4.7 Article

Exploiting Heterogeneity for Opportunistic Resource Scaling in Cloud-Hosted Applications

Journal

IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 14, Issue 6, Pages 1739-1750

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TSC.2019.2908647

Keywords

Resource management; Contracts; Optimization; Cloud computing; Pricing; Internet of Things; Australia; Cost optimization; resource heterogeneity; contract heterogeneity; Amazon EC2; cloud-based applications

Funding

  1. ARC-Linkage Project [LP150100846]
  2. Australian Research Council
  3. Australian Research Council [LP150100846] Funding Source: Australian Research Council

Ask authors/readers for more resources

This paper introduces a novel opportunistic resource scaling approach that leverages resource and contract heterogeneity for cost-effective resource allocations. The results show that this approach, especially full capacity optimization, outperforms traditional scaling methods and offers significant cost savings.
Cloud consumers have access to an increasingly diverse range of resource and contract options, but lack appropriate resource scaling solutions that can exploit this to minimize the cost of their cloud-hosted applications. Traditional approaches tend to use homogeneous resources and horizontal scaling to handle workload fluctuations and do not leverage resource and contract heterogeneity to optimize cloud costs. In this paper, we propose a novel opportunistic resource scaling approach that exploits both resource and contract heterogeneity to achieve cost-effective resource allocations. We model resource allocation as an unbounded knapsack problem, and resource scaling as an one-step ahead resource allocation problem. Based on these models, we propose two scaling strategies: (a) delta capacity optimization, which focuses on optimizing costs for the difference between existing resource allocation and the required capacity based on the forecast workload, and (b) full capacity optimization, which focuses on optimizing costs for resource capacity corresponding to the forecast workload. We evaluate both strategies using two real world workload datasets, and compare them against three different scaling strategies. The results show that our proposed approach, particularly full capacity optimization, outperforms all of them and offers in excess of 70 percent cost savings compared to the traditional scaling approach.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available