Optimal Migration Research Articles

Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction

In network function virtualization, the resource demand of network services changes with network traffic. SFC migration has emerged as an effective technique for preserving the quality of service. However, one important problem that has not been addressed in prior studies is how to manage network load while maintaining service-level agreements for time-varying resource demands. Therefore, we propose the Resource Predictive Load Balancing SFC Migration (RP-LBM) algorithm in this paper. The algorithm uses CNN-AT-LSTM to predict VNF resource demands in advance, eliminating the delays associated with dynamic migrations and determining the optimal migration timing. It leverages the PPO algorithm’s perceptual capabilities in complex environments to develop SFC migration strategies and ensure network load balancing. Additionally, it reduces the number of subsequent migrations and minimizes the service interruption rate. The simulation results show that the service interruption rate of the RP-LBM algorithm is on average 27.3% lower than that of the passive migration method. The PPO-based migration algorithm has lower SFC migration times and service interruption rates compared to the DQN algorithm, ensuring service continuity with low migration costs.

NoSQL document data migration strategy in the context of schema evolution

In Agile development, one approach cannot be chosen and used all the time. Constant updates and strategy changes are necessary. We want to show that combining several migration strategies is better than choosing only one. Also, we emphasize the need to consider the type of schema change. This paper introduces a novel approach designed to optimize the migration process for NoSQL databases. The approach represents a significant advancement in migration strategy planning, providing a quantitative framework to guide decision-making. By incorporating critical factors such as schema changes, database size, the necessity of data in search functionalities, and potential latency issues, the approach comprehensively evaluates the migration feasibility and identifies the optimal migration path. Unlike existing methodologies, this approach adapts to the dynamic nature of NoSQL databases, offering a scalable and flexible approach to migration planning.

Latency‐Sensitive Service Function Chains Intelligent Migration in Satellite Communication Driven by Deep Reinforcement Learning

ABSTRACTSatellite communication technology solves the problem that the traditional wired network infrastructure is difficult to achieve global communication coverage. However, factors such as satellite orbits introduce frequent changes to the network topology, and challenges like satellite failures and communication link interruptions are prevalent. In the face of these issues, service function chain (SFC) migration becomes a crucial method for swiftly adjusting SFCs during faults, maintaining service continuity and availability. This article proposes a latency‐sensitive SFC migration algorithm tailored to satellite networks. The algorithm first models the satellite network as a multi‐domain virtual network, capturing the constraints faced during SFC migration. Subsequently, a deep reinforcement learning algorithm integrated attention mechanism is designed to more accurately capture and understand the complex network environment and dynamic satellite network topology and derive optimal SFC migration strategies for superior performance. Finally, through experimentation and evaluation of the deep reinforcement learning‐driven latency‐sensitive service function chain intelligent migration algorithm (LS‐SFCM) in satellite communication, this study validates the effectiveness and superior performance of the algorithm in latency‐sensitive scenarios. It provides a new avenue for enhancing the service quality and efficiency of satellite communication networks.

TRPC3-mediated NFATc1 calcium signaling promotes triple negative breast cancer migration through regulating glypican-6 and focal adhesion.

Canonical transient receptor potential isoform 3 (TRPC3), a calcium-permeable non-selective cation channel, has been reported to be upregulated in breast cancers and a modulator of cell migration. Calcium-sensitive transcription factor NFATc1, which is important for cell migration, was shown to be frequently activated in triple negative breast cancer (TNBC) biopsy tissues. However, whether TRPC3-mediated calcium influx would activate NFATc1 and affect the migration of TNBC cells, and, if yes, the underlying mechanisms involved, remain to be investigated. By immunostaining followed by confocal microscopy, TNBC lines MDA-MB-231 and BT-549 were both found to express TRPC3 on their plasma membrane while ER+ line MCF-7 and HER2+ line SK-BR3 do not. Blockade of TRPC3 by pharmacological inhibitor Pyr3 or stable knockdown of TRPC3 by lentiviral vector both inhibited cell migration as measured by wound healing assay. Importantly, blocking TRPC3 by Pyr3 or knockdown of TRPC3 both caused the translocation of NFATc1 from the nucleus to the cytosol as revealed by confocal microscopy. Interestingly, NFATc1 was found to bind to the promoter of glypican 6 (GPC6) as determined by chromatin immunoprecipitation assay. Consistently, knockdown of TRPC3 decreased the expression of GPC6 as revealed by western blotting. Moreover, long-term knockdown of GPC6 by lentiviral vector also consistently decreased the migration of TNBC cells. Intriguingly, GPC6 proteins physically interact with vinculin in MDA-MB-231 as determined by co-immunoprecipitation. Blockade of TRPC3, knockdown of TRPC3 or knockdown of GPC6 all induced larger, stabilized actin-bound peripheral focal adhesion (FA) formations in TNBC cells as determined by co-staining of actin and vinculin followed by confocal microscopy. These large, stabilized actin-bound peripheral FAs indicated a defective FA turnover, and were reported to be responsible for impairing directed cell migration. Our results suggest that, in TNBC cells, calcium influx through TRPC3 channel positively regulates NFATc1 nuclear translocation and GPC6 expression, which maintains the dynamics of FA turnover and optimal cell migration. Our study reveals a novel TRPC3-NFATc1-GPC6-vinculin signaling cascade in maintaining the migration of TNBC cells.

Strength of seasonality and type of migratory cue determine the fitness consequences of changing phenology for migratory animals

Phenological mismatch has been highlighted as a reason why climate change is causing declines of migratory populations. The likelihood of declines due to phenological mismatch might depend on what cues trigger migration. Migrants that use environmental cues (e.g. temperature, resource availability) to trigger migration are often considered to be less vulnerable than migrants that use temporal information (e.g. photoperiod). We developed a proof‐of‐concept model that demonstrates that which cue type performs better in the context of phenological change can depend on differences in seasonal amplitude between the habitats used by a migrant. Environmental cues perform better than temporal cues when the habitat that undergoes a phenological change has a larger seasonal amplitude. This result aligns with observations that populations of short‐distance migrants that use environmental cues are less likely to decline as a consequence of phenological mismatch than long‐distance avian migrants that use temporal cues. Temporal cues perform better than environmental cues when the habitat that undergoes a phenological change has a smaller seasonal amplitude. This result may correspond to empirical scenarios where the more seasonal habitat is associated with variation in precipitation patterns or human actions that are not changing in phenology. In addition to these results, we offer distinctions that will help clarify future work on phenological mismatch. First, we highlight the difference between the cue accuracy (difference between the timing of migration using the cue and optimal migration timing) and the cue efficacy (the difference between the fitness using the cue and the fitness using optimal migration). Second, we recommend considering both how the benefits available from migrating and the benefits that are captured by migrants change with phenological change.

Enhancing Software‐Defined Networking With Dynamic Load Balancing and Fault Tolerance Using a Q‐Learning Approach

ABSTRACTThe Software‐Defined Networking (SDN) paradigm represents a fundamental shift in networking by decoupling the control plane from the data plane in network devices. This architectural change offers numerous advantages, including network programmability and centralized management capabilities, which improve scalability and efficiency compared to conventional network architectures. However, the dynamic nature of network traffic presents overload challenges, both temporally and spatially, especially in multi‐controller SDN settings. To address these challenges, this paper presents an approach leveraging network traffic patterns for dynamic load balancing. The proposed framework optimizes migration strategies to reduce costs and enhance in‐packet request‐response rates. By exploiting load ratio variance across controllers, the architecture identifies optimal migration triplets, encompassing migration‐in and migration‐out domains by selecting a subset of switches. The architecture utilizes online Q‐learning technology to achieve optimal controller load balancing while minimizing associated expenses. The proposed approach ensures stability and scalability by imposing limits to maintain maximum efficiency and reduce migration conflicts. It iteratively converges to an optimal policy through a comprehensive set of simulations performed on switches under a wide range of load distribution situations. These results highlight the effectiveness and adaptability of the proposed methodology in addressing the intricacies present in dynamic network settings, encouraging further progress in the field of SDN technologies and their real‐world applications.

Migratory behaviour of Ipswich Sparrows during spring

Optimal migration theory predicts birds will follow migration strategies that minimize time, energy, or predation risk, and these strategies employ specific and conflicting behaviours. Many behaviours driving songbird strategies are relatively well known, however gaps remain, especially for spring migration. Here, we describe the behaviours of a short-distance migrant, the Ipswich Sparrow ( Passerculus sandwichensis princeps Maynard, 1872), during spring migration. We determined the start and end dates of migration, migratory duration, travel speed, distance per night, and stopover location and duration. We also examined whether behaviours differed by sex and age. Sparrows migrated between March and June, and took an average of 29.1 days at a speed of 7.2 ± 9.4 m/s (SD) to fly ∼1574 km to their breeding grounds. Sparrows flew approximately 100 km/night and made stopovers of >7 days adjacent to ecological barriers. Males migrated before females, and behaviour did not differ with age. Overall, Ipswich Sparrows had a relatively slow travel speed and covered short distances each night consistent with efforts to minimize energy costs or predation risk. They had prolonged stopovers suggesting that they were also trying to minimize time. More work is required to determine which strategy sparrows may be using during spring migration.

A co-optimized control method of key parameters during operating state migration in zinc electrolysis process

Electrolysis is the primary energy-consuming process in zinc hydrometallurgy. Time-of-use pricing policy has caused changes in the optimal operating conditions of the electrolysis cell, necessitating adjustments to key parameters such as current density and the acid-to-zinc ratio to migrate it to the desired operational state. However, while the current density can be switched instantaneously, adjustments to the acid-to-zinc ratio occur slowly, leading to a prolonged mismatch between these key parameters, increasing energy consumption. To address this problem, this paper proposes a co-optimized control method of key parameters during the operating state migration in zinc electrolysis process. First, a co-optimization model is established with current density and acid-to-zinc ratio as decision variables. Next, a cascade control framework is designed in which the current density controller is subordinate to the acid-to-zinc ratio controller, transforming the problem of solving the co-optimization model into a parameter optimization problem for the acid-to-zinc ratio controller. Finally, a fitness function representing energy efficiency, control performance, and zinc yield during the operating state migration in zinc electrolysis process is designed, and a heuristic algorithm is employed to find the optimal parameters for the acid-to-zinc ratio controller. This achieves the optimal trajectory migration of the operating state in the zinc electrolysis process. Simulation experiments demonstrate that the proposed method can reduce energy consumption during the operating state migration without compromising production efficiency, offering a new approach to energy-saving in the zinc electrolysis industry.

Quantitative detection of plasticizer migration in starch/polyvinyl alcohol film with UV-Vis and GC-MS/MS

In this work, the migration of urea and/or formamide plasticizers in crosslinked starch/polyvinyl alcohol (PVA) films under UV irradiation has been accurately quantified by using a combination of GC-MS/MS and UV–Vis analysis. The crosslinked starch/PVA films, prepared with different amide plasticizers (urea, formamide, urea-formamide), were characterized via scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and X-ray photoelectron spectroscopy (XPS). After 7 days of UV irradiation, the starch/PVA film plasticized with urea-formamide (A7-SPF-UF) exhibited a flat surface morphology, high crystal stability, and highest thermal stability. The migration ratio of urea and formamide for A7-SPF-UF was also quantified to be 3.2 % and 4.5 %, respectively, using GC-MS/MS and UV–Vis analysis, indicating optimal migration resistance. The synergistic effect of urea and formamide as plasticizers built the dense network structure of An-SPF-UF and improved the UV aging resistance, toughness and durability of the film. On one hand, this enhances the market competitiveness of the products; on the other hand, it promotes the development of environmentally friendly materials, aligning with sustainability requirements.

Load-aware switch migration for controller load balancing in edge–cloud architectures

As the fundamental infrastructure for edge–cloud architectures, the inter-datacenter elastic optical network is used for data analysis and processing. As the demand for applications increases, the large number of service requests increases the processing overhead in the control plane, resulting in unbalanced controller loads. Existing switch migration mechanisms have been proposed for controller load balancing. Unfortunately, most of the existing mechanisms only consider the switch with the highest flow request rate as the migration object in the process of switch selection, and ignore the migration cost generated in the switch migration activity, such as the update cost of flow request message and the deployment cost of migration rule, which may increase the controller load. Additionally, most of them choose the controller with light load as the target controller to associate with the switch to be migrated, without considering whether the target controller is overloaded after the switch migration, which leads to the low load balancing performance of the controller. In view of the above problems, this paper proposes a Load-Aware Switch Migration (LASM) mechanism in edge–cloud architectures. The LASM mechanism models and analyses the cost metrics affecting switch migration and selects lower-cost switches from overloaded controller-controlled domain networks for migration activities. Besides, the LASM mechanism models switch migration based on the 0-1 knapsack problem and avoids overloading the target controllers through a greedy policy to achieving optimal migration activities. The experimental results show that the proposed LASM mechanism improves controller load balancing performance by an average of 34.3%, eliminates migration costs by 30.2%, and reduces response times by an average of 39.3%, respectively, compared to existing solutions.

Neohesperidin exerts subtle yet comprehensive regulation of mouse dental papilla cell-23 in vitro

ObjectiveThe molecular regulation of odontoblasts in dentin formation remains largely uncharacterized. Using neohesperidin (NEO), a well-documented osteoblast regulator, we investigated whether and how NEO participates in odontoblast regulation through longitudinal treatments using various doses of NEO. DesignMouse dental papilla cell-23 (MDPC-23) served as a model for odontoblasts. MDPC-23 were treated with various doses of NEO (0, 1, 5, 10, 15, 20 μmol/L). Proliferation was assessed using the Cell counting kit-8 assay. Survival/apoptosis was assayed by live/dead ratio. Migration capability was assessed using scratch healing and Transwell migration assays. Mineralization was assessed using alkaline phosphatase staining and alizarin red staining. The expression levels of four key genes (Runx2, osteocalcin [OCN], β-catenin, and bone morphogenetic protein [BMP]−2) representing NEO-induced differentiation of MDPC-23 were measured by quantitative reverse transcription polymerase chain reaction. ResultsThe proliferation trajectories of MDPC-23 treated with the five doses of NEO demonstrated similar curves, with a rapid increase in the 10 μmol/L NEO condition after 48 h of treatment. Similar dose-dependent trajectories were observed for survival/apoptosis. All four key genes representing odontogenic differentiation were upregulated in MDPC-23 induced by NEO treatments at two optimal doses (5 μmol/L and 10 μmol/L). Optimal migration and mobility trajectories were observed in MDPC-23 treated with 10 μmol/L NEO. Optimal mineralization was observed in MDPC-23 treated with 5 μmol/L NEO. ConclusionNEO can subtly regulate odontoblast proliferation, differentiation, migration, and mineralization in vitro. NEO at 5–10 μmol/L offers a safe and effective perspective for clinical promotion of dentin bridge formation in teenagers.

Seasonal patterns and processes of migration in a long-distance migratory bird: energy or time minimization?

Optimal migration theory prescribes adaptive strategies of energy, time or mortality minimization. To test alternative hypotheses of energy- and time-minimization migration we used multisensory data loggers that record time-resolved flight activity and light for positioning by geolocation in a long-distance migratory shorebird, the little ringed plover, Charadrius dubius. We could reject the hypothesis of energy minimization based on a relationship between stopover duration and subsequent flight time as predicted for a time minimizer. We found seasonally diverging slopes between stopover and flight durations in relation to the progress (time) of migration, which follows a time-minimizing policy if resource gradients along the migration route increase in autumn and decrease in spring. Total flight duration did not differ significantly between autumn and spring migration, although spring migration was 6% shorter. Overall duration of autumn migration was longer than that in spring, mainly owing to a mid-migration stop in most birds, when they likely initiated moult. Overall migration speed was significantly different between autumn and spring. Migratory flights often occurred as runs of two to seven nocturnal flights on adjacent days, which may be countering a time-minimization strategy. Other factors may influence a preference for nocturnal migration, such as avoiding flight in turbulent conditions, heat stress and diurnal predators.

Predicting resilience of migratory birds to environmental change.

The pace and scale of environmental change represent major challenges to many organisms. Animals that move long distances, such as migratory birds, are especially vulnerable to change since they need chains of intact habitat along their migratory routes. Estimating the resilience of such species to environmental changes assists in targeting conservation efforts. We developed a migration modeling framework to predict past (1960s), present (2010s), and future (2060s) optimal migration strategies across five shorebird species (Scolopacidae) within the East Asian-Australasian Flyway, which has seen major habitat deterioration and loss over the last century, and compared these predictions to empirical tracks from the present. Our model captured the migration strategies of the five species and identified the changes in migrations needed to respond to habitat deterioration and climate change. Notably, the larger species, with single or few major stopover sites, need to establish new migration routes and strategies, while smaller species can buffer habitat loss by redistributing their stopover areas to novel or less-used sites. Comparing model predictions with empirical tracks also indicates that larger species with the stronger need for adaptations continue to migrate closer to the optimal routes of the past, before habitat deterioration accelerated. Our study not only quantifies the vulnerability of species in the face of global change but also explicitly reveals the extent of adaptations required to sustain their migrations. This modeling framework provides a tool for conservation planning that can accommodate the future needs of migratory species.

Spatiotemporally Mapping Non-Grain Production of Winter Wheat Using a Developed Auto-Generating Sample Algorithm on Google Earth Engine

Spatiotemporally mapping winter wheat is imperative for informing and shaping global food security policies. Traditional mapping methods heavily rely on sufficient and reliable samples obtained through labor-intensive fieldwork and manual sample collection. However, these methods are time-consuming, costly, and lack timely and continuous data collection. To address these challenges and fully leverage remote sensing big data and cloud computing platforms like Google Earth Engine (GEE), this paper developed an algorithm for Auto-Generating Winter Wheat Samples for mapping (AGWWS). The AGWWS utilizes historical samples to determine the optimal migration threshold by measuring Spectral Angle Distance (SAD), Euclidean Distance (ED), and Near-Infrared band Difference Index (NIRDI). This facilitates the auto-generation of winter wheat sample sets for the years 2000, 2005, 2010, 2015, and 2021. Approximately two-thirds of the samples were allocated for training, with the remaining one-third used for validating the mapping method, employing the One-Class Support Vector Machine (OCSVM). The Huang–Huai–Hai (HHH) Plain, a major winter wheat production region, was selected to perform the algorithm and subsequent analysis on. Different combinations of the hyper-parameters, gamma and nu, of the OCSVM based on the Gaussian Radial Basis Function Kernel were tested for each year. Following correlation analysis between the winter wheat area derived from the generated maps and the national statistical dataset at the city level, the map with the highest corresponding R2 was chosen as the AGWWS map for each year (0.77, 0.77, 0.80, 0.86, and 0.87 for 2000, 2005, 2010, 2015, and 2021, respectively). The AGWWS maps ultimately achieved an average Overall Accuracy of 81.65%. The study then explores the Non-Grain Production of Winter Wheat (NGPOWW) by analyzing winter wheat change maps from 2000–2005, 2005–2010, 2005–2010, and 2015–2021 in the HHH Plain. Despite an overall increase in the total planted area of winter wheat, the NGPOWW phenomena has led to concerning winter wheat planting marginalization. Compensatory winter wheat areas are notably situated in mountainous and suburban cultivated lands with low qualities. Consequently, despite the apparent expansion in planted areas, winter wheat production is anticipated to be adversely affected. The findings highlight the necessity for improved cultivated land protection policies monitoring the land quality of the compensation and setting strict quota limits on occupations.

Lightweight Imitation Learning for Real-Time Cooperative Service Migration

Due to the revolution of communication technology, the rapidly increasing number of mobile devices in edge networks generates various real-time service requests, requiring a considerable volume of heterogeneous resources all the time. However, edge devices with limited resources cannot afford substantial learning cost, while migrating services requires heterogeneous resources, especially for dynamic networks. To address these issues, we first establish a cooperative service migration framework and formulate a bi-objective optimization problem to optimize service performance and cost. By analyzing the optimal migration ratio of service cooperative migration, we propose an offline expert policy based on global states to provide optimal expert demonstrations. To realize real-time service migration based on observable states, we design a lightweight online agent policy to imitate expert demonstrations and leverage meta update to accelerate the model transfer. Experimental results show that our algorithm is exceptional in training cost and accuracy, and has significant superiors in multiple metrics such as the service latency and payment under different workloads, compared to other representative algorithms.

Energy-efficient secure dynamic service migration for edge-based 3-D networks

AbstractIn communication networks, where users are highly mobile, migrating edge servers that are performing services closer to users, i.e., service migration, is essential to maintain the high quality of service (QoS). However, existing dynamic service migration techniques face two distinct challenges: (1) The security and energy consumption of service migration systems need to be optimised urgently; (2) The uncertainty of user movement makes it difficult to develop optimal service migration strategies, especially in future three-dimensional (3-D) communication networks. To address these challenges, we propose a novel energy-efficient secure 3-D dynamic service migration framework for communication networks. We then quantify the cost of service migration based on the proposed framework considering security, energy efficiency and delay and present a solution based on a deep reinforcement learning (DRL) approach to make migration decisions optimally in the 3-D communication network. We also propose a universal formula for measuring the reliability value of intelligent autonomous nodes in order to reduce the energy consumption and delay of the proposed security paradigm and to optimise the service migration decision making. Simulation results demonstrate our proposed migration strategy for 3-D communication network services outperforms the baseline solutions in terms of reducing communication network delay and energy consumption while preserving migration security. Moreover, the results confirm the effectiveness of the proposed reliability value calculation approach applied to improve the QoS in the secured edge networks.

Computing and Communication Cost-Aware Service Migration Enabled by Transfer Reinforcement Learning for Dynamic Vehicular Edge Computing Networks

Due to the high mobility of vehicles, service migration is inevitable in vehicular edge computing (VEC) networks. Frequent service migrations incur prohibitive migration cost including the computing cost (e.g., increased computing delay) and communication cost (e.g., occupied backhaul bandwidth). Yet existing service migration schemes are usually designed without considering the impact of the computing cost. This paper considers the impact of computing and communication cost jointly, and proposes a computing and communication cost-aware service migration scheme for VEC networks (i.e., CA-migration). Taking the service delay as a QoS metric for VEC networks, this paper formulates a migration optimization problem aiming to maximize the services' satisfaction degree of delay (i.e., the probability that the service delay is smaller than the service delay requirement), where both the communication cost and computing cost affect the services' satisfaction degree. Since the optimization problem is a constrained non-linear integer programming problem, it is difficult to solve. Moreover, the VEC networks are highly dynamic. Thus, a fast transfer reinforcement learning (fast-TRL) method combining transfer learning and reinforcement learning is proposed to provide an adaptive service migration scheme in dynamic VEC networks. Simulation results show that compared with existing schemes, the proposed CA-migration scheme can increase the satisfaction degree by up to 30%, and needs 25% less training time to obtain the optimal service migration policy.

Measuring Piezo1 and Actin Polarity in Chemokine-Stimulated Jurkat Cells During Live-Cell Imaging.

The process of T-lymphocyte migration involves a complex interplay of chemical and mechanical signals. Mechanotransduction mechanisms in T lymphocytes enable them to efficiently navigate through diverse architectural and topographical features of the dynamic tissue macro- and micro-niches encountered during immune responses. Piezo1 mechanosensors are crucial for driving optimal T-cell migration by driving actin-cytoskeletal remodeling. Chemokine-stimulated T lymphocytes demonstrate significant asymmetry or polarity of Piezo1 and actin along the cell axis. The establishment and maintenance of polarity in migrating cells are paramount for facilitating coordinated and directional movements along gradients of chemokine signals. Live-cell imaging techniques are widely employed to study the trajectories of migrating cells. Our approach expands upon current methodologies by not only tracking migrating cells but also imaging fluorescently labeled cellular components. Specifically, our method enables measurement of protein enrichment in the front and rear halves of the moving cell by analyzing the temporal direction of cell trajectories, subsequently bisecting the cell into front-back halves, and measuring the intensities of the fluorescent signals in each cell half at each time frame. Our protocol also facilitates the quantification of the angular distribution of fluorescent signals, enabling visualization of the spatial distribution of signals relative to the direction of cell migration. The protocol describes the examination of polarity in chemokine-treated Jurkat cells transfected with Piezo1-mCherry and actin-GFP constructs. This approach can be extended to live-cell imaging and polarity assessment of other fluorescently labeled proteins. Key features • This experimental protocol allows real-time imaging of Jurkat cells expressing two fluorescent proteins (Piezo1 mCherry and actin-GFP). • Measures cell polarity by examining spatial enrichment of Piezo1 and actin proteins within the front and rear halves of a moving Jurkat cell. • The protocol enables analysis of cell polarity in 2D tracks of moving cells. • Polarity analysis includes measuring fluorescent signal intensities in front-rear halves of a moving cell and calculation of signal polarization angles relative to the cell trajectory.

Monte Carlo‐based service migration under multiple constraints in mobile edge computing

AbstractMobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.

An elastic framework construction method based on task migration in edge computing

AbstractEdge computing (EC) serves as an effective technology, empowering end‐users to attain high bandwidth and low latency by offloading tasks with high computational demands from mobile devices to edge servers. However, a major challenge arises when the processing load fluctuates continuously, leading to a performance bottleneck due to the inability to rescale edge node (EN) resources. To address this problem, the approach of task migration is introduced, and EN load prediction model, the resource constrained model, optimal communication overhead model, optimal task migration model, and energy consumption model are built to form a theoretical foundation from which to propose a task migration based resilient framework construction method in EC. With the aid of the domino effect and the combined effect of task migration, a dynamic node‐growing algorithm (DNGA) and a dynamic node‐shrinking algorithm (DNSA), both based on the task migration strategy, are proposed. Specifically, the DNGA smoothly expands the EN scale when the processing load increases, while the DNSA shrinks the EN scale when the processing load decreases. The experimental results show that for standard benchmarks deployed on an elastic framework, the proposed method realizes a smooth scaling mechanism in the EC, which reduces the latency and improves the reliability of data processing.