TY - GEN
T1 - An OO-based approach of computing offloading and resource allocation for large-scale mobile edge computing systems
AU - Tan, Yufu
AU - Ali, Sikandar
AU - Wang, Haotian
AU - Huang, Jiwei
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Mobile edge computing (MEC) is an emerging paradigm to meet the increasing real-time performance demands for Internet of Things and mobile applications. By offloading the computationally intensive workloads to edge servers, the quality of service (QoS) could be greatly improved. However, with the growing popularity of MEC, the MEC systems grow extremely large, and thus the QoS optimization suffers from search space explosion problem, making it impractical in real-life scenarios. To attack this challenge, this paper studies the joint optimization of task offloading and computational resource allocation for large-scale MEC systems. We formulate this problem as a cost minimization problem and illustrate the NP-hardness of this problem. In order to solve this problem, we divide the original problem into two sub-problems and introduce the theory of Ordinal Optimization (OO) to search for a near-optimal computing offloading and resource allocation policy within a significantly reduced search space. Finally, the efficacy of our approach is validated by simulation experiments.
AB - Mobile edge computing (MEC) is an emerging paradigm to meet the increasing real-time performance demands for Internet of Things and mobile applications. By offloading the computationally intensive workloads to edge servers, the quality of service (QoS) could be greatly improved. However, with the growing popularity of MEC, the MEC systems grow extremely large, and thus the QoS optimization suffers from search space explosion problem, making it impractical in real-life scenarios. To attack this challenge, this paper studies the joint optimization of task offloading and computational resource allocation for large-scale MEC systems. We formulate this problem as a cost minimization problem and illustrate the NP-hardness of this problem. In order to solve this problem, we divide the original problem into two sub-problems and introduce the theory of Ordinal Optimization (OO) to search for a near-optimal computing offloading and resource allocation policy within a significantly reduced search space. Finally, the efficacy of our approach is validated by simulation experiments.
KW - mobile edge computing (MEC)
KW - computing offloading
KW - resource allocation
KW - ordinal optimization
KW - large-scale MEC systems
UR - https://www.scopus.com/inward/record.url?eid=2-s2.0-85122595824&partnerID=MN8TOARS
U2 - 10.1007/978-3-030-92638-0_5
DO - 10.1007/978-3-030-92638-0_5
M3 - Conference contribution
SN - 9783030926373
T3 - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
SP - 65
EP - 83
BT - Collaborative Computing: Networking, Applications and Worksharing
PB - Springer Cham
ER -