Advanced Computation Capacity Modeling for Delay-Constrained Placement of IoT Services

Area of Science:

• Computer Science
• Telecommunications Engineering
• Network Infrastructure

Background:

• The Internet of Things (IoT) generates vast data, requiring integrated services for analysis and value extraction.
• Virtualized 5G networks are crucial for the scale and dynamism needed by IoT applications.
• Existing service placement algorithms struggle to model evolving computation and communication infrastructures, particularly resource abstraction and dynamic features.

Purpose of the Study:

• To extend current IoT service placement algorithms to accommodate the rapid evolution of 5G infrastructure.
• To maintain critical attributes like end-to-end delay constraints and cost minimization objectives.
• To provide a theoretical foundation for resource abstraction, elasticity, and delay constraints in 5G service placement.

Main Methods:

• Developing an extended framework for IoT service placement algorithms.
• Proposing efficient solutions for aggregating computation resource capacities.
• Implementing behavior prediction for dynamic Kubernetes infrastructure within a delay-constrained service embedding context.
• Providing mathematical theorems and detailed proofs to support the proposed solutions.

Main Results:

• Successfully adapted IoT service placement algorithms to keep pace with 5G infrastructure advancements.
• Enabled effective aggregation of computation resources and prediction of dynamic infrastructure behavior.
• Demonstrated solutions within a delay-constrained service embedding framework, adhering to cost minimization and delay constraints.

Conclusions:

• The proposed extensions enhance IoT service placement algorithms, enabling them to effectively manage dynamic 5G network infrastructures.
• The theoretical foundation and efficient solutions address key challenges in resource abstraction, elasticity, and delay-constrained service embedding.
• This work facilitates the exploitation of IoT data by aligning service placement strategies with modern network capabilities.