View source for Tata Consultancy Services Limited (20240235961). SERVICE-LEVEL OBJECTIVE (SLO) AWARE EXECUTION OF CONCURRENCE INFERENCE REQUESTS ON A FOG-CLOUD NETWORK simplified abstract

=SERVICE-LEVEL OBJECTIVE (SLO) AWARE EXECUTION OF CONCURRENCE INFERENCE REQUESTS ON A FOG-CLOUD NETWORK=


==Organization Name==

[[:Category:Tata Consultancy Services Limited|Tata Consultancy Services Limited]]


[[Category:Tata Consultancy Services Limited]]

==Inventor(s)==

[[:Category:CHETAN DNYANDEO Phalak of Mumbai (IN)|CHETAN DNYANDEO Phalak of Mumbai (IN)]][[Category:CHETAN DNYANDEO Phalak of Mumbai (IN)]]

[[:Category:DHEERAJ Chahal of Pune (IN)|DHEERAJ Chahal of Pune (IN)]][[Category:DHEERAJ Chahal of Pune (IN)]]

[[:Category:REKHA Singhal of Thane West (IN)|REKHA Singhal of Thane West (IN)]][[Category:REKHA Singhal of Thane West (IN)]]

==SERVICE-LEVEL OBJECTIVE (SLO) AWARE EXECUTION OF CONCURRENCE INFERENCE REQUESTS ON A FOG-CLOUD NETWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240235961 titled 'SERVICE-LEVEL OBJECTIVE (SLO) AWARE EXECUTION OF CONCURRENCE INFERENCE REQUESTS ON A FOG-CLOUD NETWORK

The abstract discusses the use of cloud and fog computing for IoT applications, focusing on managing machine learning inference requests efficiently to minimize costs and avoid SLO violations.

* Fog and cloud computing are essential for complex IoT deployments.
* The volume of data generated by internet-connected devices has increased significantly.
* Managing data and workloads for real-time predictive decisions using fog computing is challenging.
* The patent provides systems and methods for automating the execution of ML/DL inference requests using fog and various cloud instances.
* The generated workflow helps reduce deployment costs and prevents SLO violations.

**Potential Applications:**
This technology can be applied in various industries such as healthcare, manufacturing, smart cities, and agriculture for real-time data processing and decision-making.

**Problems Solved:**
The technology addresses the challenge of efficiently managing machine learning inference requests in fog computing environments to minimize costs and prevent SLO violations.

**Benefits:**
The technology helps optimize the execution of ML/DL inference requests, reducing deployment costs and ensuring efficient real-time decision-making without violating service-level objectives.

**Commercial Applications:**
Optimizing machine learning inference requests in fog computing environments can benefit companies in industries such as logistics, retail, and telecommunications by improving data processing speed and accuracy.

**Prior Art:**
Prior research in fog and cloud computing, as well as machine learning optimization, can provide valuable insights into similar technologies and approaches.

**Frequently Updated Research:**
Stay updated on the latest advancements in fog computing, cloud technologies, and machine learning optimization to enhance the efficiency and effectiveness of this technology.

**Questions about Fog and Cloud Computing:**
1. How does fog computing differ from cloud computing in managing IoT applications?
2. What are the key challenges in optimizing machine learning inference requests in fog computing environments?


==Original Abstract Submitted==

cloud and fog computing are complementary technologies used for complex internet of things (iot) based deployment of applications. with an increase in the number of internet-connected devices, the volume of data generated and processed at higher speeds has increased substantially. serving a large amount of data and workloads for predictive decisions in real-time using fog computing without service-level objective (slo) violation is a challenge. present disclosure provides systems and method for inference management wherein a suitable execution workflow is automatically generated to execute machine learning (ml)/deep learning (dl) inference requests using fog with various type of instances (e.g., function-as-a-service (faas) instance, machine learning-as-a-service (mlaas) instance, and the like) provided by cloud vendors/platforms. generated workflow minimizes the cost of deployment as well as slo violations.



[[Category:H04L41/5009]]
[[Category:G06F9/50]]
[[Category:CPC_H04L41/5009]]