  • Ming MaoM. Humphrey
  • Published 12 November 2011
  • Computer Science
  • 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
A goal in cloud computing is to allocate (and thus pay for) only those cloud resources that are truly needed. [] Key Result We evaluate our approach in four representative cloud workload patterns and show cost savings from 9.8% to 40.4% compared to other approaches.

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...

References

SHOWING 1-10 OF 38 REFERENCES

Cloud auto-scaling with deadline and budget constraints

This paper presents a cloud auto-scaling mechanism to automatically scale computing instances based on workload information and performance desire, and demonstrates that it can meet user specified performance goal with less cost.

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.

Bag-of-Tasks Scheduling under Budget Constraints

  • Ana OprescuT. Kielmann
  • Computer Science
    2010 IEEE Second International Conference on Cloud Computing Technology and Science
  • 2010
The results show that BaTS is able to schedule within a user-defined budget (if such a schedule is possible at all), and significant cost savings can be achieved when comparing to a cost-oblivious round-robin scheduler.

Optimal Resource Allocation in Clouds

An approximation algorithm with a proof of its approximation bound that can yield close to optimum solutions in polynomial time is presented and it is shown that finding a minimized allocation is NP-complete.

Cost-based scheduling of scientific workflow applications on utility grids

  • Jia YuR. BuyyaC. Tham
  • Computer Science
    First International Conference on e-Science and Grid Computing (e-Science'05)
  • 2005
This paper proposes a cost-based workflow scheduling algorithm that minimizes execution cost while meeting the deadline for delivering results and attempts to optimally solve the task scheduling problem in branches with several sequential tasks by modeling the branch as a Markov decision process and using the value iteration method.

Scheduling Workflows with Budget Constraints

This paper considers a basic model for workflow applications modelled as Directed Acyclic Graphs (DAGs) and investigates heuristics that allow to schedule the nodes of the DAG (or tasks of a workflow) onto resources in a way that satisfies a budget constraint and is still optimized for overall time.

A budget constrained scheduling of workflow applications on utility Grids using genetic algorithms

  • Jia YuR. Buyya
  • Computer Science
    2006 Workshop on Workflows in Support of Large-Scale Science
  • 2006
A new type of genetic algorithm is developed to solve the scheduling optimization problem and it is tested in a simulated grid testbed, which minimizes execution time while meeting a specified budget for delivering results.

Adaptive control of virtualized resources in utility computing environments

An adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level quality of service (QoS) goals while achieving high resource utilization in the data center is developed.

Dynamic Resource Allocation in Computing Clouds Using Distributed Multiple Criteria Decision Analysis

A distributed architecture where resource management is decomposed into independent tasks, each of which is performed by Autonomous Node Agents that are tightly coupled with the physical machines in a data center is adopted.

Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms

A genetic algorithm approach is presented to address scheduling optimization problems in workflow applications, based on two QoS constraints, deadline and budget, which are presented in this paper.