A Two-Sided Matching Approach for Distributed Edge Computation Offloading

Bao, Hongyi; Liu, Yuan

doi:10.1109/iccchina.2019.8855906

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…Adding a common Z to each term serves for rewriting the objective function (5) as a form of sum of non-negative functions, so as to meet the requirement of the sum-of-ratios algorithm design. Evidently, since R k,n monotonically decreases with y n , the auxiliary variable y n always achieves its lower bound k∈K x k,n in (11) for optimality, i.e., the equality holds in (11). Due to the integer relaxation at constraints (12), Problem (P1 ′ ) yields the upper bound of the original Problem (P1).…”

Section: A Continuous Relaxation and Fractional Programming Transformmentioning

confidence: 99%

“…In a multiuser and multi-server system, there is an additional issue of load distribution among servers. It is addressed in [11] by an efficient distributed offloading design based on matching theory and in [12] using the reinforcement-learning approach. It has also been studied in various MEC system configurations like vehicular networks [13] and unmanned aerial vehicle (UAV) systems [14].…”

Section: A Resource Management In Mec Networkmentioning

confidence: 99%

“…Then, the convex optimization methods can be used to solve this problem optimally. By introducing a set of Lagrangian multipliers µ = {µ n } 0 associated with the constraints (11), the dual problem of Problem ( 18) can be expressed as…”

Section: B Solving Problem (P2) Given (α β γ)mentioning

confidence: 99%

“…Next, we turn to construct an effective y ∈ Y by rounding the fractional-optimal y ′ . Note that y ′ satisfies n∈N y ′ n = K because of the necessarily optimal condition k∈K x ′ k,n = y ′ n in (11), and ⌈y ′ n ⌉ ≤ M n , ∀n, since y ′ n ≤ M n by ( 13) and M n is integral, where ⌊•⌋/⌈•⌉ denotes the floor/ceil operation. Let s K− n∈N ⌊y ′ n ⌋ and ŷ {ŷ n } be the recovered integer solution.…”

Section: Integer Recovery For Problem (P1)mentioning

confidence: 99%

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Multi-Cell Mobile Edge Computing: Joint Service Migration and Resource Allocation

Liang

Liu

Lok

et al. 2021

Preprint

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Mobile-edge computing (MEC) enhances the capacities and features of mobile devices by offloading computation-intensive tasks over wireless networks to edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility. As a result, offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The objectives are twofold: maximizing the sum offloading rate, quantifying MEC throughput, and minimizing the migration cost. The policy design is formulated as a decision-optimization problem that accounts for virtualization, I/O interference between virtual machines (VMs), and wireless multi-access. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based solution approach. The approach relies on an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design. The latter outperforms the traditional rounding method by exploiting the derived problem properties and applying matching theory. In addition, we also consider the design for a special case of "hotspot mitigation", referring to alleviating an overloaded server/BS by migrating its load to the nearby idle servers/BSs. From simulation results, we observed close-tooptimal performance of the proposed migration policies under various settings. This demonstrates their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.

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