Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting

Abstract

Mobile Edge Computing (MEC) has evolved into a promising technology that can relieve computing pressure on wireless devices (WDs) in the Internet of Things (IoT) by offloading computation tasks to the MEC server. Resource management and allocation are challenging because of the unpredictability of task arrival, wireless channel status and energy consumption. To address such a challenge, in this paper, we provide an energy-efficient joint resource management and allocation (ECM-RMA) policy to reduce time-averaged energy consumption in a multi-user multi-task MEC system with hybrid energy harvested WDs. We first formulate the time-averaged energy consumption minimization problem while the MEC system satisfied both the data queue stability constraint and energy queue stability constraint. To solve the stochastic optimization problem, we turn the problem into two deterministic sub-problems, which can be easily solved by convex optimization technique and linear programming technique. Correspondingly, we propose the ECM-RMA algorithm that does not require priori knowledge of stochastic processes such as channel states, data arrivals and green energy harvesting. Most importantly, the proposed algorithm achieves the energy consumption-delay trade-off as [scriptO(1/V),scriptO(V)]. V, as a non-negative weight, which can effectively control the energy consumption-delay performance. Finally, simulation results verify the correctness of the theoretical analysis and the effectiveness of the proposed algorithm.

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

“…In [95], an energy efficient joint resource management and allocation (ECM-RMA) technique is proposed. This algorithm tackles the stability problem of MEC in data queue and energy queue.…”

“…EERA-NOMA [93] ECM-RMA [95] EERA-MEC [99] MECH [100] EPCO [103] Joint Opt [101] Figure 7: Algorithms suitable for long-term environmental monitoring.…”

“…Author et al: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS UC-JOPT [91] TCO [92] EERA-NOMA [93] GTC-MEC [94] ECM-RMA [95] C2OF2N [96] DCA-TAMC [97] ACCO [98] EERA-MEC [99] MECH [100] Joint Opt [101] EECO [102] EPCO [103] Algorithms having low energy consumption Figure 10: Algorithms having low energy consumption, but need to be more robust.…”

Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

“…For the phase where the AP transmits the result of the task execution to the SMD via the downlink, because the size of the returned data result is often much smaller than the amount of the task transmitted by the uplink, and the distance between the SMD and the AP compared with the distance between SMD and the remote cloud server is much smaller, so the delay and energy consumption at this stage is often not considered in many previous works. We also ignore the delay and energy consumption of the downlink [3] [29]. As for the last phase is because in addition to AP can handle tasks offloaded from the SMD, there are also services that APs do not cache, or APs do not have enough computing power.…”

“…However, based on the consideration of the portability of SMD, the small size of SMD leads to faster energy consumption, weak computing power, and small storage space. This shortcoming of SMD severely hinders the deployment of a large number of applications [3]. Therefore, SMD attempts to overcome the shortcomings of itself by utilizing the supercomputing power and super storage capacity of cloud computing resources, that is, connecting to a remote cloud through a wireless network and offloading computing tasks to the remote cloud [4].…”