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Resilient Resource Allocation for Service Placement in Mobile Edge Clouds

Time: Thu 2021-04-15 13.00

Location: nk for online defense (English)

Subject area: Electrical Engineering

Doctoral student: Peiyue Zhao , Nätverk och systemteknik

Opponent: Professor Kin K. Leung, Imperial College

Supervisor: Professor György Dán, Nätverk och systemteknik


Mobile edge computing makes available distributed computation and stor-age resources in close proximity to end users and allows to provide low-latencyand high-capacity services within mobile networks. Therefore, mobile edgecomputing is emerging as a promising architecture for hosting critical ser-vices with stringent latency and performance requirements, which otherwiseare challenging to be addressed in conventional cloud computing architectures.Notable use cases of mobile edge computing include real-time data analyticservices, industrial process control, and computation offloading for Internetof things devices. However, those services rely on efficient resource manage-ment, including resource dimensioning and service placement, and require tobe resilient to cyber-attacks, to faulty components and to operation mistakes.The work in this thesis proposes models of resilient resource management thatsupport rapid response to incidents in mobile edge computing and developsefficient algorithms for the resulting resource management problems.

In the first part of the thesis, we consider resilient resource managementfor edge computing systems in which failover is realized by restoring additionalservice instances in different mobile edge computing nodes in case of failures.We first develop a placement algorithm based on Benders decomposition andlinear relaxation to determine the mobile edge computing nodes to be openedand to compute the placement of the service instances with respect to a set ofconsidered failure scenarios, with the objective of minimizing operation costs.Upon the occurrence of a failure scenario, service migration is to be triggeredto migrate the service instances from one placement to another placement, forwhich we further develop service migration algorithms to schedule migrationunder time constraints, so as to minimize service interruptions.

In the second part of the thesis, we consider resilient resource manage-ment in mobile edge computing for services with different levels of resiliencerequirements. Resilience is achieved by synchronizing states of the servicesto two types of standby instances that maintain the trade-off between en-ergy consumption and activation time such that the standby instances cantake over the service seamlessly as an instantaneous failure response. We for-mulate the joint problem of resource dimensioning and service placement forminimizing energy consumption and prove that it is NP-hard. We propose anefficient approximation algorithm based on Lagrangian relaxation to decidethe type, amount, and locations of the computation resources and to com-pute the placement of service instances and their standby instances. We thenconsider the same resilience model but for hosting periodic services in mo-bile edge computing systems with resources portioned into availability zones,under schedulability constraints. We formulate the corresponding resilient re-source management problem as a non-linear programming problem and provethat it is NP-hard. We propose efficient solutions based on approximationprogramming and primal-dual approaches for resilient service placement.

By considering different models of resilient service placement in mobileedge computing, the results in this thesis provide effective, efficient, and scal-able resource management algorithms for emerging mobile edge computingsystems.

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Last changed: Apr 15, 2021