Probability Of Requests Research Articles

On the design of cost minimization for D2D‐enabled wireless caching networks: A joint recommendation, caching, and routing perspective

AbstractCache‐enabled device‐to‐device (D2D) network has been deemed as an effective technique to offload the data traffic. However, the gain of the caching schemes is closely related to the homogeneity among users' preference distribution. To tackle this issue, recommendation is a promising proactive approach. It increases the request probability of recommended contents, reshaping users' contents demand patterns, and improving caching performance. Moreover, considering the heterogeneous network settings, i.e. content retrieval costs vary, the routing design becomes a non‐negligible factor on caching performance optimization. On these grounds, the average system cost of D2D‐enabled wireless caching networks with multiple BSs is first described. Then the routing strategies are designed together with caching and recommendation policies by minimizing the average cost of these networks. The optimization problem is proven as NP‐hard. To facilitate the analysis, the original problem is decoupled into two sub‐problems and solve them respectively. Afterwards, all the variables are optimized in an alternating manner until the convergence is achieved. The proposed algorithm's convergence performance and benefits over benchmark strategies in terms of total cost and cache hit ratio are supported by Monte‐Carlo simulation results.

Topology Abstraction-Based Routing Scheme for Secret-Key Provisioning in Hybrid GEO/LEO Quantum Satellite Networks.

Quantum key distribution (QKD) is a promising technique to resist the threat against quantum computers. However, the high loss of quantum signals over a long-distance optical fiber is an obstacle for QKD in the intercontinental domain. In this context, the quantum satellite network is preferred over the terrestrial quantum optical network. Due to the mobility of satellites, the satellite topology is dynamic in the quantum satellite network, which remains a challenge for routing. In hybrid geostationary-earth-orbit (GEO)/low-earth-orbit (LEO) quantum satellite networks, the lack of an efficient scheduling scheme for GEO/LEO satellites also limits the construction of quantum satellite networks. Therefore, this paper provides a topology abstraction-based routing scheme for secret-key provisioning, where the dynamic physical topology is translated into a quasi-static abstracted topology. This scheme contributes to saving the precious secret key resources. In order to improve the success probability of long-distance QKD requests, three novel resource-scheduling heuristic algorithms are proposed in hybrid GEO/LEO quantum satellite networks. Simulation results indicate that the proposed algorithms can improve the success probability of QKD requests by 47% compared to the benchmark.

Revenue Maximization: The Interplay Between Personalized Bundle Recommendation and Wireless Content Caching

In this paper, we explore the interplay between personalized bundle recommendation and cache decision on the performance of wireless edge caching networks. A revenue maximization perspective is provided. To this end, we first examine the quantitative impact of bundle recommendation on the content request probability of different users. We then specify the definition of system revenue, showing its dependence on bundle recommendation and caching policies. With that, a joint bundling, caching and recommendation decision problem is formulated to maximize the achievable system revenue, taking into account the constraints of user-distinguished recommendation quality, recommendation amount, and the cache capacity budget. To solve this non-tractable optimization problem, a divide-then-conquer methodology is adopted. Specifically, we first determine the bundle state per user, on which basis we perform the joint bundle recommendation and caching decision-making, wherein several bundling strategies with different time-complexity are devised. Last but not least, we provide detailed properties analysis for our proposed bundling and joint optimization algorithms. Comprehensive numerical simulations validate the performance enhancement of the designed solutions compared to extensive conventional single-item recommendation oriented benchmarks.

Deep reinforcement learning‐based routing and resource assignment in quantum key distribution‐secured optical networks

AbstractIn quantum key distribution‐secured optical networks (QKD‐ONs), constrained network resources limit the success probability of QKD lightpath requests (QLRs). Thus, the selection of an appropriate route and the efficient utilisation of network resources for establishment of QLRs are the essential and challenging problems. This work addresses the routing and resource assignment (RRA) problem in the quantum signal channel of QKD‐ONs. The RRA problem of QKD‐ONs is a complex decision making problem, where appropriate solutions depend on understanding the networking environment. Motivated by the recent advances in deep reinforcement learning (DRL) for complex problems and also because of its capability to learn directly from experiences, DRL is exploited to solve the RRA problem and a DRL‐based RRA scheme is proposed. The proposed scheme learns the optimal policy to select an appropriate route and assigns suitable network resources for establishment of QLRs by using deep neural networks. The performance of the proposed scheme is compared with the deep‐Q network (DQN) method and two baseline schemes, namely, first‐fit (FF) and random‐fit (RF) for two different networks, namely The National Science Foundation Network (NSFNET) and UBN24. Simulation results indicate that the proposed scheme reduces blocking by 7.19%, 10.11%, and 33.50% for NSFNET and 2.47%, 3.20%, and 19.60% for UBN24 and improves resource utilisation up to 3.40%, 4.33%, and 7.18% for NSFNET and 1.34%, 1.96%, and 6.44% for UBN24 as compared with DQN, FF, and RF, respectively.

Dispatcher I-queue Optimization in Join Idle Queue Algorithm for Improved Load Balancing in Cloud Computing

Objectives: Modern data centers serving web and mobile applications employ distributed load balancers. The Join Idle Queue (JIQ) algorithm is ideally suited for load balancing in a distributed setup. It attains fast response time by directing service requests to idle servers which can immediately process them. However, JIQ is not optimal in tracking idle servers leaving room for improvement.
 Methods: We observed that JIQ assigns idle servers non-uniformly to load balancers leaving some load balancers with no access to idle servers. We propose a variant of the JIQ algorithm, Join Idle Queue Dispatcher I-queue Optimization (JIQ-DIO), which increases the probability of load balancers having access to idle servers without additional communication overhead leading to improved response time.
 Findings: We simulated JIQ-DIO on CloudSim Plus 3.0 and compared it with standard JIQ and its different variants. JIQ-DIO was found to increase the probability of incoming requests being directed to idle servers and lead to more than two-fold improvement in response time across a broad range of parameters.

2-D fragmentation-aware RMSA based on the relative flexibility of advance reservation requests in elastic optical networks

With the advantages of fine granularity spectrum allocation (SA) and large capacity, elastic optical networks (EONs) are considered as the most promising development direction of optical network in the future. The emergence of two-dimensional (2-D) fragmentation during dynamic SA of EONs not only reduces the availability of resources but also increases the blocking probability of requests. In addition, the advance reservation (AR) request has the characteristics of resource reservation and time flexibility, which further complicates the fragmentation issue. To solve the above issues, in this paper, a fragmentation-aware routing, modulation and spectrum assignment (RMSA) algorithm for AR requests is proposed. First, delayed resource allocation is developed for AR requests. Second, a resource allocation sorting strategy based on the relative flexibility of requests is proposed. The strategy makes full use of the time characteristics of AR request to determine the allocation order of requests. In addition, from the perspective of the impact on adjacent resources, a 2-D path resource model is introduced to evaluate the impact of 2-D fragmentation from the perspective of resource matching degree and resource connectivity. Moreover, simulation results show that the proposed algorithm is better than the benchmark algorithm in terms of blocking probability, resource utilization and fragmentation rate.

Deep Reinforcement Learning for Reactive Content Caching With Predicted Content Popularity in Three-Tier Wireless Networks

With the explosive growth of micro-video applications, the mobile traffic generated by retrieving a few user-generated micro-videos has brought a massive burden to backhaul links and backbone networks. Unlike other types of videos, user-generated micro-videos are typically requested by a large number of users within an extremely short period after their release. Therefore, it is crucial to predict the content popularity and make caching decision for the newly requested content timely. To predict content popularity in different locations, the request probabilities of the content in different locations are translated into rating scores. Then, a recommendation system-based prediction model is designed to predict rating scores of the newly requested content in different locations. To increase caching diversity and realize vertically collaboration in a three-tier wireless network, a deep reinforcement learning-based reactive content caching strategy is proposed to make caching decision for the newly requested content. The main goal is to obtain a higher caching gain at a lower caching space cost. To evaluate the caching performance, a new metric, cache benefit rate, is defined as the download latency reduction brought by each bit of cache. Compared with matrix factorization, the performance of prediction model can be increased by 23.98% and 12.44% in terms of mean absolute error and root mean square error, respectively. Extensive simulations demonstrate that the proposed reactive caching strategy outperforms other caching strategies under different system parameters in terms of the cache hit rate, average download time, and cache benefit rate.

Uplink Channel Allocation Scheme and QoS Management Mechanism for Cognitive Cellular-Femtocell Networks

Cognitive radio and femtocell are promising technologies which can satisfy the requirements of future mobile communications in terms of dynamic spectrum sharing and high user density areas. Providing quality-of-service (QoS) guaranteed realtime services is challenging issue of future cognitive cellular-femtocell mobile networks. In this paper, we introduce a user’s QoS management mechanism used to protect SINR of macro users from QoS violation caused by femtocell users. We design a novel uplink channel allocation scheme (denoted as “flexible scheme”) for real-time connections. The scheme uses the information of interference level and channel occupancy collected at cognitive femtocell access points and their covering macro base station (MBS) and apply relevant selection criteria to select an appropriate channel which causes the minimums interference to macro users of the covering MBS. Performance results prove that comparing with femtocell-access-point (FAP)-based and MBS-based uplink channel allocation schemes, the novel “flexible scheme” can provide lower unsuccessful probability of new connection requests.

QoS-Aware routing, space, and spectrum assignment in space division multiplexing networks

Nowadays, Space Division Multiplexing (SDM) is one of the solutions proposed to increase the capacity and optimal allocation of resources in Elastic Optical Networks (EON). The issue of allocating resources in these networks involves the optimal allocation of route, space and spectrum for the establishment of traffic requests. The quality of service (QoS) provided for establishing traffic requests must vary according to their importance and priority, so that high quality requests require guaranteed bandwidth and higher levels of service than low quality requests. Hence, reducing the blocking probability of high-quality data traffic is one of the most important goals in networks. In this paper, three new QoS-aware algorithms are proposed for route, space and spectrum assignment of dynamic traffic in SDM-EONs that reduce the blocking probability of high-quality requests as follows: Reservation without Common Core (RwoC), Reservation with One Common Core (RwC), and Reservation and Recoring with One Common Core (RRwC). The results of utilizing these algorithms and also adding distance adaptive modulation format show that the blocking rate of high-quality requests is decreased and the general spectrum utilization is increased specially in RRwC algorithm. Of course, this decrease causes a slight increase in the blocking rate of low-quality requests. Another advantage of using the RRwC algorithm is that using the slots with the same index in the recoring method reduces costs because in this case there is no need to change the frequency of transceivers. Since, this algorithm is more cost-effective. In the first proposed algorithm, dedicated cores are designed to create high quality traffic. In the second algorithm, in addition to dedicated cores, a common core is also used between high and low-quality traffic. Finally, the recoring method has been used for high quality traffic to further reduce their blocking rates.

IIoT Data Sharing Based on Blockchain: A Multileader Multifollower Stackelberg Game Approach

The evolution of the Industrial Internet of Things (IIoTs) greatly increases the volume of data generated by the connected IIoT devices. IIoT data are playing an increasingly important role in various industrial sectors. IIoT data sharing helps enterprises make better production decisions and respond to market changes timely. However, the distrust among IIoT entities and IIoT entities’ distrust of data-sharing platforms may hinder the realization of data sharing. In this article, a decentralized IIoT data-sharing scheme based on blockchain and edge computing is proposed. A Proof of Storage and Transmission (PoST) consensus mechanism is proposed to meet data storage and transmission requirements of data owners in IIoT data-sharing networks. Based on the manufacture ties of data owners, shared data request probabilities are derived. The IIoT data sharing interactions between data owners and edge devices are modeled as a multiple-leader and multiple-follower Stackelberg game. The alternating direction method of multipliers (ADMMs) algorithm is used to obtain the optimal IIoT data sharing solutions in a distributed manner. Simulation results show that compared with the cooperative scheme, the total profit of edge devices is maximally increased by 59%, and the total utility of data owners is maximally increased by 52%.

Routing and secret key assignment for secure multicast services in quantum satellite networks

Multicast is an important service type in both terrestrial and space communication networks but has security issues because more than one destination node can receive information. Point-to-point quantum key distribution can be achieved end-to-end over long distances by using a quantum satellite (e.g., Micius) as a relay node. Hence, how to provide secret keys for remote multicast services and efficiently distribute secret keys between the source node and multiple destination nodes for long-distance secure multicast services (SMSs) has become a critical problem. In this paper, a quantum satellite network with a special node structure is designed to complete point-to-multipoint key distribution, and a point-to-multipoint satellite relay scheme is proposed. The weight information of the link is dynamically described by the method of auxiliary topology, and a satellite relay tree is constructed. Finally, this paper proposes a satellite relay algorithm for multicast services to route and allocate key resources to solve the problem of long-distance point-to-multipoint key relay and improve the success probability of multicast service requests. Simulation results show that the proposed algorithm can improve the success probability of SMS requests by about 51.32% in a static scenario and 17.33% in a dynamic scenario.

Virtual optical network embedding of time-varying traffic in elastic optical networks

Network virtualization can enhance the flexibility of Internet infrastructure resources and the scalability of the substrate elastic optical networks (EONs) through the coexistence of multiple virtual optical networks supporting different services. Traditional virtual optical network embedding mostly assumed fixed embedding, that is, once a virtual optical network (VON) is embedded, the network resources allocated to it are almost unchanged during its lifetime. However, the actual traffic demand on a VON usually changes over time. To address the problems of high bandwidth blocking probability and low spectrum utilization caused by the mismatch between the resources allocated and the required resources during the virtual optical network embedding of the time-varying traffic in the elastic optical networks (EONs), we propose a virtual optical network embedding algorithm of the time-varying traffic (TVT-VONE). Firstly, in the node embedding stage, a node weight value measurement is designed based on the attributes of virtual nodes and physical nodes. Secondly, in the virtual optical link embedding stage, a measurement formula for path spectrum resource richness is designed to decrease link embedding failure due to insufficient spectrum resources on the path. Finally, in order to adapt to the characteristics of dynamic changes in bandwidth requirements of time-varying traffic, we design a resource-aware virtual network reconfiguration strategy when the spectrum resources of traffic required do not match the resources allocated to the changed traffic. Simulation results show that the proposed algorithm can effectively reduce the bandwidth blocking probability of VON requests and improve spectrum utilization of EONs.

LSTM-Based Path Prediction for Effective Sensor Filtering in Sensor Registry System.

The Internet of Things (IoT) is expected to provide intelligent services by receiving heterogeneous data from ambient sensors. A mobile device employs a sensor registry system (SRS) to present metadata from ambient sensors, then connects directly for meaningful data. The SRS should provide metadata for sensors that may be successfully connected. This process is location-based and is also known as sensor filtering. In reality, GPS sometimes shows the wrong position and thus leads to a failed connection. We propose a dual collaboration strategy that simultaneously collects GPS readings and predictions from historical trajectories to improve the probability of successful requests between mobile devices and ambient sensors. We also update the evaluation approach of sensor filtering in SRS by introducing a Monte Carlo-based simulation flow to measure the service provision rate. The empirical study shows that the LSTM-based path prediction can compensate for the loss of location abnormalities and is an effective sensor filtering model.

Power and Rate Adaptive Pushing Over Fading Channels

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

Caching Efficiency Maximization for Device-to-Device Communication Networks: A Recommend to Cache Approach

Edge side caching assisted device-to-device (D2D) communication has been acknowledged as a promising technique to alleviate the heavy burden of backhaul transmission link and to reduce the network latency. However, the effectiveness of caching strategies at the network edge is highly dependent on the distribution of individual user’s content preference. To fully attain the benefits of edge caching, some proactive mechanisms shall be considered. Among which, recommendation performs noticeably well due to its capability of reshaping the content request probabilities of different users, which in turn affects the cache decision significantly. In this work, we quantitatively investigate how recommendation can be applied to enhance the caching efficiency of D2D enabled wireless content caching networks. And for that, the cache hit ratio maximization problem for a generic network model is formulated taking into account the requirements of each user’s personalized recommendation quality, recommendation quantity and cache capacity. Then, we show that the optimal recommendation and caching policies which jointly maximize the cache efficiency is NP-hard to compute. Further, a time-efficient sub-optimal algorithm is designed, which works in an iterative manner and has provable convergence guarantee as well as polynomial time complexity. Monte-Carlo simulation results demonstrate the convergence performance of our proposed joint decision algorithm and its cache efficiency improvements compared to extensive benchmarks.

Delayed spectrum allocation in elastic optical networks with anycast traffic

We investigate the problem of dynamic routing and spectrum assignment for advance reservation anycast requests using a novel algorithm called Delayed Spectrum Allocation (DSA) which delays allocation of resources until immediately before transmission begins. We evaluate the flexible window STSD (demands that specify start-time and duration) to further improve request blocking. Through extensive simulation results, we show that anycast with flexible STSD using DSA approach can significantly reduce blocking probability of anycast and unicast requests compared to traditional Immediate Spectrum Allocation (ISA). The improvement is up to 50 percent in unicast traffic and even higher in anycasting. We also propose balanced routing for anycast algorithms to reduce the blocking.

RMSA algorithms resilient to multiple node failures in dynamic EONs

In Elastic Optical Networks (EONs), the way different service demands are supported in the network is ruled by the Routing, Modulation and Spectrum Assignment (RMSA) algorithm, which decides how the spectrum resources of the optical network are assigned to each service demand. In a dynamic EON, demand requests arrive randomly one at a time and the accepted demands last in the network for a random time duration. So, one important goal of the RMSA algorithm is the efficient use of the spectrum resources to maximize the acceptance probability of future demand requests. On the other hand, multiple failure events are becoming a concern to network operators as such events are becoming more frequent in time. In this work, we consider the case of multiple node failure events caused by malicious attacks against network nodes. In order to obtain RMSA algorithms resilient to such events, a path disaster availability metric was recently proposed which takes into account the probability of each path not being disrupted by an attack. This metric was proposed in the offline variant of the RMSA problem where all demands are assumed to be known at the beginning. Here, we exploit the use of the path disaster availability metric in the RMSA of dynamic EONs. In particular, we propose RMSA algorithms combining the path disaster availability metric with spectrum usage metrics in a dynamic way based on the network load level. The aim is that the efficient use of the resources is relaxed for improved resilience to multiple node failures when the EON is lightly loaded, while it becomes the most important goal when the EON becomes heavily loaded. We present simulation results considering a mix of unicast and anycast services in 3 well-known topologies. The results show that the RMSA algorithms combining the path disaster availability metric with spectrum usage metrics are the best trade-off between spectrum usage efficiency and resilience to multiple node failures.

Android mobile malware detection using fuzzy AHP

Android mobile is very challenging because it is an open-source operating system that is also vulnerable to attacks. Previous studies have shown various mobile malware detection methods to overcome this problem, but still, there is room for improvement. Mobile users mostly ignore long lists of permissions because these are difficult to understand. Therefore, to distinguish benign or malware applications and the probability of each permission request is understood, it is necessary to evaluate Android mobile applications. This research proposed a multi-criteria decision-making based (MCDM) mobile malware detection system using a risk-based fuzzy analytical hierarchy process (AHP) approach to evaluate the Android mobile application. This study focuses on static analysis, that uses permission-based features to assess the mobile malware detection system approach. Risk analysis is applied to increase the awareness of the mobile user in granting any permission request to contain a high-risk level. The evaluation used 10,000 samples taken from Drebin and AndroZoo. The results show a high accuracy rate of 90.54% values that can effectively classify the Android application into four different risk levels.

Association between epidural analgesia and indications for intrapartum caesarean delivery in group 1 of the 10-group classification system at a tertiary maternity hospital, Shanghai, China: a retrospective cohort study

BackgroundIn this study, we aimed to determine whether epidural analgesia affects the indications for intrapartum caesarean delivery, such as foetal distress, dystocia, or maternal request, in nulliparous term women with spontaneous labour (Group 1 in the 10-Group Classification System).MethodsWe conducted a retrospective cohort study and collected data from the electronic medical records of deliveries performed in our institution between 1 January 2017 and 30 June 2017. Women conforming to the criterion of Group 1 according to the 10-Group Classification System were enrolled. We compared labour outcomes between women with and without epidural analgesia and analysed the association between epidural analgesia and indications for caesarean by using multivariate logistic regression analysis.ResultsA total of 3212 women met the inclusion criteria, and 2876 were enrolled in the final analyses. Women who received epidural analgesia had a significantly lower intrapartum caesarean delivery rate (16.0% vs. 26.7%, P < 0.001), higher rates of amniotomy (53.4% vs. 42.3%, P < 0.001) and oxytocin augmentation (79.5% vs. 67.0%, P < 0.001), and a higher incidence of intrapartum fever (≥38 °C) (23.3% vs. 8.5%, P < 0.001) than those who did not receive epidural analgesia. There were no significant differences between the groups for most indications, except a lower probability of maternal request for caesarean delivery (3.9% vs. 10.5%, P < 0.001) observed in women who received epidural analgesia than in those who did not. Epidural analgesia was revealed to be associated with a decreased risk of maternal request for caesarean delivery (adjusted odds ratio [aOR], 0.30; 95% confidence interval [CI], 0.22–0.42; P < 0.001); however, oxytocin augmentation was related to an increased risk of maternal request (aOR, 2.34; 95%CI, 1.47–3.75; P < 0.001). Regarding the reasons for the maternal request for caesarean delivery, significantly fewer women complained of pain (0.5% vs. 4.6%, P < 0.001) or had no labour progress (1.3% vs. 3.6%, P < 0.001) among those who received analgesia.ConclusionsAmong the women in Group 1, epidural analgesia was associated with a lower intrapartum caesarean delivery rate, which may be explained by a reduction in the risk of maternal request for an intrapartum caesarean delivery.

Multi-path-based quasi-real-time key provisioning in quantum-key-distribution enabled optical networks (QKD-ON).

With its information-theoretic security, quantum-key-distribution-enabled optical networks (QKD-ON) have become a promising candidate for future optical networks. The concept of quantum key pool (QKP) was introduced to offer an effective strategy for storing quantum keys. However, with the loss on its theoretical security due to storing these keys, balancing the storage of quantum keys and the security requirements of QKD-ONs poses a major challenge in their practical deployments. Hence, in this paper a concept of quasi-real-time key provisioning (QRT-KP) is introduced to address the tradeoff between quantum key storage and the degree of security. To satisfy the practical deployment of QRT-KP and the requirement of high-traffic flow, we propose a multi-path based QRT-KP (MP-QRT-KP) algorithm. Simulation results show that the MP-QRT-KP effectively enhances the performance of QKD-ONs in different scenarios, and it turns out that the algorithm performs better than single-path based QRT-KP (SP-QRT-KP) in terms of the success probability of key-allocation requests and key-resources utilization.