Mobile edge computing (MEC) is a promising tech-nology to support the latency-critical applications of mobile devices by offloading complex computation tasks to edge servers. However, the mobility of devices yield a great challenge on de-livering reliable continuous services, especially for those latency- critical applications. Motivated by the fact that the user's location changes slower than the task arrivals, we propose a two-timescale mobility management framework by joint service migration and power control. The management design is formulated as a long-term energy minimization problem, subject to the reliability requirement of the latency-critical application. Leveraging the Lyapunov optimization technique, we develop an online two- timescale control algorithm to solve the problem. The simulation results demonstrate that our proposed online algorithm can significantly improve the energy and reliability performance compared to the baselines.

With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence-optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices.

Mobile edge computing (MEC) is a promising technology for enhancing the computation capacities and features of mobile users by offloading complex computation tasks to the edge servers. However, mobility poses great challenges on delivering reliable MEC service required for latency-critical applications. First, mobility management has to tackle the dynamics of both user’s location changes and task arrivals that vary in different timescales. Second, user mobility could induce service migration, leading to reliability loss due to the migration delay. In this paper, we propose a two-timescale mobility management framework by joint control of service migration and transmission power to address the above challenges. Specifically, the service migration operates at a large timescale to support user mobility in the multi-cell network, while the power control is performed at a small timescale for real-time task offloading. Their joint control is formulated as an optimization problem aiming at the long-term mobile energy minimization subject to the reliability requirement of computation offloading. To solve the problem, we propose a Lyapunov-based framework to decompose the problem into different timescales, based on which a low-complexity two-timescale online algorithm is developed by exploiting the problem structure. The proposed online algorithm is shown to be asymptotically optimal via theoretical analysis, and is further developed to accommodate the multiuser management. The simulation results demonstrate that our proposed algorithm can significantly improve the energy and reliability performance.

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-to-optimal 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.

Mobile-edge computing (MEC) enhances the capacities and features of mobile devices via offloading computation-intensive tasks over wireless networks to the edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility, so that the 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 policy design is formulated as a multi-objective optimization problem that maximizes the sum offloading rate, quantifying MEC throughput, and minimizes the migration cost, where the issues of virtualization, I/O interference between virtual machines (VMs), and wireless multi-access are taken into account. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based approach, including an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design that exploits the derived problem properties. The simulation results show the close-to-optimal performance of the proposed migration policies under various settings, validating their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, arises that degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, we formulate a sum offloading rate maximization problem by joint offloading-user scheduling, the offloaded size control, and time allocation for communication (offloading and downloading) and computation. The problem is a non-convex mixed-integer programming problem. An optimal algorithm with low complexity is designed based on a decomposition approach and Dinkelbach method. The simulation results demonstrate considering of I/O interference can endow on an offloading controller robustness against the performancedegradation factor.

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of the mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, offloading scheduling algorithms is designed targeting two system performance metrics: sum offloading rate maximization and sum mobile energy consumption minimization. Their designs are formulated as non-convex mixed-integer programming problems, which account for latency due to offloading, result downloading, and parallel computing. A set of low-complexity algorithms are designed based on a decomposition approach and leveraging classic techniques from combinatorial optimization. The resultant algorithms jointly schedule offloading users, control their offloading sizes, and divide time for communication (offloading and downloading) and computation. They are either optimal or can achieve close-to-optimality as shown by simulation. The comprehensive simulation results demonstrate that considering of I/O interference can endow on an offloading controller robustness against the performance-degradation factor.