With the widespread adoption of edge computing, service migration is critical for meeting real-time computing demands and ensuring service continuity. However, the dynamic and uncertain nature of edge computing-enabled networks, characterized by fluctuating topologies, bandwidth, and resources, significantly complicates migration decisions. Existing strategies rely on precise analytical models and reward functions but struggle with generalization and adaptability. This paper proposes a novel service migration strategy driven by active inference for edge computing-enabled networks. Unlike traditional approaches, it eliminates the need for explicit reward functions, instead leveraging a cognitive optimization mechanism where decisions are guided by minimizing free energy. This allows the system to maintain efficient service migration across a wider range of edge scenarios, with enhanced generalization and flexibility. Simulation results show that the proposed strategy outperforms existing approaches by reducing latency and improving adaptability to varying environments, highlighting its superiority in service migration for edge computing-enabled networks.

Collaborative resource allocation is crucial for reducing overhead and enhancing resource utilization in edge computing-enabled networks. To ensure a satisfactory user experience, we recognize the importance of considering information uncertainty in resource allocation. Therefore, we explore information uncertainty in edge computing-enabled networks, especially within the complex environment of resource coupling. However, existing methods lack a comprehensive and robust solution for coordinating wireless, transport, and computing resource under this information uncertainty. This paper addresses this gap by proposing a joint optimization of access point (AP) selection, computing node association, and traffic engineering, aiming to maximize network utility under the uncertain conditions of wireless status and application QoS requirements. The constraints under these uncertainties are modeled as chance constraints, complicating the problem's solvability. We adopt the Bernstein approximation to establish convex conservative approximations of the chance constraints. Given the problem's substantial size and computational complexity, the alternating direction method of multipliers is employed to solve the approximated problem in a distributed manner. We further derive the closed solutions of the corresponding sub-problems. Extensive simulations validate the superiority of our proposed scheme, demonstrating its ability to achieve a good trade-off between meeting user requirements and optimizing resource utilization.

Data centers struggle with growing cluster sizes and rising submissions of short-lived, high-frequency tasks that cause performance bottlenecks in task scheduling. Existing centralized and distributed scheduling systems fall short in meeting performance requirements due to computational overload on the scheduler, cluster state management overhead, and scheduling conflicts. To address these challenges, this paper introduces Beehive, a novel lightweight decentralized scheduling framework. In Beehive, each cluster node can schedule tasks within its local neighborhood, effectively reducing resource management overhead and scheduling conflicts. Moreover, all nodes are interconnected in a small-world network, an efficient structure that allows tasks to access resources across the entire cluster through global routing. This lightweight design enables Beehive to scale efficiently, supporting over 10,000 nodes and up to 80,000 task submissions per second without causing single-node scheduling bottlenecks. Experimental results demonstrate that Beehive significantly reduces scheduling latency. Specifically, 99% of tasks are scheduled within 100 milliseconds, and scheduling throughput can increase linearly with the number of nodes. Compared to existing centralized and distributed scheduling frameworks, Beehive substantially alleviates scheduling bottlenecks, particularly for high-frequency, short-lived tasks

We study the image cropping problem under the condition of determining the aspect ratio. We use two advanced saliency detection models to generate the initial region and then generate random cropping candidates around this region. These candidates are evaluated by an aesthetic scoring model. Next, Bayesian optimization is used to determine the next set of candidates that are likely to have better scores. These candidates are evaluated again by the aesthetic model, and after several iterations, we obtain a relatively optimal cropping result.

The uncertain factors such as network status, measurement errors and quality of service (QoS) requirements of applications make it challenging to guarantee the performance of edge computing-enabled networks through resource allocation schemes modeled on accurate information. This paper investigates the impact of information uncertainty on resource allocation in edge computing-enabled networks. We model the resource constraints as chance constraints and jointly optimize wireless access point (AP) selection, computing node association, and traffic engineering to maximize the network utility. Since the problem contains uncertainty parameters and binary variables, it is intractable to solve. Therefore, we utilize the Bernstein approximation to derive convex conservative approximations for chance constraints. To address the unrealistic nature of the problem due to its large size and computing complexity, we employ the alternating direction method of multiplier to iterate wireless AP selection, computing node association, and bandwidth allocation in a distributed manner. Additionally, we use the convex optimization method to solve the corresponding sub-problems. Simulations are conducted to demonstrate that our proposed resource allocation scheme can satisfy more requirements and save more resources than other schemes.

The dynamic edge computing-enabled networks contain various resources, and network parameters and system models are subject to uncertainty. Despite this, there is still a lack of comprehensive online solutions for coordinating wireless, transport, and computing resources. This paper investigates the use of online convex optimization for resource allocation in edge computing-enabled networks with time-varying cost and time-varying constraint functions. Taking into account the uncertainty of wireless status, quality of service requirements, and cost function, the goal is to minimize the long-term cost by optimizing the selection of access points, association of computing nodes, allocation of computing resources, and bandwidth allocation. To address the proposed online resource allocation problem, the modified online saddle-point algorithm is employed and dynamic regret and accumulative constraint violation are defined to measure the performance of the algorithm. To reduce the computational complexity of the projection in the modified online saddle point algorithm, the projection is reformulated as quadratic programs, which can be solved efficiently by convex optimization. Finally, the effectiveness and superiority of the proposed solution are demonstrated through simulation analysis.

Due to the limited computation resources of mobile devices in cognitive radio networks, the secondary users who without licensed spectrum in the network can suffer from long executing time, which is not acceptable for latency-sensitive and computation- intensive tasks. To tackle this issue, this paper proposes to reduce the task computing latency for secondary networks by offloading the tasks to edge servers through leveraging mobile edge computing (MEC) that is emerging as a promising technology to augment the computation capacity of mobile devices. Specifically, under the conditions that the interference caused by secondary users is tolerable to primary user, i.e., the quality of service of the PU can be guaranteed, and within the available computation resources of the MEC server, the primary user and secondary users with different channel gains both can offload tasks to the MEC server through non-orthogonal multiple access. Thus, we jointly formulate the offloading decision and power control as an optimization problem, aiming at minimizing the overall computing latency for secondary networks. To overcome the computational complexity caused by the non-convexity of the original problem, we transform the original problem to a solvable problem and decouple the transformed problem into the separate offloading decision and power control. An iterative algorithm is proposed based on block coordinate decent method to achieve the near-optimal solution. Simulation results show that the proposed scheme can effectively reduce the overall computing latency for the secondary network.

Summary In this paper, we study the full‐duplex relay selection strategy based on a potential game in a cognitive cooperative network under the interference power constraint from secondary users to the primary receivers, the total available transmission power constraint for the secondary system, and the self‐interference constraint at each secondary relay. The relay selection problem is modeled as a non‐cooperative game where the total rate of a cognitive cooperative network has common utility. Then, we prove that the game is a potential game that has at least a pure strategy Nash equilibrium (NE), and the optimal strategy set that able to maximize cognitive cooperative system rate is also a pure strategy NE of the proposed game model. On the premise of having no information of infeasible strategy sets, we solve the feasibility conditions of the pure NE in the proposed game. Furthermore, we propose a cognitive full‐duplex relay iterative algorithm that can achieve a pure strategy NE, and the complexity and the convergence of the proposed algorithm are studied. Simulation results show that the proposed algorithm can achieve optimal or near optimal rate performance with low complexity and offers significant performance gain compared with the traditional half‐duplex mode.

When the applications are running in the virtual machine (VM), the virtual address of VM are translated into physical address in host. To improve the quality of address translation, huge page mechanism is introduced to increase Translation Lookaside Buffer (TLB) hit rates and reduce page faults. In this paper, we discuss and investigate the impact ofTHP(transparent huge page) on VM.

Summary Hybrid storage systems that consist of flash‐based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two‐level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two‐level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi‐level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi‐level cache exclusiveness and makes cache resource management more efficient. By using real‐life storage traces, our evaluation shows the proposed algorithm improves hybrid multi‐level cache performance and also increases the SSD lifetime compared with traditional multi‐level cache replacement algorithms. Copyright © 2015 John Wiley & Sons, Ltd.