Network slices face challenges in supporting dynamic requests from user equipments (UEs) due to the resource constraints at the edge devices. This may result in potential anomalies due to resource unavailability or latency spikes. Existing schemes are hard to apply due to the lack of flexible steering of UE requests and multiplexed provisioning strategies. In this paper, we present a novel request provisioning model and propose a traffic steering framework to prevent anomalies and reduce the cost of serving normal UEs. Specifically, we categorize the UE requests into stateful and stateless types and utilize a multi-route resource provisioning strategy to address the UE anomalies. Additionally, the framework incorporates bandwidth-aware route selection and load balancing across multiple routes to improve the service bandwidth for UEs. Simulation results demonstrate that the proposed framework effectively reduces both the number of anomaly UEs and cost compared to existing baselines.

Addressing the pressing requirement for dynamic and intelligent allocation of slicing resources, the dynamic provisioning of resources based on traffic predictions has emerged. Although this method favours proactive scheduling of network slices, more complexities are introduced by the prediction uncertainty. In addition, because multi-tenant networks are always changing in terms of technology and business model, profit-aware network slicing is becoming an important topic of study in the field of resource provision. This paper focuses on profit-aware slicing resource provisioning amid traffic uncertainty in multi-tenancy flexible Ethernet over wavelength division multiplexing networks. Specifically, we develop a profit model for multi-tenant network slicing, accounting for the impact of network prediction uncertainty, and formulate the problem as maximizing the profit of users primarily. To solve this problem, we propose a profit-aware resource provisioning approach that first checks if the slice requests are made by pruning algorithms and then determines the service relationship between slices and tenants by matching games. Simulation results demonstrate the superiority of the proposed algorithm over benchmarks in terms of user profit, total benefit, and denial ratio of service.

Satisfying users' requests based on the service level agreements of network slices is one of the most basic and vital topics of network slicing in 6G networks, and anomaly detection is regarded as a key technique for locating the abnormal status of slices. However, current studies on slice anomaly detection mostly focused on real-time monitoring of slices and ignored the prediction of potential anomalies. Generally, when anomalies trigger, it is hard for slices to adjust the resources in time due to resource competition among physical/virtual nodes. Besides, the resource provisioning strategies can also be optimized when slices are running normally, which is seldom considered when performing slice anomaly detection. To cope with these challenges, in this paper, we are motivated to locate the potential slice anomalies and optimize the resource allocation strategies in a holistic view by learning users' historical behaviors. Specifically, we design a general network architecture, model the process of slice resource provisioning, and formulate the problem as maximizing the long-term system net promoter score (NPS). To solve this problem, we propose a framework to locate the potential slice anomalies and decide the resource allocation strategies simultaneously by predicting the users' future requests and positions. As a result, simulation results demonstrate that our proposed scheme outperforms other baselines in improving the long-term system NPS and reducing the average latency of users.

Network slices are generally coupled with services and face service continuity/unavailability concerns due to the high mobility and dynamic requests from users. Network slice mobility (NSM), which considers user mobility, service migration, and resource allocation from a holistic view, is witnessed as a key technology in enabling network slices to respond quickly to service degradation. Existing studies on NSM either ignored the trigger detection before NSM decision-making or didn't consider the prediction of future system information to improve the NSM performance, and the training of deep reinforcement learning (DRL) agents also faces challenges with incomplete observations. To cope with these challenges, we consider that network slices migrate periodically and utilize the prediction of system information to assist NSM decision-making. The periodical NSM problem is further transformed into a Markov decision process, and we creatively propose a prediction-based federated DRL framework to solve it. Particularly, the learning processes of the prediction model and DRL agents are performed in a federated learning paradigm. Based on extensive experiments, simulation results demonstrate that the proposed scheme outperforms the considered baseline schemes in improving long-term profit, reducing communication overhead, and saving transmission time.

Beyond 5G applications, future 6G services would need to support very large data volumes for emerging industry verticals, such as holographic-type communications, as well as time-sensitive services, e.g., industrial control. Network slicing is the key technology to deliver such customizable services. Slices and their dedicated resources should be provisioned optimally where the services will be run with low network latencies and associated expenses. However, the user dynamics on resource demands within and between slices result in different resource re-allocation triggers, ultimately lead to distinct mobility patterns, e.g., scaling, migration, where sufficient resources must be transferred. Efficient slice mobility requires increasing flexibility in network operation and management to ensure the customized QoS while minimizing the corresponding mobility cost. In this paper, a prediction-based intelligent network analytic is proposed to facilitate the optimized network slice mobility scheme. We will investigate how to utilize the user and network prediction as the auxiliary information to make the slice mobility decision with the objective of maximizing the long-term profits while minimizing the latency and mobility cost. Finally, we evaluate the proposed prediction-based network slice mobility scheme in a simulated environment and compare its performance in terms of system costs, revenues, and profits with two benchmark solutions.

The rapid increase of data traffic has brought great challenges to the maintenance and optimization of 5G and beyond, and some smart critical infrastructures, e.g., small base stations (SBSs) in cellular cells, are facing serious security and failure threats, causing resiliency degradation concerns. Among special smart critical infrastructure failures, the sleeping cell failure is hard to address since no alarm is generally triggered. Sleeping cells can remain undetected for a long time and can severely affect the quality of service/quality of experience to users. To enhance the resiliency of the SBSs in sleeping cells, we design a mobile edge-cloud computing system and propose a semi-supervised learning-based framework to dynamically detect the sleeping cells. Particularly, we consider two indicators, recovery proportion and recovery speed, to measure the resiliency of the SBSs. Moreover, in the proposed scheme, experts’ optimization experience and each period’s detection results can be utilized to iteratively improve the performance. Then we adopt a dataset from real-world networks for performance evaluation. Trace-driven evaluation results demonstrate that the proposed scheme outperforms existing sleeping cell detection schemes, and can also reduce the communication and runtime costs and enhance the resiliency of the SBSs.

With the rapid increase of data in mobile edge computing (MEC) networks, mobile devices (MDs) have been generating many computation-latency-sensitive tasks. As the MDs are limited by resources in terms of storage, computation, and bandwidth, part of tasks have to be offloaded to the edge of mobile networks or the remote cloud for more efficient processing. Hence, task offloading plays a vital role in this scene. Existing works about task offloading mainly aim at one-shot task offloading and rarely consider the dependencies of tasks. In this paper, we focus on minimizing the maximum delay of processing a series of tasks with dependencies in MEC networks, which supports device-to-device communications. Specifically, we consider task offloading under a hybrid scenario with a small base station (SBS) deployed with an edge server (ES) and several MDs which generate several tasks with dependencies. Then we model the tasks to a weighted directed acyclic graph (DAG) and formulate the optimization problem as minimizing the critical path of the weighted DAG. To tackle this NP-hard problem, we propose a heuristic scheme to iteratively optimize the delay of paths of the weighted DAG under the constraints of the ES. To evaluate the proposed scheme, we perform numerical experiments with different numbers of tasks. Simulation results demonstrate that the proposed scheme outperforms other schemes in terms of reducing the system delay and saving the energy consumption of the MDs.

Sleeping cell problem refers to the degradation or unavailability of network services without triggered alarm, which is one of the most critical issues in current mobile networks. This problem is generally not detectable by the operators but only revealed after users’ complaints occur. Therefore, it leads to the degradations of network performance in the service provision in the long run. To address this problem, we introduce a cloud-based sleeping cell detection platform into radio access networks (RANs) to detect the sleeping cells and deal with them automatically. In the cloud RANs (C-RANs), we combine and improve different methods employed in the pioneering studies in this field, and creatively use labeled training data and ensemble learning method for improving the accuracy. Particularly, we utilize expert optimization experience for further improving the detection framework. To evaluate the proposed ensemble learning based sleeping cell detection framework, we use a time-series dataset of Key Performance Indicator (KPI) in a real-world network. Trace-driven evaluation results show that the proposed framework can achieve up to 14.38% and 20.50% improvements compared with two existing schemes, respectively.

It is a hot topic for researchers to boost users retrieval efficiency and satisfactory degree according to a retrievers query results in the existing searchable encryption schemes in cloud computing. Considering that traditional work on searchable encryption rarely refers to the interaction between the user and the cloud, this paper proposes a method to improve the systems usability and users satisfactory degree in the fuzzy keyword search field. In this paper, based on [1] and [2], for the first time we solve the problem of full-scale fuzzy keyword set construction according to the input keyword, and construct the feedback scheme to produce pointer vector including fuzzy keyword, edit distance and keywords dynamic score, which is feasible in hybrid cloud model. Thus, different vectors form the character vector database within its data structure. It can go to the trapdoor construction procedure after access to the database with its edit distances to construct fuzzy keyword set, which makes the fullest use of the retrieval history and statistical misspelled keywords, precisely and quickly realizing the aim of ranked fuzzy keyword search over cloud encrypted data. Thorough rigorous security analyses realize privacy preservation, as well as improvement of the solution which can meet satisfaction needs of users. And experiments show that efficiency and precision are clearly achieved in the proposed solution, the retrieval time overhead is improved after many times as well.