A Deep Reinforcement Learning Approach for Cost Optimized Workflow Scheduling in Cloud Computing Environments
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910557600219136 |
|---|---|
| author | Jayanetti, Amanda Halgamuge, Saman Buyya, Rajkumar |
| author_facet | Jayanetti, Amanda Halgamuge, Saman Buyya, Rajkumar |
| contents | Cost optimization is a common goal of workflow schedulers operating in cloud computing environments. The use of spot instances is a potential means of achieving this goal, as they are offered by cloud providers at discounted prices compared to their on-demand counterparts in exchange for reduced reliability. This is due to the fact that spot instances are subjected to interruptions when spare computing capacity used for provisioning them is needed back owing to demand variations. Also, the prices of spot instances are not fixed as pricing is dependent on long term supply and demand. The possibility of interruptions and pricing variations associated with spot instances adds a layer of uncertainty to the general problem of workflow scheduling across cloud computing environments. These challenges need to be efficiently addressed for enjoying the cost savings achievable with the use of spot instances without compromising the underlying business requirements. To this end, in this paper we use Deep Reinforcement Learning for developing an autonomous agent capable of scheduling workflows in a cost efficient manner by using an intelligent mix of spot and on-demand instances. The proposed solution is implemented in the open source container native Argo workflow engine that is widely used for executing industrial workflows. The results of the experiments demonstrate that the proposed scheduling method is capable of outperforming the current benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02926 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Deep Reinforcement Learning Approach for Cost Optimized Workflow Scheduling in Cloud Computing Environments Jayanetti, Amanda Halgamuge, Saman Buyya, Rajkumar Distributed, Parallel, and Cluster Computing Cost optimization is a common goal of workflow schedulers operating in cloud computing environments. The use of spot instances is a potential means of achieving this goal, as they are offered by cloud providers at discounted prices compared to their on-demand counterparts in exchange for reduced reliability. This is due to the fact that spot instances are subjected to interruptions when spare computing capacity used for provisioning them is needed back owing to demand variations. Also, the prices of spot instances are not fixed as pricing is dependent on long term supply and demand. The possibility of interruptions and pricing variations associated with spot instances adds a layer of uncertainty to the general problem of workflow scheduling across cloud computing environments. These challenges need to be efficiently addressed for enjoying the cost savings achievable with the use of spot instances without compromising the underlying business requirements. To this end, in this paper we use Deep Reinforcement Learning for developing an autonomous agent capable of scheduling workflows in a cost efficient manner by using an intelligent mix of spot and on-demand instances. The proposed solution is implemented in the open source container native Argo workflow engine that is widely used for executing industrial workflows. The results of the experiments demonstrate that the proposed scheduling method is capable of outperforming the current benchmarks. |
| title | A Deep Reinforcement Learning Approach for Cost Optimized Workflow Scheduling in Cloud Computing Environments |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2408.02926 |