Workload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloading

Minh Tuan Thai*, Ying Dar Lin, Yuan Cheng Lai, Hsu Tung Chien

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-Tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-And-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.

Original languageAmerican English
Article number8813101
Pages (from-to)227-238
Number of pages12
JournalIEEE Transactions on Network and Service Management
Issue number1
StatePublished - Mar 2020


  • capacity optimization
  • edge computing
  • fog computing
  • optimization
  • workload offloading


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