Adaptive Service Provisioning for Mobile Edge Cloud

doi:10.3969/j.issn.1673-5188.2017.02.001

Abstract

Abstract:

A mobile edge cloud provides a platform to accommodate the offloaded traffic workload generated by mobile devices. It can significantly reduce the access delay for mobile application users. However, the high user mobility brings significant challenges to the service provisioning for mobile users, especially to delay-sensitive mobile applications. With the objective to maximize a profit, which positively associates with the overall admitted traffic served by the local edge cloud, and negatively associates with the access delay as well as virtual machine migration delay, we study a fundamental problem in this paper: how to update the service provisioning solution for a given group of mobile users. Such a profit-maximization problem is formulated as a nonlinear integer linear programming and linearized by absolute value manipulation techniques. Then, we propose a framework of heuristic algorithms to solve this Nondeterministic Polynomial (NP)-hard problem. The numerical simulation results demonstrate the efficiency of the devised algorithms. Some useful summaries are concluded via the analysis of evaluation results.

Key words: edge cloud, mobile computing, service provisioning

HUANG Huawei, GUO Song. Adaptive Service Provisioning for Mobile Edge Cloud[J]. ZTE Communications, 2017, 15(2): 2-10.

Figures/Tables 7

Figure 1. An example of service provisioning for mobile users under a cloudlet based network. The workload generated from a mobile device can be offloaded to a VM, which resides in the local edge cloud or in a remote private cloud. Meanwhile, this figure also demonstrates the dynamic characteristics of an edge network, e.g., a mobile user alternates in online and offline status frequently.

Figure 2. System model.

Table 1 Symbols and variables

Notation	Description
U	the set of mobile users in network
S	the set of servers in the local cloudlet based network
T	the set of candidate time slots when to update the provisioning solution for each online mobile user
D_u	the demanded traffic rate of user u ∈U
C_s	the traffic processing capacity of server s ∈S
F (u )	a set of time-slots, in each of which user u becomes online from offline status, according to its given trajectory
$R u t$	the access delay from user u to the remote private cloud at time slot t
$C u t$	the access delay from user u to the local edge server at time slot t
Δ^t	total access delay of all mobile users at time-slot t
ζ	the normalized VM-migration delay between the private cloud and a local edge server
$Γ u t$	total VM-migration delay of all mobile users at time-slot t
$x u t$	binary variable indicating the location where to deploy a VM for an online user u ∈ U at time-slot t ∈T
$z u t$	binary variable denoting whether to migrate a VM between the remote private cloud and the local cloudlet network for an online user u ∈U at time-slot t ∈T

Table 1 Symbols and variables

Notation	Description
U	the set of mobile users in network
S	the set of servers in the local cloudlet based network
T	the set of candidate time slots when to update the provisioning solution for each online mobile user
D_u	the demanded traffic rate of user u ∈U
C_s	the traffic processing capacity of server s ∈S
F (u )	a set of time-slots, in each of which user u becomes online from offline status, according to its given trajectory
$R u t$	the access delay from user u to the remote private cloud at time slot t
$C u t$	the access delay from user u to the local edge server at time slot t
Δ^t	total access delay of all mobile users at time-slot t
ζ	the normalized VM-migration delay between the private cloud and a local edge server
$Γ u t$	total VM-migration delay of all mobile users at time-slot t
$x u t$	binary variable indicating the location where to deploy a VM for an online user u ∈ U at time-slot t ∈T
$z u t$	binary variable denoting whether to migrate a VM between the remote private cloud and the local cloudlet network for an online user u ∈U at time-slot t ∈T

Figure 3. The structure and the feasibility specification of a solution.

Figure 4. Performance of algorithms when the serving capacity of a local server (i.e., Cs) varies in a range 600 Mb/s-1500 Mb/s.

Figure 5. Performance of algorithms when the weight of access delay (i.e., w1) varies in a range 1-5.

Figure 6. Performance of algorithms when the weight of migration delay (i.e., w 2) varies in a range 1-5.

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