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To address the problem that unmanned aerial vehicle (UAV) face complex time-varying fading channels, which could affect wireless transmission, a joint optimization problem of UAV's trajectory, reconfigurable intelligent surface (RIS) phase shift, offloading slot allocation, CPU frequency allocation, and user equipment transmission power was constructed. In order to solve the constructed problem, the stability constraints of UE and UAV task queues were transformed, and the multi-timeslot stochastic optimization problem was transformed into a deterministic optimization problem for each time slot. A JORL optimization method based on Lyapunov optimization and block coordinate descent (BCD) method was proposed. Firstly, the phase shift of RIS was solved based on the triangle inequality and the closure expression was obtained. Then the technology of transforming the non-convex into the convex problem was used to solve the offloading slot allocation, CPU frequency allocation and user equipment transmission power. Finally, the trajectory of UAV was solved based on successive convex approximation (SCA) method. Simulation results show that JORL has better performance in ensuring queue stability and reducing energy consumption.

To address the issue where the resource limitations of wireless devices caused state extraction delays that cannot meet the freshness requirements of real-time applications, considering the limited processing capacity of edge nodes, a scheduling method that jointly considered information freshness and real-time performance was proposed. This method initially characterized the task delay before computation and the information freshness after computation by utilizing the system time of the queue and the age of information, respectively. Simultaneously, reasonable deadlines were assigned to each offloaded task to ensure their validity before entering the computation process. Then, the minimum processing rate constraint method was employed to restrict the processing rate during task scheduling, thereby ensuring the real-time nature of task scheduling. Finally, the objective of optimizing long-term task scheduling decisions was achieved based on Lyapunov optimization techniques. Simulation results demonstrate the good performance of the proposed method in both scheduling timeliness and system information freshness.

In response to the construction of green data centers for sustainable development, reducing data center power consumption and carbon dioxide emissions has become an urgent issue. A data center model considering cooling systems was established. The model transforms cost functions of data center power costs, bandwidth costs, carbon emission costs, and cooling costs into a cost minimization problem. The data center operational costs are reduced by effective workload scheduling and rational servers on-off strategies. The electricity prices and carbon emission rates are difficult to predict accurately due to the fluctuation. A feature of the Lyapunov optimization framework was utilized to propose an online control strategy. The strategy only requires knowledge of the current system information. An alternating direction method of multipliers (ADMM) is introduced to enable the control center to coordinate workload distribution among various data centers. Data centers only need to exchange workload decisions, effectively reducing computational complexity and safeguarding user privacy. Simulation results based on real-world data demonstrate that the proposed algorithms can effectively reduce data center operational costs. [ABSTRACT FROM AUTHOR]

In mobile edge computing research, the edge server can effectively save computing resources by caching task data. But how to allocate cache resources to solve the competitive relationship between edge servers, as well as energy consumption and efficiency issues, and achieve optimal system performance is an NP difficult problem. Therefore, this paper proposes an online potential game resource allocation strategy OPSCO (online potential game strategy based on cache optimization) based on cache optimization, using a new cache replacement strategy CASCU (cache allocation strategy based on cache utility) to maximize the utility of the cache. By optimizing the efficiency indicator function of edge servers, combining factors such as cache replacement cost with Lyapunov optimization, potential game, and EWA (exponential weighting algorithm) algorithm, the competitive relationship of edge servers is modeled, and potential game related proofs and analyses are conducted. The simulation results show that compared with other resource allocation strategies, OPSCO can significantly improve the task completion rate and cache utility, reduce equipment energy consumption and time overhead, and solve the resource allocation and data cache problems in mobile edge computing online cache scenarios. [ABSTRACT FROM AUTHOR]

To address the information collection and aggregation issue in emergency scenarios characterized by the lack of public infrastructure and unstable satellite signals, an information timeliness-oriented information collection and transmission scheduling mechanism was proposed for emergency unmanned aerial vehicle (UAV) networks where information collection and transmission capabilities were constrained by energy consumption.Considering the age of information (AoI) as the metric and constraint of information timeliness, a stochastics optimization problem was constructed with the objective of minimizing UAV information collection and transmission energy consumption.By resorting to the Lyapunov optimization technique, virtual queues were established to impose information timeliness constraints on queue lengths, and the original problem was decoupled into two sub-problems, information collection and transmission scheduling, with the premise of ensuring system stability.Corresponding heuristic algorithms were proposed for each sub-problem.Simulation results demonstrate that the proposed algorithm outperforms conventional queue scheduling approaches in convergence rates and system energy consumption with guaranteed information timeliness.

Traditional network architectures cannot meet user needs for content caching, and there is a conflict between low latency requirements and high communication costs in content delivery. To address these problems, an online content caching and delivery algorithm based on Lyapunov optimization and branch and bound method is proposed under the scenario of horizontal collaboration between edge nodes to balance delivery delay and cost and to make efficient decisions on content caching and content delivery. The proposed algorithm decomposes the continuous problem into a single slot online optimization problem based on Lyapunov optimization theory, and solves them using branch and bound algorithms. Simulation experiments showed that the proposed algorithm can achieve lower average content delivery delay and higher content hit rate under a limited content delivery cost budget. It can also adaptively balance content delivery delay and delivery cost. [ABSTRACT FROM AUTHOR]

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Based on the idea of crowdsourcing,mobile crowdsensing(MCS) collects mobile sensing devices to sense the surroun-ding environment,which can make environment sensing and information collection more flexible,convenient and efficient.Whe-ther the task allocation strategy is reasonable or not directly affects the success of the sensing task.Therefore,formulating a reasonable task allocation strategy is a hotspot and focus in the research of MCS.At present,most of the task allocation methods in MCS systems are offline and targeted at single type tasks.However,in practice,online multi-type task allocation is more common.Therefore,this paper studies the task allocation method in MCS for multiple types of tasks,and proposes an online task allocation strategy oriented to system benefits combined with the characteristics of MCS technology in the military field.In this paper,a long-term,dynamic online task allocation system model is established,and the problem is solved based on Lyapunov optimization theory with the system benefit as the optimization goal,so that the online dynamic control of task admission strategy and task allocation scheme is realized.Experiment shows that the online task allocation algorithm proposed in this paper is effective and feasible.It can reasonably allocate the tasks arriving at the MCS system online,ensure the stability of the task queue,and increase the system utility by adjusting the parameter value.

In order to reduce the cost and improve efficiency of power line inspection, UAV (unmanned aerial vehicle), which use mobile edge computing technology to access and process service data, are used to inspect power lines in the energy internet.However, due to the dynamic changes of UAV data transmission demand and geographical location, the edge server load will be unbalanced, which causes higher service processing delay and network energy consumption.Thus, an intelligent inspection task allocation mechanism for energy internet based on deep reinforcement learning was proposed.First, a two-layer edge network task offloading model was established to archive joint optimization of multi-objectives, such as delay and energy consumption.It was designed by comprehensively considering the route of UAV and edge nodes, different demands of services and limited service capabilities of edge nodes.Furthermore, based on Lyapunov optimization theory and dual-time-scaled mechanism, proximal policy optimization algorithm based deep reinforcement learning was used to solve the connection relationship and offloading strategy of edge servers between fixed edge sink layer and mobile edge access layer.The simulation results show that, the proposed mechanism can reduce the service request delay and system energy consumption while ensuring the stability of system.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)