Joint Computation Offloading and Bandwidth Assignment in Cloud-Assisted Edge Computing

Joint Wireless and Edge Computing Resource Management With Dynamic Network Slice Selection

“…s.t. ( 12), (36) in which the inter-slice radio resource provisioning coefficients are set according to an arbitrary policy P b and the intra-slice radio and computing power provisioning coefficients are set according to the optimal policies Pwa and Pwc , respectively.…”

“…Theorem 8: The COS algorithm is a 2.62-approximation algorithm for the optimization problem ( 35)- (36) in terms of the system cost, i.e., C(d * ) C( d) ≤ 2.62. Proof: Let us denote by D * ⊆ D the set of all vectors of offloading decisions that can be computed using the COS algorithm given any policy P b and the collection ( Pwa , Pwc ) of the optimal resource allocation policies of slices.…”

“…The authors in [35] addressed the problem of minimizing the long-term average task completion times, and based on the decomposition of the original problem, proposed an iterative algorithm for which they provided a competitive ratio. In [36] the authors considered the problem of joint optimization of the task placement and the allocation of uplink and downlink communication resources, and proposed an algorithm that minimizes the average completion times of the tasks. The authors in [37] defined the cost of each device as a function of its energy consumption and task completion time, formulated the problem of minimizing the sum cost of devices, and based on the relaxation of the original problem, proposed a heuristic for computing the offloading decisions of devices and allocating communication and computing resources.…”

A Survey and Taxonomy on Task Offloading for Edge-Cloud Computing

“…A very few works concern the QoE as a metric directly. energy Gao et al [49] independent full cost Chen et al [50] independent full cost Chen et al [51] independent full profit Yuan et al [52] independent full profit Lin et al [53] independent full performance, energy Du et al [54] independent full performance, energy Duan et al [55] independent full performance, energy Mahmud et al [56] independent full performance, profit Li et al [57] independent full Performance, cost Sun et al [58] independent full performance, cost Adhikari et al [59] independent full performance, utilization Ma et al [60] independent full QoE, cost Miao et al [61] independent partial performance Kai et al [62] independent partial performance Guo et al [63] independent partial performance Meng et al [64], [65] independent partial performance hop-e Cui et al [66], [67] independent partial performance hop-d, hop-e Sarkar et al [68] independent partial performance hop-e Ouyang et al [69] independent partial performance Y Cheng et al [70] independent partial energy Xia et al [71] independent partial energy Zhang et al [72] independent partial cost Chabbouh et al [73] independent partial performance, balance Y Wang et al [74] independent partial performance, cost Zhao et al [75] independent partial performance, cost Khayyat et al [76] independent partial performance, energy Alshahrani et al [77] independent partial performance, energy Chen et al [78] independent partial performance, cost, energy Hong et al [16] independent partial performance, energy hop-d Sun et al [79] independent partial performance, energy Long et al [80] independent partial performance, energy Nguyen et al…”

“…Guo et al [63] study on optimizing the average response time of all user requests by address the offloading decision and bandwidth allocation problem in an edge-cloud. They first formulate the problem, and then decompose it into multiple convex subproblems each of which can be solved based on the binary search method and Newton's method.…”