Resource Congestion Research Articles

Synergistic optimization strategy for beamforming and power allocation in dual-functional radar-communication systems

The dual-functional radar-communication (DFRC) integrated system presents an ideal solution to address the challenge of spectrum resource congestion in future networks. This paper explores an adaptive power allocation technique based on beamforming to enhance the word error probability (WEP) performance of the DFRC system. Initially, a joint optimization model is developed to minimize the WEP while adhering to constraints on radar signal-to-interference-noise ratio (SINR), peak-to-average-power ratio, sidelobe level, and total transmit power. This model incorporates dual-function transmit beam, radar, and communication receive beam patterns. Subsequently, the proposed subproblem convex relaxation alternating update (SCRAU) algorithm is introduced to achieve a locally optimal solution for multi-carrier power allocation. This algorithm decomposes the original non-convex optimization problem into three sub-problems with lower complexity and iteratively optimizes them. Simulation experiments validate that the SCRAU algorithm can simultaneously fulfill radar and communication functions. The SCRAU algorithm demonstrates superior WEP performance compared to current advanced algorithms.

Decentralized coded caching for shared caches using erasure coding

In a client–server architecture data traffic varies through time. It exhibits congestion of network resources at peak hours while the network remains rather under-utilized during the off-peak hours. One of the potential techniques to smooth out this data traffic over time is to load part of the data into cache memories distributed across the network. In the classical cache framework, each user is associated to a cache and the caching process is centrally coordinated. However, for large networks, decentralized caching is more practical since decentralized caching does not require the knowledge of all the active users in the network during the prefetching phase which is a necessity for centralized caching. Furthermore, with an aim to reduce cache under-utilization in a network, caches are allowed to serve multiple clients. Such a scheme in which multiple users are allowed to share a cache is referred to as the shared caching scheme. Shared caching scheme has been explored in both centralized and decentralized setting with centralized scheme giving a superior performance. To improve the performance of the decentralized caching scheme we implement precoding at the server’s end. Precoding is done by encoding the data stored in the server using a Maximum Distance Separable (MDS) erasure code. The coded caching framework in this paper is denoted as MDS precoded decentralized shared caching. We propose a delivery scheme and derive a delivery rate for the proposed scheme. Using techniques from index coding, a lower bound is obtained for all MDS precoded decentralized shared caching linear delivery schemes delivering certain contents. The proposed lower bound is shown to meet the proposed delivery rate. The achievable delivery rate proposed in this paper is also shown to match with the rate obtained for the optimal centralized shared caching scheme for small and large cache memory sizes.

A Repeated Unknown Game: Decentralized Task Offloading in Vehicular Fog Computing

Offloading computation to nearby edge/fog computing nodes, including the ones carried by moving vehicles, e.g., vehicular fog nodes (VFN), has proved to be a promising approach for enabling low-latency and compute-intensive mobility applications, such as cooperative and autonomous driving. This work considers vehicular fog computing scenarios where the clients of computation offloading services try to minimize their own costs while deciding which VFNs to offload their tasks. We focus on decentralized multi-agent decision-making in a repeated unknown game where each agent, e.g., service client, can observe only its own action and realized cost. In other words, each agent is unaware of the game composition or even the existence of opponents. We apply a completely uncoupled learning rule to generalize the decentralized decision-making algorithm presented in [7] for the multi-agent case. The multi-agent solution proposed in this work can capture the unknown offloading cost variations susceptive to resource congestion under an adversarial framework where each agent may take implicit cost estimation and suitable resource choice adapting to the dynamics associated with volatile supply and demand. According to the evaluation via simulation, this work reveals that such individual perturbations for robustness to uncertainty and adaptation to dynamicity ensure a certain level of optimality in terms of social welfare, e.g., converging the actual sequence of play with unknown and asymmetric attributes and lowering the correspondent cost in social welfare due to the self-interested behaviors of agents.

“Ship-port-country” multi-dimensional research on the fine analysis of China's LNG trade

Liquefied Natural Gas (LNG) is a clean and low-carbon energy source that plays an important role in combating global warming. As the world's largest greenhouse gas (GHG) emitter, China has taken coal-to-gas as an important measure to reduce GHG emissions, and has become the world's largest LNG importer. Studying the patterns and characteristics of China's LNG trade is of great significance for ensuring energy supply security, formulating and adjusting energy strategies. However, the refined analysis of China's LNG trade is still inadequate, mainly due to the lack of research on the details of ship trajectories and port attributes. Therefore, a “ship-port-country” multidimensional LNG trade analysis method is proposed to improve the timeliness, multi-dimensionality and fineness of China's LNG trade analysis. The multidimensional characteristics of China's LNG trade are analyzed by studying the big data of nearly 3.5 billion automatic identification system (AIS) records in 2017. The study has found that (1) The arrival frequency of China's LNG trading ships varies greatly with time. For example, the most frequent daily arrivals are between 04:00 and 05:00 in the morning, while the least frequent arrivals are between 15:00 and 17:00 in the afternoon. (2) The Chinese LNG trade network consisting of 79 ports has been formed, of which NANTONG Port is the most important port. (3) The LNG trade volume calculated in this study has shown a strong correlation with the national LNG trade statistics and can be used to analyze LNG trade with higher spatial and temporal resolution. According to the characteristics of LNG trade at the three levels of ship, port and country, China can optimize its LNG trade policy from the following aspects: (1) Dynamically allocate port resources according to the arrival frequency of LNG tankers to reduce port congestion and waste of resources. (2) Expand core LNG trading ports such as NANTONG Port, ZHOUSHAN Port, TANGSHAN (JINGTANG) Port, XIUYU Port, DALIAN Port and LUSHUN Port. (3) Actively realize the diversification of LNG trading partners to reduce the dependence of LNG import sources on one country or region, and ensure energy security. The “ship-port-country” multi-level LNG trade analysis method proposed in this study provides a new research perspective for the analysis of the LNG trade pattern. This study reveals the multidimensional characteristics of China's LNG trade, which can provide decision-making support for China to formulate reasonable LNG trade policies and energy security policies, and is conducive to the stable and sustainable development of global LNG trade.

The Integration of WoT and Edge Computing: Issues and Challenges

The Web of Things is an improvement on the Internet of Things (IoT) that incorporates smart objects into both the web architecture (application) and the internet (network). WoT applications are inescapable in residential homes and communities. The intent behind WoT applications is to increase sustainable development for reducing resource consumption. The Web of Things (WoT) aims to create a decentralized Internet of Things. Edge computing addresses IoT computing demands by reducing the escalation in resource congestion situations. In edge computing data is placed closed to the end users which diverts computation load from the centralized data centers. Furthermore, the dispersed structure balances network traffic and minimizes traffic peaks in IoT networks. Therefore, resulting in reducing transmission delays between edge servers and end users which improves response times for real-time WoT applications. Low battery supply to nodes with enough power resources can increase the lifespan of the individual nodes by moving processing and communication overhead from the nodes. This paper integrates WoT and edge computing and compares their functionalities. In addition, it demonstrates how edge computing enhances WoT performance and concentrates on transmission, storage, and computation aspects. Furthermore, for performance evaluation it categorizes edge computing based on different architectures. Moreover, the challenges of Web of Things and edge computing have been discussed in terms of bandwidth, latency, energy, and cost. Finally, advantages of the Web of Things and edge computing have been discussed.

Attention, institutional friction, and punctuated equilibrium in China's budget: Changes in social security and employment expenditure

AbstractBased on the literature on punctuated equilibrium theory (PET), we conducted an empirical analysis on the changes in China's social security and employment budget expenditure at the subnational level from 2009 to 2020. We calculated the kurtosis value for 31 provinces to check whether they conform to PET. We used the number of policy documents and resource congestion as indicators of attention allocation and decision cost, respectively, to determine the impacts they have on budget changes. We then performed regression with a fixed effect model. The results show that the budgetary changes of only 20 provinces obey a leptokurtic distribution. Moreover, the allocation of attention by decision makers and institutional friction can explain this pattern of change. This article enriches the empirical study and extends its applicability to PET at the subnational level, especially in authoritarian regimes such as China, and provides some evidence for subsequent research on selecting indicators.Related ArticlesKwon, Sung‐Wook, and Sylvia Gonzalez‐Gorman. 2019. “Influence of Local Political Institutions on Policy Punctuation in Three Policy Areas.” Politics & Policy 47(2): 300–25. https://doi.org/10.1111/polp.12295.Modlin, Steve. 2008. “Defining Involvement of County Commissioners in the Budget Formulation Process.” Politics & Policy 36(6): 1044–65. https://doi.org/10.1111/j.1747‐1346.2008.00149.x.Moreno‐Enguix, María Del Rocío, and Laura Vanesa Lorente Bayona. 2017. “Factors Affecting Public Expenditure Efficiency in Developed Countries.” Politics & Policy 45(1): 105–43. https://doi.org/10.1111/polp.12194.

Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology

How to allocate the existing medical resources reasonably, alleviate hospital congestion and improve the patient experience are problems faced by all hospitals. At present, the combination of artificial intelligence and the medical field is mainly in the field of disease diagnosis, but lacks successful application in medical management. We distinguish each area of the emergency department by the division of medical links. In the spatial dimension, in this study, the waitlist number in real-time is got by processing videos using image recognition via a convolutional neural network. The congestion rate based on psychology and architecture is defined for measuring crowdedness. In the time dimension, diagnosis time and time-consuming after diagnosis are calculated from visit records. Factors related to congestion are analyzed. A total of 4717 visit records from the emergency department and 1130 videos from five areas are collected in the study. Of these, the waiting list of the pediatric waiting area is the largest, including 10,436 (person-time) people, and its average congestion rate is 2.75, which is the highest in all areas. The utilization rate of pharmacy is low, with an average of only 3.8 people using it at the one time. Its average congestion rate is only 0.16, and there is obvious space waste. It has been found that the length of diagnosis time and the length of time after diagnosis are related to age, the number of diagnoses and disease type. The most common disease type comes from respiratory problems, accounting for 54.3%. This emergency department has congestion and waste of medical resources. People can use artificial intelligence to investigate the congestion in hospitals effectively. Using artificial intelligence methods and traditional statistics methods can lead to better research on healthcare resource allocation issues in hospitals.

Deep Reinforcement Learning-Based QoS Optimization for Software-Defined Factory Heterogeneous Networks

The routing algorithm based on a single routing metric parameter is difficult to meet the Quality of Services (QoS) requirements of multi-source data flows in the software-defined factory heterogeneous network, resulting in link congestion and waste of network resources. To solve the problem, this paper proposes a QoS optimization method for software-defined factory heterogeneous networks based on the double deep Q network (DDQN). First, a QoS optimization architecture is proposed for software-defined factory heterogeneous networks, and the optimization function for network latency and load balancing is established. Then, the DDQN algorithm is used to obtain the optimal paths of multi-source data flows, and the optimal path is uniformly issued by the software-defined network controller. Experiments showed that the proposed algorithm has good convergence and generalizability. Compared with the existing algorithms, it outperforms in network latency, network jitter, and network throughput, improving network load balancing and routing efficiency. This research is able to provide a solution to the QoS optimization and dynamic traffic scheduling in smart heterogeneous networks and provide a technical reference point for realizing customized smart factories.

How slack resource affects hospital financial performance: The evidence from public hospitals in Beijing.

BackgroundBeijing is a city with high concentration and congestion of quality medical resources in China. While moderate slack seems to be beneficial to the improvement of medical quality. The actual relationship between hospital slack resources and their performance deserves further exploration. The study aims to analyze the slack resources of public hospitals in Beijing and investigate the relationship between slack and hospital financial performance. Finding a reasonable range of slack to optimize resource allocation.MethodsThe panel data of 22 public hospitals in Beijing from 2005 to 2011 were selected as the sample, and the DEA model was applied to measure the main variable using DEAP 2.1. Descriptive statistical analysis was performed using Excel and STATA 15. Pearson correlation coefficient analysis and variance inflation factor test were performed for each variable to avoid multicollinearity. The HAUSMAN test was used to determine the appropriate panel regression model, and then to analyze the influence relationship between the variables.ResultsFrom 2005 to 2011, hospital slack resource transitioned from high to low. The slack measured by the DEA model has an inverted U-shaped relationship with financial performance, with ROA increasing from 4.088 to 8.083 when slack increases from 0 to about 0.378, and then showing a decreasing trend; slack measured by financial indicators has a transposed S-shaped relationship with financial performance, with ROA increasing when slack increase from 3.772 to 5.933.ConclusionsThe slack resources of Beijing public hospitals decreased year by year from 2005 to 2011. Moderate slack resources are conducive to the improvement of healthcare quality, but when slack resources increase to a certain level, it will have a negative impact on healthcare quality. Therefore, hospital managers should control the slack within a moderate range according to the hospital operation policy and development plan to obtain the best performance.

Designing Collaborative Edge Computing for Electricity Heterogeneous Data Based on Social IoT Systems

Power generation, transmission, maintenance costs, and electricity prices are heavily influenced by accurate load forecasts at energy suppliers' operation centers. Every aspect of our life has been transformed by the social internet of things (SIoT). Collaborative edge computing (CEC) has emerged as a new paradigm for meeting the demands of the internet of things by alleviating resource congestion (IoT). Remote devices can connect to CEC's processing, storage, and network resources. About short-term electrical load forecasting, this study explores the application of feed-forward deep neurological networking (FF-DNN) and recurrent deep neuronal networking (R-DNN) methods and analyzes their accuracy and computing performance. A dynamic prediction system using a deep neural network (DPS-DNN) is proposed in this research. The recently unveiled smartgrid with the results shows the higher performance of the proposed DPS-DNN model than the existing models with an enhancement of 93.15% based on collaborative edge networks based on SIoT.

Optimization of the Reversible Lane considering the Relationship between Traffic Capacity and Number of Lanes

To make full use of road resources, improve the operation efficiency of the road network system, and alleviate the coexistence between traffic congestion and road resources idle caused by the traffic tidal phenomenon, the impact of the number of lanes on traffic capacity is examined, and the mixed-integer bilevel programming model for reversible lane optimization is established with the aim to minimalize the total travel time of the system. Taking a test road network as an example, the influence of the reversible lane optimization on characteristic values of sections, the route travel time between OD pairs, and the total time of the system are analyzed. The results indicate that the reversible lane optimization can make full use of the idle road resources and make the road network structure match the travel demands better, and the system index after the reversible lane optimization is obviously better than the original system index.

Opportunistic innovation in the age of digital services

AbstractThis study is about competitive dynamics surrounding opportunistic innovation in the digital supply chain (i.e., on‐line gaming, multi‐media broadcasting, etc.). When a digital service firm falls short of delivering required services, consumers look for alternative service offerings. For competing firms, potential demand is created suddenly, and they are compelled to innovate to capture this opportunity. This innovation response occurs in reaction to a service failure, and we call it opportunistic innovation. We characterize opportunistic innovation as being situational and temporal—the opportunity is created by the failure of another firm, and it is taken up by the fast‐responding firms. The goal of this study is to develop a theory of opportunistic innovation and empirically examine its presence and associated constructs. We have compiled the daily usage‐and‐update history of 460 online gaming services between 2014 and 2017 and their 58 major service failure events. Our results confirm the temporal and situational characteristics of opportunistic innovation. Firms with alternative services quickly introduce innovation to the market shortly after a service failure when the readiness for convertibility at competing firms is high. In addition, results suggest that opportunistic innovation is constrained by the resource congestion on the supplier side in the digital supply chain.

An optical access scheme with 1.25Gbit/s NRZ downstream signals over 6GHz subcarrier for next generation mobile systems application

In order to solve the problem of network frequency resource congestion from the central office (CS) to the base station (BS) in sixth generation (6G) mobile communication systems, we design a passive optical network (PON) access scheme. 1.25Gbit/s non-return-to-zero (NRZ) downstream signals over 6GHz subcarrier are successfully transmitted in the 20km single-mode fiber (SMF) and allocated to 4 optical network units (ONUs). The radio frequency (RF) spectrums, optical spectrums, eye-diagrams and bit error rate (BER) before and after transmission of NRZ signals in the system were measured and analysed. The results show that, this scheme can provide high-speed, stable and secure data communication.

LBRO: Load Balancing for Resource Optimization in Edge Computing

Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user’s preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users’ preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.

Multi-Objective Deep Reinforcement Learning Assisted Service Function Chains Placement

The study of Service Function Chains (SFCs) placement problem is crucial to support services flexibly and use resources efficiently. Solutions should satisfy various Quality of Service requirements, avoid edge resource congestion, and improve service acceptance ratio (SAR). This work presents a novel approach to address these challenges by solving a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-objective SFCs placement</i> problem based on the Pointer Network in multi-layer edge and cloud networks. We design a Deep Reinforcement Learning algorithm, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chebyshev-assisted Actor-Critic SFCs Placement Algorithm</i> , to overcome the limitations of traditional heuristic and evolutionary algorithms. Then, we run this algorithm iteratively with a set of weights to obtain non-dominated fronts, which have much higher hypervolume values than those obtained from other state-of-the-art algorithms. Moreover, running our algorithm individually with selected weights from non-dominated fronts can avoid edge resource congestion and achieve 98% SARs of low-latency services during high-workload periods. Finally, based on both simulation and real testbed experimental results, it is validated that the proposed algorithm fits for pragmatic service deployment while achieving 100% of SARs in the use cases deployed on the testbed.

MIPSGPU: Minimizing Pipeline Stalls for GPUs With Non-Blocking Execution

Improving the latency hiding ability is important for GPU performance. Although existing works, which mainly target on either improving thread level parallelism or optimizing memory hierarchy, are effective at improving GPUs’ latency hiding ability, warps are still blocked after executing long latency operations, reducing the number of schedulable warps. This article revisits the recently proposed non-blocking execution for GPUs to improve the latency hiding ability of GPUs. With non-blocking execution, instructions from warps blocked by long latency operations can be pre-executed to make full use of GPU resources. However, we find that the state-of-the-art non-blocking GPU architecture gains limited performance improvement. Through in-depth analysis, we observe that the poor performance is largely due to inefficient pre-execution state management, duplicate instruction extraction, frequent early eviction and severe resource congestion. To make non-blocking execution actually useful for GPUs and minimize hardware overheads, we carefully redesign the non-blocking architecture for GPUs based on our analysis and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIPSGPU</i> . Our evaluations show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIPSGPU</i> , relative to the state-of-the-art non-blocking GPU architecture, improves performance of memory intensive applications by 19.05 percent, and reduces memory to SM traffics by 14 percent.

Replica selection and placement techniques on the IoT and edge computing: a deep study

Internet of Things (IoT) has lately been presented as a new technological transformation in which things are connected via the Internet. Several sensors and devices create data and send vital signals constantly over sophisticated networks that allow machine-to-machine interactions and monitor and manage key smart-world infrastructures. Since huge amounts of data are generated, reducing the data access costs is a critical issue. Edge computing has been developed as a novel paradigm for solving IoT demands to reduce the rise in resource congestion. One of the most significant data management challenges in the IoT is selecting suitable replication things that minimize reaction time and cost. Therefore, our goal is to examine replica selection and placement techniques in IoT and edge computing. The findings revealed that the edge computing environment might significantly enhance system performance regarding access response time, prediction accuracy, effective network, and increased data availability. Furthermore, the results illustrate that data provenance is necessary to raise the accuracy of the data by. Also, the results showed that the most important challenge in data replication and placement techniques in IoT and edge computing was the availability of data and access response time.

Resource-Optimized Recursive Access Class Barring for Bursty Traffic in Cellular IoT Networks

A massive number of Internet-of-Things (IoT) and machine-to-machine (M2M) communication devices generate various types of data traffic in cellular IoT networks: periodic or nonperiodic, bursty or sporadic, etc. In particular, bursty and nonperiodic traffic may cause an unexpected network congestion and temporary lack of radio resources. In order to effectively accommodate such bursty and nonperiodic traffic, we propose a novel recursive access class barring (R-ACB) technique to optimally utilize the available resources associated with the random access procedure (RAP) that consists of multiple steps in cellular IoT networks, while existing ACB schemes only considered the resource of the first step of RAP, i.e., the number of available preambles. The proposed R-ACB technique consists of two main parts: 1) online estimation of the number of active IoT/M2M devices who have data to transmit to an eNodeB and 2) adjustment of the ACB factor that indicates the probability that an active device sends a preamble to eNodeB. It is notable that the estimation and the adjustment recursively affect each other when R-ACB operates. In addition, we also propose mathematical models to analyze the performance of R-ACB in terms of total service time, average access delay, resource efficiency, and energy efficiency (EE). Through extensive computer simulations, we show that the proposed R-ACB technique outperforms the conventional ACB schemes.

Comparative Perspectives on Modern Logistics Transportation Based on Green Logistics in Europe and Indonesia: Concept of Sustainable Economy

The concept of logistics is a supply chain system to facilitate the movement of goods and resources (raw materials), delivery scheduling, storage, and marketing to consumer endpoints that support economic growth. The increase in logistics transportation also has a negative impact, especially environmental problems, the effectiveness of logistics transportation, and the quality of materials and goods which will eventually involve economic problems. This article aims to compare the implementation of modern logistics transportation systems in the European Union and Indonesia with the application of green logistics. This study uses a comparative study method with a qualitative descriptive approach to modern logistics transportation that applies the concept of green logistics. The problem of European Union logistics transportation is only in the human resources sector and congestion in a certain period. The solution is to add regulations related to alternative or manipulated road systems to reduce congestion. On the other hand, the problems that exist in Indonesia are related to the low facilities, regulations, and investment for logistics transportation. As a solution, several regulations and programs have been implemented as a green logistics concept such as anti-ODOL regulations, and the sea toll program.

IMPACT OF COVID-19 ON PEDIATRIC SURGERY

INTRODUCTIONThe pandemic caused by the SARS-CoV-2 virus has led to an unprecedented situation, with population lockdowns, congestion of healthcare resources, cancellation of scheduled surgical activity, fear of contagion, and delays in patient care. The objective of this study was to analyze its impact on pediatric surgical activity at a reference healthcare facility.MATERIAL AND METHODSA comparative study of activity in the pediatric surgery environment at a Spanish reference healthcare facility was carried out. It included outpatient consultations, scheduled surgeries, and activity at the emergency department during the lockdown period in Spain (March-May 2020) vs. the same 2019 period. Number of consultations and surgeries, type of surgery performed and/or cancelled, and stage of the pathologies treated were collected.RESULTSA 98% decrease in scheduled surgeries, an 84% decrease in healthcare burden from the pediatric emergency department, a 55.24% decrease in urgent surgeries, an 82% decrease in outpatient activity at external consultations, and a 94% decrease in inter-hospital referrals, along with a 66% increase in urgent pathology severity, were found.CONCLUSIONSThe COVID-19 pandemic cancelled virtually all pediatric surgery activity, which caused treatment delays in severe pathologies and increased morbidity in regular urgent procedures.