This document discusses performance evaluation strategies for multi-access edge computing (MEC). It introduces MEC and outlines its advantages and challenges, including unpredictable network traffic from user mobility. It presents a performance model called MECCA and describes deployment models for testing MEC applications in cloud and edge-cloud environments. The testing approach focuses on key metrics like response time and throughput as more clients are added. Potential use cases for MEC include autonomous vehicles, healthcare, manufacturing, and augmented reality.

1. #ATAGTR2021
Performance Evaluation
Strategy of Edge Computing
Author code – 047
Arnab Majumdar

2. Performance Evaluation Strategy of multi-access edge computing
• Introduction
• MEC- Advantages and Challenges
• Performance Model
• Deployment Model
• Testing Approach and Environment
• Conclusion
• MEC – Use

3. Introduction – multi access edge computing
• Distributed Computing system
• Brings data storage and computation
closer to data sources
• Low latency in network response
• Proportionate redistribution of computing
capability, data, and storage for micro
services

4. Advantages Security
Speed
Scalability
Versatility
Cost Effective
Reliability

5. MEC - challenges
Edge computing clouds are intrinsically local and have a smaller scale and are thus
subject to significantly larger fluctuations in offered traffic due to factors such as
correlated events and user mobility, thus making the network traffic throughput
unpredictable.
Edge computing systems by definition are distributed across multiple edge networks and
hence are associated with considerable heterogeneity in bandwidth and compute
resources.
The unique nature of the distributed edge cloud system poses key design challenges
such as specification of a control plane for distributed edge, distributed or centralized
resource assignment strategies, traffic load balancing, orchestration of computing
functions and related network routing of data, mobility management technique.

6. Performance Model
• MECCA – Mobile Edge Cloud Cornering
application
• Recommendation Service – Responsible
for recommending suitable speed for all
upcoming curves around a given location
• External Database - used to permanently
store curve results
• Detection Service - requested to detect
and calculate detailed information about
upcoming curves
• Service Registry - needed to support
services discovery for various service
instances as we have many instances from
the above-mentioned services

7. Deployment Model
Cloud-deployment model:
all services are deployed in clouds,
whereas clients are in mobile devices. We
distinguish two cases: the application
owner deploys his/her own services (i) not
in the same data center with and (ii) in the
same data center with other third party
services.
Edge-cloud deployment:
Application services are deployed
in both the edge and cloud.
Similarly, application services
might be co-located or not with
third party services.
Cloud-deployment model testing is very
useful for the design of edge-cloud
deployment because we can detect suitable
services in clouds that can be migrated to
the edge or optimized for cloud deployment.
That is the reason why in this paper we also
focus on testing cloud-deployment.

8. Test Approach and Environment
The core assumption behind
MEC testing strategy is steady
increase of throughput with
more clients in network.
• Response-time
• Result-time
• Response-ratio
• Result-ratio
Key Performance Metrics

9. Conclusion
Scalability
Transactions
throughput
Transactions
Cost
Responsivenes
s

10. Use Cases for Edge Computing
• Autonomous vehicles
• Stronger security
• Healthcare
• Manufacturing and industrial processes
• Augmented reality
• Enhanced workplace safety
• Streaming services