DeF-DReL: Systematic Deployment of Serverless Functions in Fog and Cloud
environments using Deep Reinforcement Learning
- URL: http://arxiv.org/abs/2110.15702v1
- Date: Fri, 29 Oct 2021 12:10:54 GMT
- Title: DeF-DReL: Systematic Deployment of Serverless Functions in Fog and Cloud
environments using Deep Reinforcement Learning
- Authors: Chinmaya Kumar Dehurya, Shivananda Poojaraa, Shridhar Domanalb, Satish
Narayana Srirama
- Abstract summary: Fog environment made its limited resource available to a large number of users to deploy their serverless applications.
Recent research mainly focuses on assigning maximum resources to such applications from the fog node and not taking full advantage of the cloud environment.
We propose DeF-DReL, a Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning.
- Score: 8.204696165200577
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Fog computing is introduced by shifting cloud resources towards the users'
proximity to mitigate the limitations possessed by cloud computing. Fog
environment made its limited resource available to a large number of users to
deploy their serverless applications, composed of several serverless functions.
One of the primary intentions behind introducing the fog environment is to
fulfil the demand of latency and location-sensitive serverless applications
through its limited resources. The recent research mainly focuses on assigning
maximum resources to such applications from the fog node and not taking full
advantage of the cloud environment. This introduces a negative impact in
providing the resources to a maximum number of connected users. To address this
issue, in this paper, we investigated the optimum percentage of a user's
request that should be fulfilled by fog and cloud. As a result, we proposed
DeF-DReL, a Systematic Deployment of Serverless Functions in Fog and Cloud
environments using Deep Reinforcement Learning, using several real-life
parameters, such as distance and latency of the users from nearby fog node,
user's priority, the priority of the serverless applications and their resource
demand, etc. The performance of the DeF-DReL algorithm is further compared with
recent related algorithms. From the simulation and comparison results, its
superiority over other algorithms and its applicability to the real-life
scenario can be clearly observed.
Related papers
- Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning [49.91297276176978]
We propose a novel.
Efficient Fine-Tuning (PEFT) method for point cloud, called Point GST.
Point GST freezes the pre-trained model and introduces a trainable Point Cloud Spectral Adapter (PCSA) to finetune parameters in the spectral domain.
Extensive experiments on challenging point cloud datasets demonstrate that Point GST not only outperforms its fully finetuning counterpart but also significantly reduces trainable parameters.
arXiv Detail & Related papers (2024-10-10T17:00:04Z) - SeBS-Flow: Benchmarking Serverless Cloud Function Workflows [51.4200085836966]
We propose the first serverless workflow benchmarking suite SeBS-Flow.
SeBS-Flow includes six real-world application benchmarks and four microbenchmarks representing different computational patterns.
We conduct comprehensive evaluations on three major cloud platforms, assessing performance, cost, scalability, and runtime deviations.
arXiv Detail & Related papers (2024-10-04T14:52:18Z) - SPES: Towards Optimizing Performance-Resource Trade-Off for Serverless Functions [31.01399126339857]
Serverless computing is gaining traction due to its efficiency and ability to harness on-demand cloud resources.
Existing solutions tend to use over-simplistic strategies for function pre-loading/unloading without full invocation pattern exploitation.
We propose SPES, the first differentiated scheduler for runtime cold start mitigation by optimizing serverless function provision.
arXiv Detail & Related papers (2024-03-26T10:28:41Z) - Toward a real-time TCP SYN Flood DDoS mitigation using Adaptive Neuro-Fuzzy classifier and SDN Assistance in Fog Computing [0.31318403497744784]
We propose mitigation of Fog computing-based SYN Flood DDoS attacks using an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Software Defined Networking (SDN) Assistance (FASA)
The simulation results show that FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score.
arXiv Detail & Related papers (2023-11-27T08:54:00Z) - Point Cloud Pre-training with Diffusion Models [62.12279263217138]
We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif)
PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
arXiv Detail & Related papers (2023-11-25T08:10:05Z) - Client Orchestration and Cost-Efficient Joint Optimization for
NOMA-Enabled Hierarchical Federated Learning [55.49099125128281]
We propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation.
We show that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.
arXiv Detail & Related papers (2023-11-03T13:34:44Z) - Benchmarking Function Hook Latency in Cloud-Native Environments [0.5188841610098435]
Cloud-native applications are often instrumented or altered at runtime, by dynamically patching or hooking them, which introduces a significant performance overhead.
We present recommendations to mitigate these risks and demonstrate how an improper experimental setup can negatively impact latency measurements.
arXiv Detail & Related papers (2023-10-19T12:54:32Z) - Managing Cold-start in The Serverless Cloud with Temporal Convolutional
Networks [0.0]
Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties.
A big threat to the serverless cloud's performance is cold-start, which is when the time of provisioning the needed cloud resource to serve customers' requests incurs unacceptable costs to the service providers and/or the customers.
This paper proposes a novel low-coupling, high-cohesion ensemble policy that addresses the cold-start problem at infrastructure- and function-levels of the serverless cloud stack.
arXiv Detail & Related papers (2023-04-01T21:54:22Z) - Device-Cloud Collaborative Recommendation via Meta Controller [65.97416287295152]
We propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender.
On the basis of the counterfactual samples and the extended training, extensive experiments in the industrial recommendation scenarios show the promise of meta controller.
arXiv Detail & Related papers (2022-07-07T03:23:04Z) - Computation Offloading and Resource Allocation in F-RANs: A Federated
Deep Reinforcement Learning Approach [67.06539298956854]
fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs)
arXiv Detail & Related papers (2022-06-13T02:19:20Z) - Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with
Rank Reordering [8.81194405760133]
Cloud Collectives is a prototype that accelerates collectives by reorderingranks of participating frameworks.
Collectives is non-intrusive, requires no code changes nor rebuild of an existing application, and runs without support from cloud providers.
Preliminary application of Cloud Collectives on allreduce operations in public clouds results in a speedup of up to 3.7x in multiple microbenchmarks and 1.3x in real-world workloads.
arXiv Detail & Related papers (2021-05-28T20:14:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.