Modeling multi-site computation offloading in unreliable cloud environments

Pirbasti, Marzieh Ranjbar; Das, Olivia

doi:10.1016/j.peva.2020.102136

Cited by 3 publications

(1 citation statement)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al [13] combines the queuing model with the response time to ensure that various energy latencies can still be balanced when a fault occurs. Pirbasti et al [14] proposes a fault-aware multi-site offloading optimization model. It uses a checkpoint mechanism to reduce the impact of failures and further considers reducing system energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Reliable and adaptive computation offload strategy with load and cost coordination for edge computing

Tang,

Gao,

et al. 2024

Pervasive and Mobile Computing

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Reliable and adaptive computation offload strategy with load and cost coordination for edge computing

Tang,

Gao,

et al. 2024

Pervasive and Mobile Computing

View full text Add to dashboard Cite

Ovcosim: an open-source versatile computation offloading simulator for cloud and fog computing

Pirbasti,

Das

2024

Cluster Comput

View full text Add to dashboard Cite

Reliable and Adaptive Computation offload strategy with load and cost coordination for Edge computing

Tang,

Gao,

et al. 2023

Preprint

View full text Add to dashboard Cite

There are several important factors to consider in edge computing systems, including latency, reliability, power consumption and queue load. Task replication requires additional energy costs in the mobile edge offloading scenario based on master-slave replication for fault tolerance. Excessive task offloading may lead to sharp increase in the total energy consumption of the system including replication costs. On the contrary, new tasks cannot enter the waiting queue and are lost, resulting in unreliable problems. This paper proposes propose an adaptive task offloading strategy for balancing the edge node queue load and offloading cost (Lyapunov and Differential Evolution based Offloading schedule strategy, LDEO). The LDEO strategy customizes the Lyapunov drift-plus-penalty function by incorporating replication redundancy offloading costs to establish a balance model between the queue load and offloading cost. By integrating a low-complexity differential evolution method, the LDEO strategy computes the optimal offloading decisions with dynamic adjustment characteristics, aiming to find the optimal balance point that minimizes the offloading cost while maintaining reliability performance. Experimental results show that compared with the existing strategies, the LDEO strategy effectively reduces the redundancy and the growth rate of task completion time, with similar task execution completion rate. By ensuring that the queue length remains stable within a reasonable range, controlling both task waiting time and loss rate, and reducing the additional energy consumption associated with replication redundancy, the LDEO strategy effectively achieves optimal balancing under multiple conditions.

show abstract

Modeling multi-site computation offloading in unreliable cloud environments

Cited by 3 publications

References 73 publications

Reliable and adaptive computation offload strategy with load and cost coordination for edge computing

Reliable and adaptive computation offload strategy with load and cost coordination for edge computing

Ovcosim: an open-source versatile computation offloading simulator for cloud and fog computing

Reliable and Adaptive Computation offload strategy with load and cost coordination for Edge computing

Contact Info

Product

Resources

About