17970023. POWER AND ENERGY OPTIMIZATION ACROSS DISTRIBUTED CLOUD ENVIRONMENT simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

POWER AND ENERGY OPTIMIZATION ACROSS DISTRIBUTED CLOUD ENVIRONMENT

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Mathews Thomas of Flower Mound TX (US)

Utpal Mangla of Toronto (CA)

Sai Srinivas Gorti of Irving TX (US)

Sharath Prasad Krishna Prasad of Flower Mound TX (US)

Venkatesh Ashok Rao Rao of Natick MA (US)

Praveen Jayachandran of Bangalore (IN)

Eric Lee Gose of Dallas TX (US)

Juel Daniel Raju of Garland TX (US)

Amandeep Singh of Carrollton TX (US)

POWER AND ENERGY OPTIMIZATION ACROSS DISTRIBUTED CLOUD ENVIRONMENT - A simplified explanation of the abstract

This abstract first appeared for US patent application 17970023 titled 'POWER AND ENERGY OPTIMIZATION ACROSS DISTRIBUTED CLOUD ENVIRONMENT

Simplified Explanation

The approach for managing workload deployment in a distributed network, including edge computing, involves deploying modules like EMM, LDM, and EDM to monitor and manage energy consumption at edge nodes and develop an energy management system.

• EMM (Energy Management Module), LDM (Localized Deployment Manager), and EDM (Edge Deployment Manager) are deployed to monitor and manage energy consumption at edge nodes.
• These modules communicate with each other to develop a holistic energy management system, including energy policies, algorithms, and plans.
• The goal is to ensure effective energy management of workloads in the distributed network.

Potential Applications

This technology can be applied in various industries such as telecommunications, IoT, smart cities, and industrial automation for efficient energy management in distributed networks.

Problems Solved

1. Inefficient energy management in distributed networks. 2. Lack of coordination and communication between edge nodes for workload deployment.

Benefits

1. Improved energy efficiency. 2. Enhanced performance of edge computing systems. 3. Cost savings through optimized energy management.

Potential Commercial Applications

Optimizing energy management in telecommunications networks for cost savings and improved performance.

Possible Prior Art

One possible prior art could be the use of centralized energy management systems in distributed networks, which may not be as efficient or effective as the proposed approach.

Question 1:

How does the approach ensure real-time monitoring and management of energy consumption at edge nodes?

Answer:

The modules EMM, LDM, and EDM are designed to constantly monitor energy consumption at edge nodes and communicate with each other in real-time to develop and implement energy management strategies.

Question 2:

What are the specific energy policies and algorithms implemented in the energy management system?

Answer:

The specific energy policies and algorithms implemented may vary depending on the requirements of the distributed network, but they are designed to optimize energy consumption and workload deployment for maximum efficiency.

Original Abstract Submitted

An approach for managing workload deployment in a distributed network, including edge computing is provided. The approach includes deploying several modules, such as, EMM (energy management module), LDM (localized deployment manager) and EDM (edge deployment manager). These modules will be constantly monitoring and managing the energy consumption at the edge nodes under their purview and communicate with other modules to develop a holistic energy management system (e.g., energy policies, energy algorithms, energy plans, etc.) to ensure the most effective energy management of workload is implemented.