Dynamic Mobility Research Articles

Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems Under Demand and Supply Uncertainties

Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD systems makes the demand and supply uncertainties significant when designing vehicle balancing algorithms. In this work, we design a data-driven distributionally robust optimization (DRO) approach to balance EVs for both the mobility service and the charging process. The optimization goal is to minimize the worst-case expected cost under both passenger mobility demand uncertainties and EV supply uncertainties. We then propose a novel distributional uncertainty sets construction algorithm that guarantees the produced parameters are contained in desired confidence regions with a given probability. To solve the proposed DRO AMoD EV balancing problem, we derive an equivalent computationally tractable convex optimization problem. Based on real-world EV data of a taxi system, we show that with our solution the average total balancing cost is reduced by 14.49%, and the average mobility fairness and charging fairness are improved by 15.78% and 34.51%, respectively, compared to solutions that do not consider uncertainties.

Latency-Aware Multi-Antenna SWIPT System With Battery-Constrained Receivers

Power splitting (PS) based simultaneous wireless information and power transfer (SWIPT) is considered in a multi-user multiple-input-single-output broadcast scenario. Specifically, we focus on jointly configuring the transmit beamforming vectors and receive PS ratios to minimize the total transmit energy of the base station under the user-specific latency and energy harvesting (EH) requirements. The battery depletion phenomenon is avoided by preemptively incorporating information regarding the receivers' battery state and EH fluctuations into the resource allocation design. The resulting time-average sum-power minimization problem is temporally correlated, non-convex (including mutually coupled latency-battery queue dynamics), and in general intractable. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform the original problem into a sequence of deterministic and independent subproblems, which are then solved via two alternative approaches: i) semidefinite relaxation combined with fractional programming, and ii) successive convex approximation. Furthermore, we design a low-complexity closed-form iterative algorithm exploiting the Karush-Kuhn-Tucker optimality conditions for a specific scenario with delay bounded batteryless receivers. Numerical results provide insights on the robustness of the proposed design to realize an energy-efficient SWIPT system while ensuring latency and EH requirements in a time dynamic mobile access network.

Drone Swarm Path Planning for Mobile Edge Computing in Industrial Internet of Things

Drone-swarm-assisted mobile edge computing (MEC) provides extra computation and storage capacity for smart city applications and the industrial Internet of Things. To solve the problems of traditional fixed base stations in complex terrain, including cost of deployment, transmission loss of telecommunication, and limited coverage, this paper brings forward the unmanned aerial vehicles (UAVs) as mobile edge computing nodes in the air. For the purpose of matching the dynamic mobile devices and UAV trajectory, this paper raises a multi-UAVs-assisted mobile edge computing offloading algorithm based on global and local path planning controlled by ground station and onboard computer. Firstly, this paper considers a drone swarm scheduling and allocation strategy based on the priority of monitoring areas, UAVs residual energy and distance to target points, so that to minimize the global flight length and energy consumption. Secondly, based on user mobility, this paper calculates the optimal communication coverage of UAV, and jointly optimizes the local path planning and computing offloading, so that to maximize the number of offloading services and minimize the total latency in completing the computation task. Finally, based on the total latency and energy consumption of path planning and computation offloading, a UAV cluster computation offloading strategy with optimized energy efficiency is realized. Experimental results prove that the proposed algorithm can provide more offloading services while obtaining shorter path length and greater energy efficiency.

Effects of ambient population with different income levels on the spatio-temporal pattern of theft: A study based on mobile phone big data

Recent studies focus on how to accurately measure ambient population and its effects on crimes. However, limited attention has been paid to the internal heterogeneity of ambient population composition and its temporal variation. To fill these gaps, controlling for indicators of ambient population, social disorganization, crime generators and attractors, we model the effects of the share of low income, middle income, upper-middle income and high income of ambient population at the community level in the whole and specific time periods on weekdays and weekends. Results suggest that while all income groups of the ambient population contribute to theft at the community level, their contributions are not uniform. At different periods of both weekdays and weekends, there is no single income group that consistently predicts theft. Furthermore, the magnitude of the impact of each income group fluctuates with time. Our results highlight the heterogeneous segments of the ambient population, coupled with the dynamic human mobility patterns, structure the convergence of the crime triangle – motivated offenders, potential targets, and guardianship – in nuanced yet crucial ways.

Framework to leverage physical therapists for the assessment and treatment of chemotherapy-induced peripheral neurotoxicity (CIPN).

Chemotherapy-induced peripheral neurotoxicity (CIPN) is a highly prevalent, dose-limiting, costly, and tough-to-treat adverse effect of several chemotherapy agents, presenting as sensory and motor dysfunction in the distal extremities. Due to limited effective treatments, CIPN can permanently reduce patient function, independence, and quality of life. One of the most promising interventions for CIPN is physical therapy which includes exercise, stretching, balance, and manual therapy interventions. Currently, there are no physical therapy guidelines for CIPN, thus limiting its uptake and potential effectiveness. Utilizing the authors' collective expertise spanning physical therapy, symptom management research, oncology, neurology, and treating patients with CIPN, we propose a comprehensive clinical workflow for physical therapists to assess and treat CIPN. This workflow is based on (1) physical therapy guidelines for treating neurologic symptoms like those of CIPN, (2) results of clinical research on physical therapy and exercise, and (3) physical therapy clinical judgement. We present detailed tables of pertinent physical therapy assessment and treatment methods that can be used in clinical settings. CIPN assessment should include detailed sensory assessment, objective strength assessments of involved extremities, and validated physical performance measures incorporating static and dynamic balance, gait, and functional mobility components. CIPN treatment should involve sensorimotor, strength, balance, and endurance-focused interventions, alongside a home-based exercise prescription that includes aerobic training. We conclude with action items for oncology teams, physical therapists, patients, and researchers to best apply this framework to address CIPN. Physical therapists are in a unique position to help assess, prevent, and treat CIPN given their training and prevalence, yet there are no physical therapy clinical practice guidelines for CIPN. Our preliminary suggestions for CIPN assessments and treatments can catalyze the development of guidelines to assess and treat CIPN. We urge oncology teams, physical therapists, patients, and researchers to develop, adapt, and disseminate this framework to help alleviate the burden of chemotherapy on patients with cancer.

Geo-Tile2Vec: A Multi-Modal and Multi-Stage Embedding Framework for Urban Analytics

Cities are very complex systems. Representing urban regions are essential for exploring, understanding, and predicting properties and features of cities. The enrichment of multi-modal urban big data has provided opportunities for researchers to enhance urban region embedding. However, existing works failed to develop an integrated pipeline that fully utilizes effective and informative data sources within geographic units. In this article, we regard a geo-tile as a geographic unit and propose a multi-modal and multi-stage representation learning framework, namely Geo-Tile2Vec, for urban analytics, especially for urban region properties identification. Specifically, in the early stage, geo-tile embeddings are firstly inferred through dynamic mobility events which are combinations of point-of-interest (POI) data and trajectory data by a Word2Vec-like model and metric learning. Then, in the latter stage, we use static street-level imagery to further enrich the embedding information by metric learning. Lastly, the framework learns distributed geo-tile embeddings for the given multi-modal data. We conduct experiments on real-world urban datasets. Four downstream tasks, i.e., main POI category classification task, main land use category classification task, restaurant average price regression task, and firm number regression task, are adopted for validating the effectiveness of the proposed framework in representing geo-tiles. Our proposed framework can significantly improve the performances of all downstream tasks. In addition, we also demonstrate that geo-tiles with similar urban region properties are geometrically closer in the vector space.

Cauliflower mosaic virus protein P6 is a multivalent node for RNA granule proteins and interferes with stress granule responses during plant infection.

Biomolecular condensation is a multipurpose cellular process that viruses use ubiquitously during their multiplication. Cauliflower mosaic virus replication complexes are condensates that differ from those of most viruses, as they are nonmembranous assemblies that consist of RNA and protein, mainly the viral protein P6. Although these viral factories (VFs) were described half a century ago, with many observations that followed since, functional details of the condensation process and the properties and relevance of VFs have remained enigmatic. Here, we studied these issues in Arabidopsis thaliana and Nicotiana benthamiana. We observed a large dynamic mobility range of host proteins within VFs, while the viral matrix protein P6 is immobile, as it represents the central node of these condensates. We identified the stress granule (SG) nucleating factors G3BP7 and UBP1 family members as components of VFs. Similarly, as SG components localize to VFs during infection, ectopic P6 localizes to SGs and reduces their assembly after stress. Intriguingly, it appears that soluble rather than condensed P6 suppresses SG formation and mediates other essential P6 functions, suggesting that the increased condensation over the infection time-course may accompany a progressive shift in selected P6 functions. Together, this study highlights VFs as dynamic condensates and P6 as a complex modulator of SG responses.

A conceptual framework for developing dashboards for big mobility data

ABSTRACT Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets.

Трансформация понимания идентичности в философском дискурсе

The article analyzes the concept of “identity”. Within the framework of the history of the development of the concept, periodization is given in the form of two large stages, which include a set of different theories. A semantic explication and characterization of the main theories of identity is made. The semantic transformation of the concept of “identity” is traced in different philosophical directions and schools, as well as by different authors. A causal relationship is established between the understanding of identity and a specific historical socio-cultural situation. Within the framework of the article, identity is conceptualized not as a static entity, but as a dynamic mobile structure with the potential for recombination and reconfiguration. A correlation is made between identity as a topic of philosophical reflection and epistemological procedures of identity and distinction. The main problem is the problem of identity in modern Kazakhstani society, which, according to the author, lies in the divergence of objective socio-cultural circumstances (the reality of globalization) and internal factors that have a strong influence on self-determination (history, traditions, mentality). The fact is emphasized that the history of the Western world in its character, consistency, dynamics and partly purposefulness is not identical to the history of Kazakhstan. Therefore, linear modernization (building one's culture according to European patterns) is hindered by many endemic reasons that cannot simply be discarded as something outdated. In many ways, these endemic factors make up the originality of the national and cultural identity of the Kazakh society.

A facile and small-molecule regulated borate network gelation to improve the mildew proof, fire-retardant of bamboo

Bamboo lacks transverse tissues, which negatively affect the intercellular water/nutrient molecules transfer and predisposed to mold. Borate is widely used industrially for wood protection but it still short-time achieved. Here, small molecules of natural polyphenol-tannic acid (TA) were applied to tune the bonding types on bamboo inner surfaces and in situ construct gel-like networks with boric acid (BA) in the xylem. The borate was affinity towards catechol groups to facilely fabricate borate-catechol covalent linkages. Meantime, the all small molecules’ gel network allows the boron remains a small amount of dynamic mobility and thus to increase bamboo durability. Take advantage of these small molecules gelling, the treated bamboo strips maintained good bending strength (118.5 MPa) comparable to the unmodified one, flame-retardant, and excellent mildew proof effect for the Aspergillus Niger, special long-time anti-mold feature at quite high moisture condition (97 % relative humidity) for 60-day. Overall, this borate modified bamboo shows great promising to drive the large-scale production of anti-mold bamboo.

Stable hemoglobin-based biosensor based on coordination-assisted microfluidic technology for hydrogen peroxide determination

Enhancing the stability and sensitivity of electrochemical biosensors is highly significant for their practical application. Herein, inspired by the formation of mussel foot protein, we proposed a strategy to construct a hemoglobin-based biosensor for hydrogen peroxide detection using a hydrophobic ionic liquid (HIL) coordination assisted microfluidic technology. The active layer HIL@Hb was achieved by mixing BBimPF6 (HIL) and Hb via a microfluidic channel, in which HIL helps to maintain the conformational dynamic mobility of hemoglobin (Hb), while the coordination process in a microfluidic reactor prevents aggregation of Hb. Further, the electrode surface was modified with ultra-thin MXene-Ti3C2 nanosheets to ensure the effective adhesion of active layer and collection of sensing signals, thus improving the sensitivity of the sensor by synergistic catalysis. Experimental results demonstrate that our designed sensor has good repeatability and stability, which can retain 93% of the initial current response after 30 uses and about 90.11% of its primary current response to H2O2 after 30 days. And it has a good linear response range of 1.996–27.232 μM, detection limit reaching 1.996 nM (signal-to-noise ratio, S/N = 3), sensitivity of 52.08 μA·μM−1·cm−2. Overall, this research offers a facile and effective strategy for constructing a stable biosensor to effectively detect hydrogen peroxide.

Cross-domain coordination of resource allocation and route planning for the edge computing-enabled multi-connected vehicles

Multi-access edge computing (MEC) has unique interests in processing intensive and delay-critical computation tasks for vehicular application through computation offloading. However, due to the spatial inhomogeneity and dynamic mobility of connected vehicles (CVs), the edge servers (ESs) must dynamically adjust their resource allocation schemes to effectively provide computation offloading services for CVs. In this case, we propose a MEC framework supporting the collaboration of CVs, and incorporate digital twins (DTs) into wireless network to mitigate the unreliable long-distance communication between CVs and ESs. To solve the contradiction between the task change requirements of CVs and ES resources, we proactively balance the computation resources load of ESs by appropriately cooperative route planning of CVs, and achieve cross-domain load balancing between traffic flow and edge cloud resources domains. Furthermore, we jointly formulate route planning and resource allocation to balance the travel and service time delay by considering the mobility of CVs, distributed resources of ESs and the deadline sensitive vehicular tasks comprehensively. Besides, considering the coupled relationship between route planning and resource allocation, an alternating optimization algorithm is proposed to solve the formulated problem. we decompose it into two sub-problems. Firstly, a reinforcement learning method is used to optimize the route planning of CVs with fixed resource allocation. Then, an online learning and iterative algorithm is used to optimize the resource allocation strategy of edge cloud with fixed route selection. In order to demonstrate that our suggested scheme is more effective than other comparison schemes, a comprehensive series of experiments are conducted.

Ant-Colony Optimization Based Energy Aware Cross Layer Routing Protocol to Improve Route Reliability in MANETs

Improved route reliability is one of the key parameter for successful dissemination of the multimedia and emergency messages in decentralized, highly dynamic Mobile Ad Hoc Networks (MANETs). In order to achieve this, an energy efficient optimized routing protocol is essential, as mobile nodes run on battery power. There is plethora of studies that emphasizes on route reliability and energy consumption by modifying the existing MANET reactive routing schemes. Most of these are based on shortest path with traditional single layer approach, allows only one or two node parameters for both relay node selection and optimal path. However, due to the nature of the MANETs, still several factors such as unnecessary flooding of control packets and intermittent connectivity among the nodes distress the battery power directly. Therefore, in this paper, an Ant colony optimization based energy aware cross layer routing protocol (AOERP) is presented. The proposed AOERP includes series of mechanisms: firstly, selection of an adaptive relay nodes based on high energy factor and neighbor node ratio at each node in the network to avoid dead nodes and secondly identifying a finest path among the selected relay nodes in the direction of destination using Ant Colony Optimization (ACO) method. These mechanisms make an attempt to improve the route reliability and in turn increases entire network lifetime to overcome the rerouting in the network by making use of multiple node parameters from lower three layers such as physical, MAC and network layers. The performance of the proposed AOERP protocol is carried out using Network Simulator Version 2.34 (NS-2.34) and compared over existing energy aware routing schemes. The simulation results show that the proposed AOERP outperforms in terms of reliability, energy factor, average end-to-end delay and percentage of dead nodes for different node density and node speeds.

Comparative study of dynamic soft tissue mobilization and passive stretching versus passive stretching alone on hamstring tightness of cricket players in order to increase rom

Comparative study of dynamic soft tissue mobilization and passive stretching versus passive stretching alone on hamstring tightness of cricket players in order to increase rom

Multi-Agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks

The 6G network enables a subnetwork-wide evolution, resulting in a “network of subnetworks”. However, due to the dynamic mobility of wireless subnetworks, the data transmission of intra-subnetwork and inter-subnetwork will inevitably interfere with each other, which poses a great challenge to radio resource management. Moreover, most existing approaches require the instantaneous channel gain between subnetworks, which are usually difficult to be collected. To tackle these issues, in this paper we propose a novel effective intelligent radio resource management method using multi-agent deep reinforcement learning (MARL), which only needs the sum of received power, named received signal strength indicator (RSSI), on each channel instead of channel gains. However, to directly separate individual interference from RSSI is an almost impossible thing. To this end, we further propose a novel MARL architecture, named GA-Net, which integrates a hard attention layer to model the importance distribution of inter-subnetwork relationships based on RSSI and excludes the impact of unrelated subnetworks, and employs a graph attention network with a multi-head attention layer to exact the features and calculate their weights that will impact individual throughput. Experimental results prove that our proposed framework significantly outperforms both traditional and MARL-based methods in various aspects.

Barriers and Drivers of Sustainable Business Model Innovation: Present and Future Research Perspectives

Sustainable business model innovation (SBMI) introduced a unique frontier in current business operations and innovation management. Despite the numerous advantages of SBMI to contemporary business strategy, most established firms face challenges in its successful implementation. Through a systematic review process (SRP), the paper attempted to critically evaluate and analyze the previous outcome on the barriers and drivers to SBMI. The research explored 42 prior studies to identify the thematic study areas, highlight the research gaps, and outline future propositions and agendas. The research thoroughly evaluates the state-of-the-art regarding barriers and drivers to implement SBMI. The SRP approach utilized in the study sheds light on the intricacies of SBMI by highlighting six critical barriers: institutional, organizational, strategic, resource allocation, technological, and financial barriers that hinder the successful deployment of SBMI. In addition, the study’s findings indicated that organizational learning, knowledge management, dynamic capabilities resource mobilization, innovative business activities, and human resource development could be a catalyst to the successful implementation of SBMI. Furthermore, the study highlighted some critical gaps and agendas for future research on SBMI. This study contributes to the literature on business model innovation and offers a practical outlook that can facilitate firms and policymakers in developing strategies to improve their business model.

PIRAP: Intelligent Hybrid Approach for Secure Data Transmission in Wireless Sensor Networks

The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[Formula: see text]s and decryption time by 15[Formula: see text]s.

Human Mobility, Sociolinguistic Diversity, and Social Sustainability in Rural Areas: Insights from Indonesian Transmigrant Communities

A substantial number of studies have been completed with respect to human mobility, linguistic diversity, and social sustainability in the Global North, but very few have been undertaken in relation to the Global South. Mobility, diversity, and sustainability are not recent phenomena, but little, if anything, is understood as regards how human mobility leads to linguistic diversity and social sustainability. This article fills this gap by explaining how the transmigrants of Javanese, Balinese, and Sasak ethnic backgrounds, along with the Bima and Dompu host communities, have established the ethnically and linguistically multi-diverse transmigrant communities of Manggelewa on the Indonesian island of Sumbawa. Data were collected from the participant ethnographic observations of the communities. The main strategies for data collection included taking notes, collecting documents, distributing questionnaires concerning human mobility, sociolinguistic diversity, and social sustainability, interviewing key informants, and recording conversations. Employing qualitative, quantitative, and ethnographic analyses, the study exhibits the community’s dynamic mobility, sociolinguistic diversity, and social sustainability. The study displays how human mobility leads to sociolinguistic diversity and how the diversity is used as a resource for sustaining a better interethnic relationship. The dimensions of mobility, sociolinguistic diversity, and social sustainability are discussed, and the factors that are essential for symbolic social sustainability are statistically attested.

A Retrospective Study to Evaluate the Role of Dynamic Fracture Mobility in the Conservative Treatment of Osteoporotic Vertebral Compression Fractures

To investigate whether dynamic fracture mobility could affect the outcome of conservative treatment in patients with acute osteoporotic vertebral compression fracture (OVCF). A total of 158 patients who underwent conservative treatment in our hospital for painful OVCFs were included in this study and their data were retrospectively analyzed. According to the degree of pain relief, patients were divided into an excellent efficacy group and a poor efficacy group. Factors that may affect the outcome of conservative treatment were recorded for each patient. Variables with a statistical difference between the 2 groups were entered into multivariate logistic regression analysis to identify the factors influencing the outcome of conservative treatment. Receiver operating characteristic curve analysis was also performed. The result showed that dynamic fracture mobility, overweight, age, and bone mineral density (BMD) (all P < 0.001) were independent factors influencing the outcome of conservative treatment. Receiver operating characteristic curve analysis showed that the cutoff values for age and BMD that predicted treatment effect were 72.5 years and -3.30, respectively. This study confirmed that dynamic fracture mobility could be used as an independent factor predicting the outcome of conservative treatment in patients with acute OVCFs. It was also shown that overweight, age, and BMD were other independent factors influencing the outcome of conservative treatment. A comprehensive evaluation of these related factors can guide the doctor to take appropriate treatment for a unique acute OCVF.

Provisioning network slice for mobile content delivery in uncertain MEC environment

Network Slicing (NS) and Mobile Edge Computing (MEC) are recognized as a promising paradigm for mobile Content Delivery Network (CDN) by instantiating Virtual Network Functions (VNFs) on MEC platforms. Mobile network exhibits intrinsic dynamics, where user requests patterns vary in time and space due to human behaviors. The requirements and distribution of users are arbitrary spatio-temporal stochastic variables, which makes the deployment and reconfiguration of a CDN slice a challenging task. In this work, by adopting a periodical reconfiguration strategy, we consider a CDN slice system which operates in discrete time intervals. Within each time interval, we formalize the CDN-MEC-VNFs Planning (CMVP) problem based on uncertain programming to tackle the dynamic and uncertain mobile network environment, where user requests are treated as random variables with arbitrary pattern. Then, we propose a data-driven and learning-based algorithm which integrates Stochastic Simulation (SS), Deep Neural Networks (DNN) and meta-heuristic algorithm to determine the provisioning of the CDN slice from historical user requests information. The practicability of the proposed mechanism in offline training and online running are also discussed. Finally, we conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on planning CDN slice with higher QoS under dynamic system environment and arbitrary user requests by comparing towards several baselines.