Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management

Abstract

Mobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lifetime, by offloading intensive computation from mobiles to nearby servers such as base stations. In this paper, we study the energy-efficient resourcemanagement policy for the asynchronous MECO system, where the mobiles have heterogeneous inputdata arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained in (semi-) closed-form expression by using the block coordinate decent method. To gain further insights, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to equalize the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach, which first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

“…How to improve EE of mobile devices in MEC systems subject to the delay constraint has been widely studied in existing literature [7,8,[12][13][14]. To study the tradeoff between EE and latency, a weighted sum of energy consumption and latency was minimized in a single-AP scenario [7].…”

“…With this scheme, there is no exploration and the output of the DNN will be used as the user association scheme. Some similar studies focused on offloading and resource allocation with a single AP [8,18]. The implicit assumption on the user association is that the users are served by the nearest AP or the AP with the highest large-scale channel gain.…”

“…19a), (19e), (19f), (1), (2),(7),(8),(11),(12),(14) and(15),If η ξ k ≤ η th , then N ξ * k ≤ N th,ξ k . Otherwise, N ξ * k ≥ N th,ξ k (See proof in Subsection V-C).…”

Impact of Non-Orthogonal Multiple Access on the Offloading of Mobile Edge Computing

“…In [15], a user scheduling scheme was proposed to MEC in order to achieve a balanced tradeoff between the latency and reliability for task offloading. In [16], a more challenging multi-user MEC scenario was considered, where the users offload their tasks to the MEC server in an asynchronous manner. Initial studies in [17] and [18] have already demonstrated the benefit for the application of NOMA to MEC, by developing various optimization frameworks.…”

Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

“…Zhang et al [14] presented an energy-efficient computation offloading scheme that incurs minimal energy consumption under latency constraints by optimizing the offloading policy and radio resource allocation for MEC in 5G heterogeneous networks with multiaccess characteristics. You et al [15], based on insight into the input data arrival time instants and computation deadlines, studied an energy-efficient resource management policy for MECO systems and formulated an optimization strategy that minimizes the total mobile-energy consumption. Unfortunately, these works are mainly interested in reducing the energy consumption without attempting to reduce the completion time of computation tasks.…”