RAW23-Reza.pdf

Unveiling the Potential of the Edge-Cloud Continuum:
A Deep Dive into Video Streaming and Graph Processing Use Cases
Reza Farahani
Institute for Information Technology (ITEC),University of Klagenfurt,Austria
reza.farahani@aau.at
28.08.2023
The 2nd International Workshop on Resource Awareness of Systems and Society (RAW23),Limassol,Cyprus

Agenda
○ About me
○ Edge-Cloud Continuum
○ Network-Assisted Video Streaming Frameworks
■ SARENA Framework
■ ARARAT Framework
○ Towards Serverless Sustainable Graph Processing on the Computing Continuum
○ Conclusion
R. Farahani: Unveiling the Potential of the Edge-Cloud Continuum … | RAW Workshop | Limassol, Cyprus | 28.08.2023 2

About me
Reza Farahani
● Postdoc Researcher, ITEC, University of Klagenfurt, Austria
Education::
● 2023: Ph.D. of in Information Technology Engineering, University of Klagenfurt, Austria
● 2019: M.Sc. in Computer Engineering University of Tehran, IRAN
● 2014: B.Sc. in Computer Hardware Engineering, University of Isfahan, IRAN
Previous Positions:
● 2019-2023: Research Assistant, ATHENA CDG laboratory, University of Klagenfurt, Austria
● Nov 2022- Jan 2023: Visiting Researcher, 5G&6G Center, University of Surrey, UK
● 2016-2019: Research Assistant, RouterLab laboratory, University of Tehran, IRAN
● 2015-2019: Network Engineer and Programmer, ISP and MNP Companies, IRAN
● 2016-2019: Cisco R&S, SP Instructor
Project Experience:
● H2020 Graph-Massivizer
● Christian Doppler ATHENA
Publications:
● Over 20 publications in journals and conferences

R. Farahani: Unveiling the Potential of the Edge-Cloud Continuum … | RAW Workshop | Limassol, Cyprus | 28.08.2023
Edge-Cloud Continuum
4

Network-Assisted Video
Streaming Frameworks
1
5

Video Traffic
6
● Video is dominating today’s Internet traffic
○ Video streaming includes 66% of the total video Internet traffic [1]
○ Video-on-Demand (VoD) and live streaming have become
significantly popular video streaming applications
○ Live streaming will increase 15-fold and reach 17% of Internet video
traffic [2]
[1] Sandvine, “The Global Internet Phenomena Report,” White Paper, 2023. https://www.sandvine.com/global-internet-phenomena-report-2023
[2] Cisco Visual Networking Index (VNI), Forecast and Trends, 2018– 2023. White Paper, 2018.
https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.pdf

Video Downloading vs.Streaming
7

Video Downloading vs.Streaming
8

Content Delivery Network (CDN)
9

HTTPAdaptive Video Streaming (HAS)
1
2
3
6
4
5 5
6
5
6 7
Encoder
Origin
Packager
CDNs
HTTP Res
HTTP Req
Reza Shokri Kalan, Reza Farahani, Emre Karsli, Christian Timmerer, and Hermann Hellwagner. Towards Low Latency Live Streaming: Challenges in a Real-world Deployment. 13th ACM MMSys, 2022.
Q
uality
Bandwidth
Time
Time

Network-Assisted Video Streaming (NAVS)
11
Network Assistance by HAS
Network Assistance for HAS
Network
Media Server

SARENA Framework
1.1
12
R. Farahani, et al. "SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications", IEEE International Conference
on Communications (ICC), 2023.

Motivation
● OTT video
● Live video streaming
● Immersive multimedia
● Video Gaming
● Video analytics for security,
quality assurance, etc.
Increase in amount of video
generated and transported
13

Motivation
● OTT video
● Live video streaming
● Immersive multimedia
● Video Gaming
● Video analytics for security,
quality assurance, etc.
Increase in amount of video
generated and transported
Versatile QoE, QoS requirements
Resolution (4K, 8K)
Latency (LL,ULL)
Bitrate
13

Research Question
● How to leverage modern networking/computing paradigms to serve different MSs
requests with acceptable QoE and improved network utilization?
● How to design a network-assisted HAS scheme without client-side modiﬁcation ?
M
E
C
S
F
C
HAS
SDN
14

Software Deﬁned Networking (SDN)
Data Plane
Control Plane
15

Software Deﬁned Networking (SDN)
Data Plane
Control
Plane
https://opennetworking.org/sdn-definition/
15

Network Function Virtualization (NFV)
16

Service Function Chaining (SFC)
VNF i VNF i+1 VNF n
VNF i VNF i+1 VNF n
SFC
Chains
Chain
1
Chain
m
…
…
…
17

Service Function Chaining (SFC)
VNF i VNF i+1 VNF n
VNF i VNF i+1 VNF n
SFC
Chains
Chain
1
Chain
m
…
…
…
Orchestration
Placement
Scheduling
SFC
Definition
VNF
Definition
17

SARENAArchitecture
Virtual Proxy Function
Virtual Cache Function
Virtual Transcoding Function
CDN Cache
Origin Cache
1
2
3
4
5
Multimedia
VNFs
18

SARENAArchitecture
3
1
2
5
Multimedia
SFCs
1
2
4
1
1
4
1 3
19

SARENA Optimization Model
● The Requests Scheduler run an MILP optimization model to respond:
○ Where is the optimal place for fetching the content quality level requested by
each client, while efﬁciently employing layers’ available resources and satisfying
service requirements (e.g., service deadlines)?
○ How can we use the functions/services provided in the EL and IL layers to
form MS function chains (SFCs)?
○ What is the optimal SFC for responding to the requested quality level with
speciﬁc service requirements?
Transmitting Time
Transcoding Time
Total MSs Serving Latency
20

SARENA Heuristic Solution
Virtual Scheduler Function
21

Evaluation Setup
22
A cloud-based testbed, including 280 elements and real backbone topology (InternetMCI)
○ Xen virtual machines
○ 250 Dash player
○ Four Apache cache servers and an origin server
○ 19 backbone switches and 45 layer-2 links
○ Five edge server
○ Floodlight SDN controller
○ BOLA ABR algorithms
○ FFmpeg transcoders
○ LRU cache replacement policy
○ Zipf distribution is used for video and channel access popularity

Evaluation Setup
23
0.089
320
480
720
108
0
108
0
0.262
0.791
2.4
4.2
Resolution (p) Bitrate (Mbps) Bitrate (Mbps)
Resolution (p)
20
VoDs,
300
sec.
duration,
4
sec.
segments
320
480
720
720
1080
1080
1080
0.128
0.32
0.78
1.4
2.4
3.3
3.9
5
live
ch,
300
sec.
duration,
2
sec.
segments

Evaluation Method and Metrics
24
✔ Baseline systems:
◆ CDN-assisted (CDA)
◆ Non VNF-assisted (NVA)
◆ Non VTF-enabled (NTE)
◆ Non Reconﬁguration-enabled (NRE)
✔ The performance of the aforementioned approaches is evaluated through
◆ ASB: Average Segment Bitrate
◆ AQS: Average Number of Quality Switches
◆ ANS: Average Number of Stalls
◆ ASD: Average Stall Duration
◆ APQ: Average Perceived QoE calculated by ITU-T P.1203 mode 0 [1]
◆ ASL: Overall time for serving
◆ NCV: Network Cost Value
◆ ETR: Edge/P2P Transcoding Ratio
◆ BTL: Backhaul Trafﬁc Load
[1] https://www.itu.int/net4/ipr/details_ps.aspx?sector=ITU-T&id=P1203-01

SARENA Results
25
ASB: Average Segment Bitrate
AQS: Average Number of Quality Switches
ANS: Average Number of Stalls
ASD: Average Stall Duration
APQ: Average Perceived QoE calculated by ITU-T
P.1203 mode 0 [1]
ASL: Average Serving Latency

SARENA Results
26
NCV: Network Cost Value
ETR: Edge Transcoding Ratio
BTL: Backhaul Trafﬁc Load

ARARAT Framework
27
R. Farahani, et al., "ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming", IEEE Transactions on
Network and Service Management (TNSM), 2022.
1.2

Motivation
✔ Establish a collaboration between edge servers to use their potential idle
resources for serving HAS clients.
M
E
C
SDN
N
F
V
H
A
S
28

ARARATARCHITECTURE
✔ MEC Servers:
◆ Local Edge Server (LES)
◆ Neighboring Edge Server (NES)
✔ Virtualized Edge Functions:
◆ Partial Cache (PC)
◆ Video Transcoder (Tran)
Edge Layer
29

ARARATARCHITECTURE
30

ARARATARCHITECTURE
cache_map
Requests, edge_map, comp_map
31

ARARATAction Tree
32

ARARAT Optimization Model
● The SDN controller run an MILP optimization model to respond:
○ Where is the optimal place (i.e., LES, NESs, CSs, or the origin server) in terms of the
following items for fetching each client’s requested content quality level from?
■ minimum serving time and minimum network cost
○ What is the optimal approach for responding to the requested quality level (i.e., fetch
or transcode)?
Transmission Time
Transcoding Time
Serving Time
Network Cost
Computational Cost
Bandwidth Cost
33

ARARAT Coarse-Grained (CG) Heuristic
COM → LOM
34

ARARAT Fine-Grained I (FG I) Heuristic
LOM → EFG I
35

ARARAT Fine-Grained I (FG I) Heuristic
LOM → EFG I
Network bandwidth allocation?
35

ARARAT Fine-Grained II (FG II) Heuristic
LOM → EFG I
36

ARARAT Fine-Grained II (FG II) Heuristic
The bandwidth allocation is modeled as a “Fairness LP Optimization Model”
37

Evaluation Setup
38
● A cloud-based testbed, including 301 elements (Xen virtual machines) and real backbone topology
(Geant and Abilene)

Evaluation Methods
39
✔ SABR (https://doi.org/10.1145/3083187.3083196) :
◆ Non edge-enabled system
✔ ES-HAS (https://doi.org/10.1145/3458306.3460997) :
◆ Non edge-collaborative and transcoding-enabled system
◆ Each edge runs an MILP model on the collected client requests to serve them via one of actions
1, 7, or 9
✔ CSDN (10.1109/LCN52139.2021.9524970) :
◆ Non-collaborative approach
◆ Each edge server runs an MILP model for the collected client requests and serves them
separately via one of the actions 1, 2, 7, 8 or 9
✔ NECOL
◆ The Non Edge Collaborative (NECOL) system does not support an edge collaboration.
◆ Each NECOL edge server executes a simpliﬁed version of the proposed FG I heuristic for
◆ each client request to serve it through one of the actions 1, 2, 7, 8 or 9

ARARAT Results
40
ASB: Average Segment Bitrate
AQS: Average Number of Quality Switches
ANS: Average Number of Stalls
ASD: Average Stall Duration
APQ: Average Perceived QoE calculated by
ITU-T P.1203 mode 0 [1]

ARARAT Results
41
CHR: Cache Hit Ratio
ETR: Edge Transcoding Ratio
BTL: Backhaul Trafﬁc Load
ANU: Average Network Utilization
ASL: Average Serving Time
NCV: Network Cost Value
ANC: Average Number of Communicated messages
from/to the SDN controller

Towards Serverless Sustainable Graph Processing
on the Computing Continuum
2
42
R. Farahani, et al., "Towards Sustainable Serverless Processing of Massive Graphs on the Computing Continuum
", The 1st Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems (GraphSys), co-located with ICPE 2023.
Reza Farahani, Sashko Rostov, Radu Prodan, "Designing A Sustainable Serverless Graph Processing Tool on the Computing
Continuum", Demo & Poster track, Euro-Par 2023.
Sashko Rostov, Reza Farahani, Radu Prodan, "Large-scale Graph Processing and Simulation with Serverless Workflows in
Federated FaaS", The 1st Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems (GraphSys), co-located with ICPE
2023.

Motivation
● Graph processing has become increasingly popular and essential for
solving complex problems in various domains.
● Processing graphs at a massive scale poses critical challenges, such as
inefﬁcient resource and energy utilization.
43

Graph-Massivizer Project
● Graph-Massivizer project
○ Extreme and Sustainable Graph Processing for Urgent
Societal Challenges in Europe
○ https://graph-massivizer.eu/
● Objective
○ Deliver a high-performance, scalable, and sustainable
platform for information processing and reasoning
based on massive graph representation of extreme data
44

Graph-Massivizer Architecture
45

Serverless Computing
● Serverless computing is becoming the de-facto technology to transform the future of
computing
○ Lower Cost
46

computing
○ Lower Cost
○ Simpliﬁed Backend Code
46

computing
○ Lower Cost
○ Increased Productivity
46

computing
○ Lower Cost
○ Increased Productivity and Scalability
46

● Public cloud providers serverlessize their services:
○ Serverless Containers (AWS Fargate)
○ Serverless Databases (AWS DynamoDB)
○ Function-as-a-Service (AWS Lambda)
○ Serverless Graph Processing (AWS Neptune)
47

Serverless Graph Processing
● Serverless functions are more powerful if they are orchestrated in a serverless workﬂows
or Function Choreographies (FC)
● Directed Acyclic Graph (DAG) is one way to represent the workﬂow
○
48

○
● Serverless workﬂow management systems offer limited support for graph processing
48

○
● Serverless workﬂow management systems offer limited support for graph processing
● Graph processing platforms support libraries for graph processing but with limited
resource utilization
48

Research Gaps
● Graphless
○ Limited to AWS Lambda (e.g., 1000 functions)
● SeBS Benchmakr
○ Uses serverless functions for PageRank
● OrcBench
○ Mimic function execution (CPU and memory usage simulation)
● Challenge: FC systems and sustainability?
49

Research Questions
1. How to design a scalable FC management system for graph processing!?
2. How can a serverless computing continuum system be designed to
support a broad class of graph algorithms?
3. How can a serverless computing continuum system be devised to
minimize energy consumption and carbon footprint while maximizing
performance and scalability?
4. How can we devise a serverless simulator to consider cost, performance,
and sustainability?
50

Graph-Choreographer Tool
51

The role of Edge-Cloud Continuum!?
51

Conclusions Edge-Cloud Continuum
UC1: Video Streaming UC2: Graph Processing
Network-Assisted
Frameworks for HAS
FC Management systems Graph Processing Platforms
Graph-Massivizer Project
SARENA ARARAT
Edge Collaborative
Single Edge

Publications
54
1. R. Farahani, E. Cetinkaya, C. Timmerer, M. Shojafar, M. Ghanbari, H. Hellwagner. ALIVE: A Latency- and Cost-Aware Hybrid P2P-CDN
Framework for Live Video Streaming. Submitted to IEEE Transactions on Network and Service Management (TNSM), 2023.
2. R. Farahani, C. Timmerer, H. Hellwagner. Towards Low-Latency and Energy-Efficient Hybrid P2P-CDN Live Video Streaming.
Submitted to IEEE COMSOC MMTC Communication-Frontiers, 2023.
3. R. Farahani, M. Shojafar, C. Timmerer, F. Tashtarian, M. Ghanbari, H. Hellwagner. ARARAT: A Collaborative Edge-Assisted Framework
for HTTP Adaptive Video Streaming. IEEE Transactions on Network and Service Management (TNSM), 2022.
4. R. Farahani, A. Bentaleb, C. Timmerer, M. Shojafar, R. Prodan, H. Hellwagner. SARENA: SFC-Enabled Architecture for Adaptive Video
Streaming Applications. IEEE International Conference on Communications (ICC), 2022.
5. R. Farahani, A. Bentaleb, E. Cetinkaya, C. Timmerer, R. Zimmermann, H. Hellwagner. Hybrid P2P-CDN Architecture for Live Video
Streaming: An Online Learning Approach. IEEE Global Communications Conference (GLOBECOM), 2022.
6. R. Farahani, F. Tashtarian, C. Timmerer, M. Ghanbari, H. Hellwagner. LEADER: A Collaborative Edge- and SDN-Assisted Framework for
HTTP Adaptive Video. IEEE International Conference on Communications (ICC), 2022.
7. R. Farahani, F. Tashtarian, H. Amirpour, C. Timmerer, M. Ghanbari, H. Hellwagner. CSDN: CDN-Aware QoE Optimization in SDN-Assisted
HTTP Adaptive Video Streaming. 46th IEEE Conference on Local Computer Networks (LCN), 2021.
8. R. Farahani, F. Tashtarian, A. Erfanian, C. Timmerer, M. Ghanbari, H. Hellwagner. ES-HAS: an edge- and SDN-assisted framework for
HTTP adaptive video streaming. 31st ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV),
2021.
9. R. Farahani. CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming. 12th ACM Multimedia Systems
Conference (MMSys), 2021.
10. R. Farahani, A. Bentaleb, M. Shojafar, H. Hellwagner. CP-Steering: CDN- and Protocol-Aware Content Steering Solution for HTTP
Adaptive Video Streaming. ACM Mile High Video (MHV), 2023.

11. R. Farahani, H. Amirpour, F. Tashtarian, A.Bentaleb, C. Timmerer, H. Hellwagner, R. Zimmermann. RICHTER: Hybrid P2P-CDN Architecture
for Low Latency Live Video Streaming. ACM Mile-High Video (MHV), 2022.
12. R Farahani, D Kimovski, S Ristov, A Iosup, R Prodan. Towards Sustainable Serverless Processing of Massive Graphs on the Computing
Continuum. The 1st Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems (GraphSys), co-located with
ICPE, 2023.
13. R Farahani, S Ristov, R Prodan. Designing A Sustainable Serverless Graph Processing Tool on the Computing Continuum. Accepted
as a poster at Euro-Par conference, 2023.
14. S Ristov, R Farahani, R Prodan. Large-scale Graph Processing and Simulation with Serverless Workflows in Federated FaaS. The
1st Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems (GraphSys), co-located with ICPE, 2023.
15. V. V Menon, R. Farahani, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Reduced Reference Transcoding Quality
Prediction for Video Streaming Applications. ACM Mile High Video (MHV), 2023.
16. V. V Menon, P. T Rajendran, R. Farahani, K. Schöffmann, C. Timmerer. Video Quality Assessment with Texture Information Fusion
for Streaming Applications. Submitted to the IEEE International Conference on Visual Communications and Image Processing (VCIP),
2023.
17. V. V Menon, R. Farahani, P. T Rajendran, S. Afzal, K. Schöffmann, C. Timmerer. Energy-Efficient Multi-Codec Bitrate-Ladder
Estimation for Adaptive Video Streaming. Submitted to the IEEE International Conference on Visual Communications and Image
Processing (VCIP), 2023.
18. S. Chellappa, R. Farahani, R. Bartos, H. Hellwagner. Context-Aware HTTP Adaptive Video Streaming Utilizing QUIC’s Stream Priority.
ACM Mile High Video (MHV), 2023.
55
Publications
Publications

56
19. A. Bentaleb, R. Farahani, F. Tashtarian, H. Hellwagner, R.Zimmermann. Which CDN to Download From? A Client and Server
Strategies. ACM Mile High Video (MHV), 2023.
20. R. Shokri Kalan, R. Farahani, E. Karsli, C. Timmerer, H. Hellwagner. Towards Low Latency Live Streaming: Challenges in
Real-World Deployment. The 13th ACM Multimedia Systems Conference (MMSys), 2022.
21. F. Tashtarian, A. Bentaleb, R. Farahani, M. Nguyen, C. Timmerer, H.Hellwagner, R. Zimmermann. A Distributed Delivery Architecture
for User Generated Content Live Streaming over HTTP. IEEE 46th Conference on Local Computer Networks (LCN), 2021.
22. A. Erfanian, F. Tashtarian, R. Farahani, C. Timmerer, H. Hellwagner. On Optimizing Resource Utilization in AVC-based Real-time
Video Streaming. The 6th IEEE Conference on Network Softwarization (NetSoft), 2020.
Publications

Thank you
reza.farahani@aau.at
https://www.linkedin.com/in/reza-farahani/

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