Admission Control Optimisation for QoS and QoE Enhancement in Future Networks

Recent exponential growth in demand for traffic heterogeneity support and the number of associated devices has considerably increased demand for network resources and induced numerous challenges for the networks, such as bottleneck congestion, and inefficient admission control and resource allocation. Challenges such as these degrade network Quality of Service (QoS) and user-perceived Quality of Experience (QoE). This work studies admission control from various perspectives. For example, two novel single-objective optimisation-based admission control models, Dynamica Slice Allocation and Admission Control (DSAAC) and Signalling and Admission Control (SAC), are presented to enhance future limited-capacity network Grade of Service (GoS), and for control signalling optimisation, respectively. DSAAC is an integrated model whereby a cost-estimation function based on user demand and network capacity quantifies resource allocation among users. Moreover, to maximise resource utility, adjustable minimum and maximum slice resource bounds have also been derived. In the case of user blocking from the primary slice due to congestion or resource scarcity, a set of optimisation algorithms on inter-slice admission control and resource allocation and adaptability of slice elasticity have been proposed. A novel SAC model uses an unsupervised learning technique (i.e. Ranking-based clustering) for optimal clustering based on users’ homogeneous demand characteristics to minimise signalling redundancy in the access network. The redundant signalling reduction reduces the additional burden on the network in terms of unnecessary resource utilisation and computational time. Moreover, dynamically reconfigurable QoE-based slice performance bounds are also derived in the SAC model from multiple demand characteristics for clustered user admission to the optimal network. A set of optimisation algorithms are also proposed to attain efficient slice allocation and users’ QoE enhancement via assessing the capability of slice QoE elasticity. An enhancement of the SAC model is proposed through a novel multi-objective optimisation model named Edge Redundancy Minimisation and Admission Control (E-RMAC). A novel E-RMAC model for the first time considers the issue of redundant signalling between the edge and core networks. This model minimises redundant signalling using two classical unsupervised learning algorithms, K-mean and Ranking-based clustering, and maximises the efficiency of the link (bandwidth resources) between the edge and core networks. For multi-operator environments such as Open-RAN, a novel Forecasting and Admission Control (FAC) model for tenant-aware network selection and configuration is proposed. The model features a dynamic demand-estimation scheme embedded with fuzzy-logic-based optimisation for optimal network selection and admission control. FAC for the first time considers the coexistence of the various heterogeneous cellular technologies (2G, 3G,4G, and 5G) and their integration to enhance overall network throughput by efficient resource allocation and utilisation within a multi-operator environment. A QoS/QoE-based service monitoring feature is also presented to update the demand estimates with the support of a forecasting modifier. he provided service monitoring feature helps resource allocation to tenants, approximately closer to the actual demand of the tenants, to improve tenant-acquired QoE and overall network performance. Foremost, a novel and dynamic admission control model named Slice Congestion and Admission Control (SCAC) is also presented in this thesis. SCAC employs machine learning (i.e. unsupervised, reinforcement, and transfer learning) and multi-objective optimisation techniques (i.e. Non-dominated Sorting Genetic Algorithm II ) to minimise bottleneck and intra-slice congestion. Knowledge transfer among requests in form of coefficients has been employed for the first time for optimal slice requests queuing. A unified cost estimation function is also derived in this model for slice selection to ensure fairness among slice request admission. In view of instantaneous network circumstances and load, a reinforcement learning-based admission control policy is established for taking appropriate action on guaranteed soft and best-effort slice requests admissions. Intra-slice, as well as inter-slice resource allocation, along with the adaptability of slice elasticity, are also proposed for maximising slice acceptance ratio and resource utilisation. Extensive simulation results are obtained and compared with similar models found in the literature. The proposed E-RMAC model is 35% superior at reducing redundant signalling between the edge and core networks compared to recent work. The E-RMAC model reduces the complexity from O(U) to O(R) for service signalling and O(N) for resource signalling. This represents a significant saving in the uplink control plane signalling and link capacity compared to the results found in the existing literature. Similarly, the SCAC model reduces bottleneck congestion by approximately 56% over the entire load compared to ground truth and increases the slice acceptance ratio. Inter-slice admission and resource allocation offer admission gain of 25% and 51% over cooperative slice- and intra-slice-based admission control and resource allocation, respectively. Detailed analysis of the results obtained suggests that the proposed models can efficiently manage future heterogeneous traffic flow in terms of enhanced throughput, maximum network resources utilisation, better admission gain, and congestion control

Black Gold\u27s Price Plunge: Are Conventional and Islamic Banks Equally Vulnerable?

Regarding the vulnerability of the banking industry to oil price plunges, we investigate the effects of oil price declines on credit and insolvency risks for the banking industry within specific bank specializations (conventional, Islamic, and conventional banks with Islamic windows), from 2000 through 2016, at both the aggregate and country levels in the Gulf Cooperation Council (GCC). Our findings show that falling oil prices significantly increase the credit risk for the banking industry, particularly for banks operating in Kuwait, Qatar, Saudi Arabia and the United Arab Emirates. Commercial banks with Islamic windows are also prone to oil price shocks. However, falling oil prices do not affect the credit risk of Islamic banks. Utilizing accounting-based and marked-based proxies for the insolvency risk, our analysis shows that oil price plunges do not increase the insolvency risk of the banking industry or bank specializations. We argue that bailout packages given by the wealth funds to GCC banks is a probable cause for counter intuitive results with respect to solvency risk. Our research findings will be of interest to various stakeholders, particularly the regulators who look for empirical evidence to develop deeper insights to the sound functioning of the banking systems

Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks

Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN

Comparative Germination of Barley Seeds ( Hordeum Vulgare ) Soaked in Alkaline Media and Effects on Starch and Soluble Proteins

Barley seeds ( Hordeum Vulgare ) were germinated after soaking in different alkaline solutions of varied concentrations and pH, at room temperature of 25\ub0C. The rate of germination after 48 hours of soaking of the seeds in distilled water was found to be 35% and the rate for the seeds soaked in the solutions of Ca (OH)2 , KOH and Mg(OH)2 was observed as 60, 66 and 62% respectively. Where-as the rate of germination for the solutions of NaOH and NaHCO3 remained the same as that of the water. The influence in length of rootlets was also examined as a function of the nature of the soaking solutions. Sharp increase in the length was observed in case of Mg (OH)2 and KOH while in NaOH, Ca(OH)2 and NaHCO3 increase in rootlets length was found insignificant . Variation of starch and soluble protein contents in soaked solutions were also examined. Starch and soluble protein contents were found to be the highest in NaOH soaked seeds as 57.7 and 5.95% respectively, compared to 45.07 and 2.50 % for the seeds soaked in water

Admission Control Optimisation for QoS and QoE Enhancement in Future Networks

Recent exponential growth in demand for traffic heterogeneity support and the number of associated devices has considerably increased demand for network resources and induced numerous challenges for the networks, such as bottleneck congestion, and inefficient admission control and resource allocation. Challenges such as these degrade network Quality of Service (QoS) and user-perceived Quality of Experience (QoE). This work studies admission control from various perspectives. For example, two novel single-objective optimisation-based admission control models, Dynamica Slice Allocation and Admission Control (DSAAC) and Signalling and Admission Control (SAC), are presented to enhance future limited-capacity network Grade of Service (GoS), and for control signalling optimisation, respectively. DSAAC is an integrated model whereby a cost-estimation function based on user demand and network capacity quantifies resource allocation among users. Moreover, to maximise resource utility, adjustable minimum and maximum slice resource bounds have also been derived. In the case of user blocking from the primary slice due to congestion or resource scarcity, a set of optimisation algorithms on inter-slice admission control and resource allocation and adaptability of slice elasticity have been proposed. A novel SAC model uses an unsupervised learning technique (i.e. Ranking-based clustering) for optimal clustering based on users’ homogeneous demand characteristics to minimise signalling redundancy in the access network. The redundant signalling reduction reduces the additional burden on the network in terms of unnecessary resource utilisation and computational time. Moreover, dynamically reconfigurable QoE-based slice performance bounds are also derived in the SAC model from multiple demand characteristics for clustered user admission to the optimal network. A set of optimisation algorithms are also proposed to attain efficient slice allocation and users’ QoE enhancement via assessing the capability of slice QoE elasticity. An enhancement of the SAC model is proposed through a novel multi-objective optimisation model named Edge Redundancy Minimisation and Admission Control (E-RMAC). A novel E-RMAC model for the first time considers the issue of redundant signalling between the edge and core networks. This model minimises redundant signalling using two classical unsupervised learning algorithms, K-mean and Ranking-based clustering, and maximises the efficiency of the link (bandwidth resources) between the edge and core networks. For multi-operator environments such as Open-RAN, a novel Forecasting and Admission Control (FAC) model for tenant-aware network selection and configuration is proposed. The model features a dynamic demand-estimation scheme embedded with fuzzy-logic-based optimisation for optimal network selection and admission control. FAC for the first time considers the coexistence of the various heterogeneous cellular technologies (2G, 3G,4G, and 5G) and their integration to enhance overall network throughput by efficient resource allocation and utilisation within a multi-operator environment. A QoS/QoE-based service monitoring feature is also presented to update the demand estimates with the support of a forecasting modifier. he provided service monitoring feature helps resource allocation to tenants, approximately closer to the actual demand of the tenants, to improve tenant-acquired QoE and overall network performance. Foremost, a novel and dynamic admission control model named Slice Congestion and Admission Control (SCAC) is also presented in this thesis. SCAC employs machine learning (i.e. unsupervised, reinforcement, and transfer learning) and multi-objective optimisation techniques (i.e. Non-dominated Sorting Genetic Algorithm II ) to minimise bottleneck and intra-slice congestion. Knowledge transfer among requests in form of coefficients has been employed for the first time for optimal slice requests queuing. A unified cost estimation function is also derived in this model for slice selection to ensure fairness among slice request admission. In view of instantaneous network circumstances and load, a reinforcement learning-based admission control policy is established for taking appropriate action on guaranteed soft and best-effort slice requests admissions. Intra-slice, as well as inter-slice resource allocation, along with the adaptability of slice elasticity, are also proposed for maximising slice acceptance ratio and resource utilisation. Extensive simulation results are obtained and compared with similar models found in the literature. The proposed E-RMAC model is 35% superior at reducing redundant signalling between the edge and core networks compared to recent work. The E-RMAC model reduces the complexity from O(U) to O(R) for service signalling and O(N) for resource signalling. This represents a significant saving in the uplink control plane signalling and link capacity compared to the results found in the existing literature. Similarly, the SCAC model reduces bottleneck congestion by approximately 56% over the entire load compared to ground truth and increases the slice acceptance ratio. Inter-slice admission and resource allocation offer admission gain of 25% and 51% over cooperative slice- and intra-slice-based admission control and resource allocation, respectively. Detailed analysis of the results obtained suggests that the proposed models can efficiently manage future heterogeneous traffic flow in terms of enhanced throughput, maximum network resources utilisation, better admission gain, and congestion control

Dynamically Reconfigurable Slice Allocation and Admission Control within 5G Wireless Networks.

Serving heterogeneous traffic demand requires efficient resource utilization to deliver the promises of 5G wireless network towards enhanced mobile broadband, massive machine type communication and ultra-reliable low-latency communication. In this paper, an integrated user application-specific demand characteristics as well as network characteristics evaluation based online slice allocation model for 5G wireless network is proposed. Such characteristics include, available bandwidth, power, quality of service demand, service priority, security sensitivity, network load, predictive load etc. A degree of intra-slice resource sharing elasticity has been considered based on their availability. The availability has been assessed based on the current availability as well as forecasted availability. On the basis of application characteristics, an admission control strategy has been proposed. An interactive AMF (Access and Mobility Function)-RAN (Radio Access Network) information exchange has been assumed. A cost function has been derived to quantify resource allocation decision metric that is valid for both static and dynamic nature of user and network characteristics. A dynamic intra-slice decision boundary estimation model has been proposed. A set of analytical comparative results have been attained in comparison to the results available in the literature. The results suggest the proposed resource allocation framework performance is superior to the existing results in the context of network utility, mean delay and network grade of service, while providing similar throughput. The superiority reported is due to soft nature of the decision metric while reconfiguring slice resource block-size and boundaries

Hamstring tightness among individuals with neck and low back pain: a cross-sectional study in a public sector institute of Karachi

Objective: To determine the frequency of hamstring tightness and its impact among patients with chronic neck and low-back pain. Method: The analytical, cross-sectional study was conducted at the outpatient department of the Sindh Institute of Physical Medicine and Rehabilitation, Karachi, from September 10, 2021, to January 31, 2022, and comprised patients of either gender aged 18-40 years with non-specific cervical and lumbar pain for more than 3 months. The participants were divided into 2 groups. Group A included those with chronic neck pain and group B included participants with chronic low-back pain. Clinical assessment was done to measure hamstring tightness and pain by using the active knee extension test and the visual analogue scale, respectively. Data was analysed using SPSS 24. Results: Out of 104 participants, there were 52(50%) males and as many females. The overall mean age was 28.155.10years. There were 52(50%) subjects in each of the two groups. Hamstring tightness was found in 73(70.2%) subjects. Patients with chronic low-back pain reported more tightness of hamstring muscle 38(73.1%) than those with chronic neck pain 35(67.3%) (p>0.05). Conclusion: Hamstring tightness was frequent among patients with chronic neck pain and low-back pain though not significantly. Key Words: Lumbar, Muscle, Neck pain, Pain intensity

Comparative Germination of Barley Seeds ( Hordeum Vulgare ) Soaked in Alkaline Media and Effects on Starch and Soluble Proteins

Barley seeds ( Hordeum Vulgare ) were germinated after soaking in different alkaline solutions of varied concentrations and pH, at room temperature of 25°C. The rate of germination after 48 hours of soaking of the seeds in distilled water was found to be 35% and the rate for the seeds soaked in the solutions of Ca (OH)2 , KOH and Mg(OH)2 was observed as 60, 66 and 62% respectively. Where-as the rate of germination for the solutions of NaOH and NaHCO3 remained the same as that of the water. The influence in length of rootlets was also examined as a function of the nature of the soaking solutions. Sharp increase in the length was observed in case of Mg (OH)2 and KOH while in NaOH, Ca(OH)2 and NaHCO3 increase in rootlets length was found insignificant . Variation of starch and soluble protein contents in soaked solutions were also examined. Starch and soluble protein contents were found to be the highest in NaOH soaked seeds as 57.7 and 5.95% respectively, compared to 45.07 and 2.50 % for the seeds soaked in water