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Created January 25, 2026 16:47
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Summary of the edge Paper.md

Summary of the Paper

This paper, authored by Siavash Alamouti, investigates how Hybrid Edge Cloud (HEC) can address workload distribution challenges in centralized cloud systems .

Problem Addressed

Cloud workloads often follow a Pareto distribution, meaning a small percentage of tasks consume the majority of resources . This creates bottlenecks and energy inefficiencies in traditional centralized cloud architectures .

Workloads Analyzed

The study examines two types of workloads:

  • Traditional workloads – typical IoT and smart device usage patterns
  • Agentic workloads – tasks generated by AI agents, robotics, and autonomous systems

Key Findings

The research demonstrates significant benefits of the Hybrid Edge Cloud approach:

  • Energy savings of up to 75% compared to centralized systems
  • Cost reductions exceeding 80%, even in resource-intensive agentic scenarios

Implications

The authors conclude that HEC plays a critical role in enabling scalable, cost-effective, and sustainable computing infrastructure for next-generation intelligent systems . The paper is 13 pages long and includes 2 tables and 3 figures to support its analysis .

but cant you just make better distribution algos inside cloud systems? for this problem: Problem Addressed

Cloud workloads often follow a Pareto distribution, meaning a small percentage of tasks consume the majority of resources . This creates bottlenecks and energy inefficiencies in traditional centralized cloud architectures .

Great question! While better distribution algorithms can help, they can't fully solve the problem for several fundamental reasons:

Physical Limitations Algorithms Can't Overcome

Latency is bounded by physics. No matter how smart your load balancing algorithm is, data still needs to travel to distant data centers. Edge computing delivers latency of 100–200ms compared to 500–1,000ms for cloud computing. For autonomous vehicles, robotics, and AR/VR applications requiring sub-10ms response times, this round-trip delay is simply unacceptable.12

Bandwidth Bottleneck

Even with perfect workload distribution, you still must transmit all raw data to the cloud first. Billions of IoT devices generate massive data streams, and transferring everything is expensive and often unnecessary. Edge computing processes data locally, only sending relevant or compressed information to the cloud—reducing bandwidth costs and network congestion.31

Energy Cost of Data Transfer

The paper's energy savings come largely from avoiding data transmission, not just distributing it better. Research shows that the communication itself between clients and servers consumes significant energy, and reduced latency at edge servers directly translates to energy benefits. Better algorithms distribute load more evenly but don't eliminate the energy cost of moving data long distances.4

Algorithm Limitations in Practice

Challenge Can Algorithms Solve It?
Network latency to distant servers No—physics constraint 1
Bandwidth costs for raw data transfer No—data still travels 3
Energy of radio/network transmission No—inherent to communication 4
Uneven server loads Partially—dynamic algorithms help but have limitations 5
Real-time processing needs (<10ms) No—requires local processing 1

Static algorithms like Round-Robin are considered inefficient for modern cloud environments due to their inability to adapt to dynamic workloads. Even advanced algorithms like Weighted Least Connections struggle with the heterogeneous, unpredictable nature of cloud workloads.56

The Hybrid Approach

The key insight is that edge and cloud complement each other. Edge handles latency-sensitive, high-volume initial processing, while cloud handles tasks benefiting from massive scale and centralized resources. Better algorithms within each tier still matter, but they work alongside architectural changes—not as a replacement for them.1 789101112131415

⁂

Footnotes

  1. https://www.emma.ms/blog/edge-computing-vs-cloud-computing ↩ ↩2 ↩3 ↩4 ↩5

  2. https://datafloq.com/edge-computing-vs-cloud-computing-cost-analysis/?amp=1 ↩

  3. https://www.iotforall.com/edge-computing-low-latency-iot ↩ ↩2

  4. https://arxiv.org/abs/2111.10076 ↩ ↩2

  5. https://www.sciencedirect.com/science/article/pii/S131915782100046X ↩ ↩2

  6. https://www.academia.edu/105273744/Limitations_of_Load_Balancing_and_Performance_Analysis_Processes_and_Algorithms_in_Cloud_Computing ↩

  7. https://peg.unipv.it/MCC/publications/workloads-clouds-preprint.pdf ↩

  8. https://versatek.com/how-edge-computing-compares-to-cloud/ ↩

  9. https://www.digitalrealty.be/resources/articles/workload-management ↩

  10. https://blog.seeburger.com/living-in-the-clouds-or-living-on-the-edge-cloud-computing-versus-edge-computing/ ↩

  11. https://www.nature.com/articles/s41598-025-97494-2 ↩

  12. https://www.scalecomputing.com/resources/what-is-the-difference-between-edge-computing-and-cloud-computing ↩

  13. https://www.zeepalm.com/blog/comparing-load-balancing-algorithms-for-cloud-computing ↩

  14. https://cloudian.com/guides/ai-infrastructure/6-types-of-ai-workloads-challenges-and-critical-best-practices/ ↩

  15. https://dl.acm.org/doi/fullHtml/10.1145/3659677.3659713 ↩

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