Migration Costs Research Articles

The underlying causes of differential migration: assumptions, hypotheses, and predictions

ABSTRACTMechanisms governing the migratory decisions of birds have long fascinated ecologists and sparked considerable debate. Identifying factors responsible for variation in migration distance, also known as differential migration, has been a popular approach to understanding the mechanisms underlying migratory behaviour more generally. However, research progress has been slowed by the continued testing of overlapping, non‐mechanistic, and circular predictions among a small set of historically entrenched hypotheses. We highlight the body size hypothesis and suggest that the predictions commonly tested have impeded progress because body size relationships with migration distance are predictions made by several distinct hypotheses with contrasting mechanisms. The cost of migration itself has not been adequately accounted for in most hypotheses, and we propose two flight efficiency hypotheses with time‐ and energy‐minimizing mechanisms that allow individuals to mitigate the risks inherent to longer migrations. We also advance two conceptual versions of the social dominance hypothesis based on two distinct underlying mechanisms related to distance minimization and food maximization that will help clarify the role of competition in driving migratory decisions. Overall, we describe and refine 12 mechanistic hypotheses proposed to explain differential migration (along with several other special‐case hypotheses), seven of which have underlying mechanisms related to food limitation as past research has identified this to be an important driver of differential migration. We also thoroughly reviewed 145 publications to assess the amount of support for 10 critical assumptions underlying alternative hypotheses for differential migration in birds. Our review reveals that surprisingly few studies explicitly evaluate assumptions within a differential migration context. Generating and testing strong predictions and critical assumptions underlying mechanisms of alternative hypotheses will improve our ability to differentiate among these explanations of differential migration. Additionally, future intraspecific progress will be greatest if investigators continue to focus on mechanisms underlying variation in migration distance within rather than among demographic classes, as previous research has found differing mechanisms to be responsible for differential migration among demographic classes. Interspecifically, a thorough comparative analysis that seeks to explain variation in migration distance among species would broaden both our understanding of the mechanisms regulating current differential migration patterns and those that led to the evolution of migration more generally. Collectively, we provide a framework that, together with advances in animal‐borne tracking and other technology, can be used to advance our understanding of the causes of differential migration distance, and migratory decisions more generally.

Exploring an adaptive management model for “status-optimization-regulation” of mining city in transition: A case study of Huangshi, China

Mining cities face the challenges of ecological transformation and sustainable development after mineral depletion. Thus, ecological space optimization and adaptive management are pivotal after ecological restoration project, but easily neglected. Taking Huangshi in Hubei Province as an example, a top-down adaptive management model for regional mining ecological space integrating status-optimization-regulation (SOR) was established based on land system resilience (LSR) evaluation, circuit theory, complex network model and community discovery algorithm. The results showed that i) As the LSR increased, the land use structure shifted from non-ecological zones to forest and cropland, indicating that balancing agriculture and forest protection was crucial for increasing LSR in Huangshi. i) The ecological networks (ENs) of Huangshi had an irregular fishnet pattern, with densely intricate corridors in the south and broader, sparser ones in the north, and numerous ecological barriers near the mining area. Furthermore, the distribution of ecological sources and corridors displayed complementarity and hierarchy. iii) The optimized ENs exhibited higher connectivity and efficiency under disturbance, reducing corridor redundancy and migration costs. iv) The ENs clusters were classified into five types based on ecological connectivity: ecological buffer zone, priority restoration zone, moderate restoration zone, natural restoration zone, and moderate development zone, and tailored ecological regulatory strategies were proposed. The findings provide practice-oriented guidance for the sustainable development of mining cities, and offer a direct approach to support conscious, clear, and coherent adaptive management of ecological restoration in rapidly changing environments.

Oxidative state is associated with migration distance, but not traits linked to flight energetics

Flight can be highly energy demanding, but its efficiency depends largely on flight style, wing shape and wing loading, and a range of morphological and lifestyle adaptations that can modify the cost of sustained flight. Such behavioural and morphological adaptations can also influence the physiological costs associated with migration. For instance, during intense flight and catabolism of reserves, lipid damage induced by pro‐oxidants increases, and to keep oxidative physiological homeostasis under control, the antioxidant machinery can be upregulated. Studies on the oxidative physiology of endurance flight have produced contradictory results, making generalization difficult, especially because multispecies studies are missing. Therefore, to explore the oxidative cost of flight and migration, we used samples collected during the breeding season from 113 European bird species and explored the associations of measures of antioxidant capacity (total antioxidant status, uric acid and glutathione concentration) and oxidative damage of lipids (malondialdehyde) with variables reflecting flight energetics (year‐round or specifically during migration) using a phylogenetic framework. We found that none of the traits predicting year‐round flight energy expenditure (flight style, wing morphology and flight muscle morphology) explained any measures of oxidative state. Our results suggest that birds endure their everyday flight exercise without or with low oxidative cost. However, oxidative damage to lipids and one component of the endogenous antioxidant system (uric acid), measured after the end of spring migration on breeding adult birds, increased with migration distance. Our results suggest that migration could have oxidative consequences that might be carried over to subsequent life‐history stages (breeding).

The Future of Data Warehousing: Trends, Technologies, and Challenges in the Era of Big Data, Cloud Computing, and Artificial Intelligence

This article explores the future of data warehousing, discussing emerging trends, technologies, and challenges shaping the landscape of data management and analytics in the era of big data, cloud computing, and artificial intelligence. It delves into topics such as the rise of cloud data warehouses, the increasing adoption of artificial intelligence and machine learning in data warehousing for advanced analytics and automation, the growing importance of data governance, privacy, and security in the face of evolving regulations, and the need for real-time data processing and analytics to support agile decision-making. The article also discusses the challenges faced by organizations in adopting and implementing these new technologies, such as the need for skilled personnel, the complexity of data integration from diverse sources, the cost of infrastructure and migration, and the ethical considerations surrounding the use of AI and ML in data warehousing. It concludes by highlighting the potential of data warehousing to drive innovation, improve operational efficiency, and gain a competitive advantage in the digital age.

Cost of resilience to climate change: migration, conflicts, and epidemics in imperial China

Abstract A growing scholarship is focusing on the cost of social resilience to climate change in the past. Among different resilience strategies, migration could be effective for nomadic societies despite the potential consequences of conflicts and epidemics. Thus, this study utilizes historical records to statistically investigate the linkages among nomadic migrations, nomad–farmer conflicts, and epidemics under climate change and population pressure in imperial China (200 BCE–1840 CE) on the national and provincial scales. The current study will first attempt to empirically identify and analyze the cost of resilience to climate change mainly in the direction from nomadic societies to agrarian societies in historical China. In particular, we show the cost of nomadic migration passed in a chain mechanism as ‘climate change → nomadic migration → nomad–farmer conflicts → epidemics.’ Nomad–farmer conflicts were one direct effect of nomadic migration, while epidemics were an indirect one. Spatially, more provinces were affected under the direct effect than under the indirect effect. Furthermore, the first level of chain ‘nomadic migration → nomad–farmer conflicts’ covers more provinces than the second level ‘nomad–farmer conflicts → epidemics’. These empirical results remind us to identify and avoid the cost of resilience as early as possible before the cost may transmit further in a chain manner. However, the provinces outside the concentrated nomad–farmer conflicts did not demonstrate significant linkages between conflicts and epidemics, which highlights the importance of peaceful cross-civilizational and inter-societal interactions against common challenges of climate change. This study with a cross-scale perspective in geography provides a theoretical implication to improve the current understanding on climate justice and have a practical value to avoid or minimize the cost of resilience.

OPTIMIZING PROCESSOR WORKLOADS AND SYSTEM EFFICIENCY THROUGH GAME-THEORETIC MODELS IN DISTRIBUTED SYSTEMS

The primary goal of this research is to examine how different strategic behaviors adopted by processors affect the workload management and overall efficiency of the system. Specifically, the study focuses on the attainment of a pure strategy Nash Equilibrium and explores its implications on system performance. In this context, Nash Equilibrium is considered as a state where no player has anything to gain by changing only their own strategy unilaterally, suggesting a stable, yet not necessarily optimal, configuration under strategic interactions. The paper rigorously develops a formal mathematical model and employs extensive simulations to validate the theoretical findings, thus ensuring the reliability of the proposed model. Additionally, adaptive algorithms for dynamic task allocation are proposed, aimed at enhancing system flexibility and efficiency in real-time processing environments. Key results from this study highlight that while Nash Equilibrium fosters stability within the system, the adoption of optimal cooperative strategies significantly improves operational efficiency and minimizes transaction costs. These findings are illustrated through detailed 3D plots and tabulated results, which provide a detailed examination of how strategic decisions influence system performance under varying conditions, such as fluctuating system loads and migration costs. The analysis also examines the balance between individual processor job satisfaction and overall system performance, highlighting the effect of rigid task reallocation frameworks. Through this study, the paper not only improves our understanding of strategic interactions within computational systems but also provides key ideas that could guide the development of more efficient computational frameworks for various applications.

Adaptive load balancing in distributed cloud environment: Hybrid Kookaburra-Osprey optimization algorithm

A distributed cloud environment is characterized by the dispersion of computing resources, services, and applications across multiple locations or data centres. This distribution enhances scalability, redundancy, and resource utilization efficiency. To optimize performance and prevent any single node from becoming a bottleneck, it is imperative to implement effective load-balancing strategies, particularly as user demands vary and certain nodes experience increased processing requirements. This research introduces an Adaptive Load Balancing (ALB) approach aimed at maximizing the efficiency and reliability of distributed cloud environments. The approach employs a three-step process: Chunk Creation, Task Allocation, and Load Balancing. In the Chunk Creation step, a novel Improved Fuzzy C-means clustering (IFCMC) clustering method categorizes similar tasks into clusters for assignment to Physical Machines (PMs). Subsequently, a hybrid optimization algorithm called the Kookaburra-Osprey Updated Optimization Algorithm (KOU), incorporating the Kookaburra Optimization Algorithm (KOA) and Osprey Optimization Algorithm (OOA), allocates tasks assigned to PMs to Virtual Machines (VMs) in the Task Allocation step, considering various constraints. The Load Balancing step ensures even distribution of tasks among VMs, considering migration cost and efficiency. This systematic approach, by efficiently distributing tasks across VMs within the distributed cloud environment, contributes to enhanced efficiency and scalability. Further, the contribution of the ALB approach in enhancing the efficiency and scalability of distributed cloud environments is evaluated through analyses. The KBA is 1189.279, BES is 629.240, ACO is 1017.889, Osprey is 1147.300, SMO is 1215.148, APDPSO is 1191.014, and DGWO is 1095.405, respectively. The resource utilization attained by the KOU method is 1224.433 at task 1000.

Improved ECC cryptosystem-based lightweight authentication for SDN-based edge computing in IoT-enabled healthcare systems

The Internet of Things (IoT) consists of smart devices with limited resources that can identify and analyze data. In IoT-enabled healthcare systems, the security of IoT devices and the data they contain is complex. These devices in the healthcare industry, edge computing can provide low-latency information services at a reasonable cost. This work proposes a security infrastructure for Software Defined Network (SDN)-based edge computing in IoT-enabled healthcare systems consisting of three steps: Lightweight authentication, collaborative edge computing and job migration. The lightweight authentication step involves both Improved Lightweight Key Management (ILKM) and Improved Elliptic Curve Cryptography (IECC) schemes to ensure authentication among the devices and edge servers. Moreover, the patient’s data in IoT devices are scheduled to the appropriate edge server by examining the load balancing in the collaborative edge computing phase. This is done optimally using the adopted hybrid optimization model, Osprey Assisted Coati Optimization Algorithm (OACOA). Further, job migration takes place, in which the data is allocated to the edge server by comparing the capacity of edge servers and the data gets migrated to other servers by considering migration cost when the capacity of the edge server is overloaded. Finally, the efficiency of the suggested OACOA scheme is evaluated over traditional models with regard to several metrics. When considering the edge-server 30, the OACOA scheme achieves a makespan of 385, while conventional methods acquired fewer makespan ratings. Also, the OACOA approach obtained the highest security ratings (0.7143) on edge-server 20 when compared to existing schemes.

African Migration at a Crossroads: The Social and Theoretical Implications of Emerging International Migration Trends

This study examines international migration trends in Africa since the mid-1960s. It argues that African international migration trends are at a turning point that could significantly affect the future of migration studies. New African immigrant communities are emerging in Asia, South America, and other world regions, while the influence of state and non-state institutions is increasing. Moreover, interstate migration in Africa is now more disconnected from the legacies of the continent's colonial labor migration systems. By 2050, Africa is also expected to have the world's largest population in the age-groups that typically have the highest probabilities of migration, while per-capita incomes will increase in ways that could reduce the costs of migration. Although Africa's influence on future migration research is likely to increase, progress in developing theories to examine its emerging trends has been limited. With few exceptions, studies on migration in Africa focus on theory application rather than theory building. New theoretical approaches are therefore needed to adequately situate Africa's emerging migration trends within the larger context of migration studies. The building blocks for developing these theories include decolonial approaches that highlight African perspectives and the integration of the substantive influences of future migration trends into theory-building processes.

Deep Learning-Driven Workload Prediction and Optimization for Load Balancing in Cloud Computing Environment

Abstract Cloud computing revolutionizes as a technology that succeeds in serving large-scale user demands. Workload prediction and scheduling tend to be factors dictating cloud performance. Forecasting the future workload in due to avoid unfair resource allocation, emerges to be a crucial inspecting feature for enhanced performance. The aforementioned issues of interest are addressed in our work by soliciting a Deep Learning driven Max-out prediction model, which efficiently forecasts the future workload by providing a balanced approach for enhanced scheduling with the Tasmanian Devil-Bald Eagle Search (TDBES) optimization algorithm. The results obtained proved that the TDBES scored efficacy in makespan with 16.75%, migration cost with 14.78%, and a migration efficiency rate of 9.36% over other existing techniques like DBOA, WACO, and MPSO, with additional error analysis of prediction performance using RMSE, MAP, and MAE, among which our contributed approach overrides traditional methods with least error.

Evidence of longitudinal differences in spring migration strategies of an Arctic-nesting goose.

During spring, migratory birds are required to optimally balance energetic costs of migration across heterogeneous landscapes and weather conditions to survive and reproduce successfully. Therefore, an individual's migratory performance may influence reproductive outcomes. Given large-scale changes in land use, climate, and potential carry-over effects, understanding how individuals migrate in relation to breeding outcomes is critical to predicting how future scenarios may affect populations. We used GPS tracking devices on 56 Greater White-fronted Geese (Anser albifrons) during four spring migrations to examine whether migration characteristics influenced breeding propensity and breeding outcome. We found a strong longitudinal difference in arrival to the breeding areas (18 days earlier), pre-nesting duration (90.9% longer), and incubation initiation dates (9 days earlier) between western- and eastern-Arctic breeding regions, with contrasting effects on breeding outcomes, but no migration characteristic strongly influenced breeding outcome. We found that breeding region influenced whether an individual likely pursued a capital or income breeding strategy. Where individuals fell along the capital-income breeding continuum was influenced by longitude, revealing geographic effects of life-history strategy among conspecifics. Factors that govern breeding outcomes likely occur primarily upon arrival to breeding areas or are related to individual quality and previous breeding outcome, and may not be directly tied to migratory decision-making across broad scales.

Resource prioritization and allocation in fog computing using hybrid optimization

With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promising solutions to reduce service latency and enhance Quality of Service (QoS). However, allocating resources effectively remains challenging due to factors such as uncertainty, mobility, heterogeneity, and limited resources in fog computing environments. Traditional resource allocation (RA) algorithms often fall short of addressing these complexities. This study proposes a novel approach to RA in fog computing, utilizing a non-linear function to optimize resource allocation. An objective function is introduced, incorporating multi constraints such as resource utilization, service response rate, makespan, migration cost, and communication cost. The methodology emphasizes efficient resource allocation in crucial scenarios, facilitating rapid resource distribution where needed. The novel Coati Integrated Beluga Whale Optimization (CI-BWO) strategy is proposed to achieve optimal resource allocation in fog computing environments. By leveraging CI-BWO, this research aims to overcome the limitations of traditional RA methods and enhance the performance and scalability of fog computing applications. Finally, the superiority of the suggested strategy is assessed by comparison with many existing methods. When the task count is 200, the developed CI-BWO attained less migration cost of around 1.287, while existing models have acquired higher migration costs.

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.

Fleeing a failing state: Self-selection, earnings, and migration costs

Three-quarters of the global migrant population have fled fragile contexts, with 64 percent hosted by similarly vulnerable countries. These contexts account for about 75 percent of those living in extreme poverty. Despite the scale of this phenomenon, the extent to which the self-selection of South–South migrants differs from those migrating to more developed countries remains poorly understood. In this paper, I investigate the self-selection of Venezuelan migrants during the 2015–2021 crisis, which led to greater migration to less developed countries such as Colombia and Peru, compared to more developed ones like the United States and Chile. Using individual-level data representative of the Venezuelan population and similar data on Venezuelan migrants in these key destination countries, the study finds that migrants are generally positively selected in terms of education compared to those who remain in Venezuela, with migrants to developed countries being positively sorted with respect to those to developing countries. However, comparing the cumulative distribution functions of pre-migration predicted earnings reveals that migrants to developing countries are negatively selected relative to stayers, while migrants to developed countries are positively selected. This highlights the significant role of unobserved abilities in shaping South–South migration patterns. Furthermore, a discrete choice model shows that women and college graduates face lower migration costs, independent of expected earnings at the destination. Factors such as the distance to the destination country and pre-crisis networks also play a crucial role in shaping migration decisions.

Family migration and structural transformation

AbstractThis paper integrates the migration decisions of married couples into a multi‐sector spatial model, investigating their impact on structural transformation, productivity and gender wage gap. Focusing on China, a country characterized by a higher share of agricultural employment and a lower share in services compared with countries with similar income, we uncover a significant gender gap in migration costs among rural married couples. Furthermore, while migration costs have decreased for all demographic groups from 2000 to 2010, the decline was least pronounced for married couples when both partners left agriculture. We find that reducing migration costs for married couples who migrate together would lead to a decline in agricultural employment, a rise in service sector employment, an increase in aggregate productivity and a narrowing of the gender wage gap. Eliminating the gender differences in migration costs would also increase service sector employment and reduce the gender wage gap.

Unpacking migration costs: Heterogeneous effects in EU labor markets

I employ a tractable two-country search model of unemployment with endogenous migration decisions for workers and apply the model to the context of the European Union. I find that migration costs for workers are important factors in determining migration, unemployment and wages. Increasing migration costs increase unemployment and decrease migration, wages and welfare. This headline result is disaggregated into heterogeneous effects across workers with different countries of origin and migration histories. Workers who move more times, or for longer spells, are more affected by costs than workers who move less or not at all, though non-migrating workers experience changes to their labor market outcomes due to the externalities imposed by migrant workers. Using EU data, I find that costly migration lowers welfare by 11%–60% relative to free mobility.

Lifetime internal migration trajectories and social networks: Do repeat migrants fare worst?

While the economic benefits of internal migration are widely documented, the social costs of internal migration have received comparatively less attention. In addition, most studies focus on the impact of the last-recorded migration, ignoring the cumulative impact of successive migrations. Grounded in the life-course trajectory approach to migration and the convoy model of social networks, this paper addresses this gap by applying sequence and cluster analysis to retrospective data from the Survey of Health, Ageing and Retirement in Europe (SHARE) in 26 European countries to establish internal migration trajectories based on the timing, frequency, and direction of migration between NUTS-2 regions. The results reveal that differences in social networks between lifetime stayers, childhood migrants and one-time adult migrants are minimal. A more complex picture emerges for repeat migrants who account for half migrants and are split between return migrants, serial onward migrants, and circular migrants. Regression results show that repeat migrants – whether onward, return, or circular – display social networks less focused on family and more geographically dispersed, which results in a lower frequency of contact than lifetime stayers. However, repeat migrants report the same level of overall satisfaction with their social networks as lifetime stayers, which suggests that they start with different expectations than stayers or simply adjust their expectations in response to the social costs and benefits of migration.

FRICTIONAL SORTING: THE IMPACTS OF DUAL CONSTRAINTS ON MOBILITY AND HOUSING SUPPLY IN CHINA

AbstractUsing an equilibrium sorting model and microdata from China, we evaluate the impacts of dual constraints on mobility and housing supply on workers’ sorting behavior, quantifying the welfare and distributional consequences. Counterfactual simulations show that lowering migration costs associated with institutional restrictions and migrant‐specific amenities would increase welfare and reduce inequality by moving workers from inland to coastal regions in China. These impacts depend on the housing supply elasticity in coastal regions. Similarly, the impacts of relaxing housing supply restrictions depend on mobility constraints. Results highlight the policy complementarities between reducing the two kinds of frictions.

An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

AbstractTraditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real‐time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real‐time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi‐objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing.

Environmental drivers and fitness consequences of partial migration under climate change

Abstract Animals migrate to access seasonally variable resources or to escape unfavorable local conditions. Partial (flexible) migration strategies offer animals a fitness trade-off in which individuals may choose whether to incur the energetic costs of migration depending on the potential benefits associated with avoiding adverse conditions. Partial migration patterns may shift due to anthropogenic land use and climate change, possibly reducing the benefits of migration and resulting in unpredictable migration cues. We examined causes, consequences, and changes in individual-level partial migration of white-tailed deer Odocoileus virginianus by analyzing archival telemetry data (1986-2014) across four distinct populations in the Great Lakes region, North America. We hypothesized that if the costs of migration outweigh benefits, we would see migratory behavior decline. Migratory behavior declined from 75% in 1987 to 11% in 2014 independent of population identity. Annual mortality was higher for migratory white-tailed deer, while increasing minimum temperatures positively impacted survival but did not influence migration. Our results suggest that migratory behavior in white-tailed deer is declining over time and may be associated with declining winter severity. The loss of migratory behavior in large mammals may have ecosystem-level impacts and our study emphasizes the need to better understand and conserve these migratory traditions.