Migration Costs Research Articles

Migration, debt, and transnational livelihood: Indian labour diasporas in the GCC states amid the pandemic

ABSTRACT South Asian labour migration to the GCC States is a debt-financed migration in which labour diasporas mobilise resources from a variety of sources, often at exorbitant interest rates, to cover migration costs. In the event of the COVID pandemic, job losses and involuntary returns compound the problem of debt-financed migration, affecting the transnational livelihood of migrant families. This paper explores how the debt-financed migration shapes the transnational livelihood of Gulf labour diasporas amid the pandemic. Empirically, this paper draws on the experiences of 60 Gulf migrants from the Indian state of Bihar. This study reports that the families diversify their labour resources by joining Gulf labour market and migration generates remittances that provide improved livelihood for their families. Although unforeseen events such as the pandemic may delay migration episodes and thus mount the debt burden on migrants, migrants nevertheless find ways to join the Gulf labour market and erk out a trananational livelihood.

Migration speculation: microfinance and migration in the Global South

Across the Global South, microfinance providers have begun offering formalised pre-departure loans as part of their broader efforts to promote migrant financial inclusion. This article critically examines the logic behind these migration loans, drawing on data from Bangladesh to denaturalise the discursive shifts justifying these programs. Advocates of formalised migration loans view them as enabling the ‘worthwhile investment’ of migration, while reducing migration costs and risks. Yet these claims ignore the systemic precarity of migrant labour, the potential for abuse and dispossession via microcredit, and the ways that formal debt can heighten vulnerability for migrants. In making these claims, I draw attention to the infrastructures that have enabled contemporary forms of migration lending, highlighting that migration loans are now possible precisely because financial institutions have found ways to reshape the risks of lending to mobile populations. As such, I make the case for seeing migration loans as a form of migration speculation—in which migrant experiences become a site of investment and profit for microfinance institutions.

Reinforcement Learning-Based Optimization Framework for Application Component Migration in NFV Cloud-Fog Environments

By decoupling network functions from the underlying hardware, Network Function Virtualization (NFV) allows application components to be implemented as sets of Virtual Network Functions (VNFs) chained in a specific order, represented by VNF-Forwarding Graphs (VNF-FG). Fog computing is instrumental to tap into the full potential of NFV by deploying VNFs in close proximity to end-users, thus decreasing the latency significantly. However, the mobility of end-users and the fog nodes, and the limited fog nodes coverage results in service discontinuity and may increase application delay. Application component migration offers great potential to address this issue. In this paper, we propose a component migration strategy in an NFV-based hybrid cloud/fog system considering the mobility of both end-users and fog nodes. We use the Gauss-Markov mobility model and a random walk mobility model for fog nodes and end-user devices, respectively. We modeled the problem mathematically, which minimizes the aggregated weighted function of application delay and cost. However, considering the mobility of both end-users and fog nodes makes the problem quite complex. Hence, we propose a Deep Reinforcement Learning (DRL) approach to decide where and when to migrate application components and to achieve rapid decision-making. Simulation results demonstrate that the proposed scheme performs well. It offers favorable convergence and outperforms existing algorithms in terms of application delay and migration costs.

QoS-Aware Management Reconfiguration of vNF Service Trees With Heterogeneous NFV Platforms

The rapid development of in-network computing motivates people to consider deploying virtual network functions (vNFs) on heterogeneous platforms that include both software systems like virtual machines and docker containers and hardware systems like programmable data plane switches. Meanwhile, with the emergence of multi-client network services, service providers need to build vNF service trees (vNF-STs) in their substrate networks (SNTs). In this work, we study how to optimize the management reconfiguration of vNF-STs in an SNT equipped with heterogeneous platforms. An integer linear programming (ILP) model is first formulated to consider three common reasons for vNF-ST reconfiguration and reduce both the total resource usage after reconfiguration and the overall vNF migration cost during reconfiguration. Then, we design a two-step algorithm to reduce the time complexity of problem-solving. Specifically, the algorithm first checks all the active vNF-STs to select the vNF-STs that should be reconfigured, and then leverages an approach based on layered auxiliary graphs (LAGs) to reconfigure the selected vNF-STs. Extensive simulations verify the effectiveness of our algorithms on optimizing the management reconfiguration, and demonstrate that they can outperform existing benchmarks.

VM performance-aware virtual machine migration method based on ant colony optimization in cloud environment

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource competition among these VMs, which results in VM performance reduction. Many VM migration (VMM) methods neglect VM performance reduction. Although some performance-aware VM allocation methods were proposed, the performance optimization objective of these methods mainly aimed at guaranteeing service level agreement or reducing VM downtime during migration, and they did not scientifically analyze how the VM performance degrades. Therefore, how to minimize the VM performance reduction for users when migrating VMs remains a major challenge. This paper proposes a VM Performance-Aware VMM method (PAVMM) for both users and cloud service providers. To maximize VM performance for users, it utilizes the VM performance model, which was built in our previous works, to predict the VM performance after migrating VMs. It then establishes an optimization objective of maximizing VM performance for users. Meanwhile, minimizing the number of active PMs and the total migration cost are regarded as additional optimization objectives for cloud service providers. Therefore, we formulate VMM as a multi-objective optimization problem, which tries to maximize VM performance for users and minimize the number of active PMs and the total migration cost for cloud service providers simultaneously. Then an ant colony optimization (ACO)-based algorithm is proposed to solve the NP-hard VMM problem. Lastly, the experiments are conducted to evaluate PAVMM, and the results verify its efficiency.

BalCon — resource balancing algorithm for VM consolidation

Cloud providers handle substantial number of requests to create and delete virtual machines (VMs) on a daily basis, where the unknown sequence of requests eventually leads to resource fragmentation. To mitigate this issue, periodic consolidation of VMs into fewer number of physical hosts is an important cost-saving procedure, closely related to the vector bin-packing problem. In this paper, we propose the BalCon algorithm for consolidation that steadily reduces the number of active hosts and keeps migration costs low. BalCon classifies the cluster’s state and selects one of three heuristics to balance resources for superior consolidation. To evaluate BalCon’s performance with respect to optimality, we introduce integer programming models. BalCon finds 99.7% of the optimal solutions for over 750 problem instances. This outstanding result was achieved due to the Force Step of our algorithm, which is the key improvement detail for common heuristics. We compare BalCon with a modified Sercon heuristic using Huawei and synthetic datasets with two resources for allocation.

Digital twin-assisted and mobility-aware service migration in Mobile Edge Computing

Mobile Edge Computing (MEC) is emerging as one of the key technologies to process massive amount of data at the edge of the network for upcoming 6G networks. In the paradigm of MEC, users can offload the computation-intensive and delay-sensitive tasks to the edge servers, since the capabilities of User Equipment (UE) cannot be not sufficient to meet different quality of service (QoS) requirements. However, due to the limited coverage of edge servers, users can move between multiple edge servers. In such cases, service migration is complicated which arises the challenge of where and when to migrate the service dynamically to maintain acceptable QoS. In addition, high uncertainty of the user mobility and different QoS requirements of the services are challenging. Instead of assigning each user to the nearest edge server, we propose a Digital Twin (DT)-assisted service migration approach to minimize the task completion time. The contributions in this paper are threefold: First, a DT edge network architecture is presented that uses the real-time and historical data to determine service migration strategy. Second, the DT classifies the computing tasks as delay-sensitive task and delay-insensitive tasks to meet the QoS requirements. Third, the DT predicts the mobility of users in the future based on the Hidden Markov Model (HMM) and studies the impact of reducing the cost of service migration. Thus, we propose an optimization algorithm to find optimal resource allocation at each edge server. The simulation results shows that our DT-assisted service migration model can achieve less delay and service migration rate compared with the traditional methods.

Food Insecurity Among Haitian Migrants Living In Brazil.

To analyze the situation of food insecurity among Haitians living in Midwest of Brazil, based on questions of the Brazilian Scale of Food Insecurity (BSFI). A survey was carried out with a probabilistic sampling proportional to sex of Haitians aged over 18 years who lived in Cuiabá and Várzea Grande, Mato Grosso. Haitian migrants were interviewed by bilingual (Haitian Creole/Portuguese) individuals, and for the analysis of the BSFI questions. Because the BSFI is not validated for Haitians, the first eight questions of the scale were assessed separately, according to sex, debts incurred for the migration project and time of residence in Brazil. Chi-square was calculated to test the differences between groups. A total of 404 Haitians were evaluated, 81.9% male individuals, 60.9% aged between 18 and 36 years. Among the respondents, 64.4% were worried about running out of food before having money to buy more food, and 62.6% answered that they ran out of food before having money to buy it. Higher proportions of positive answers were observed for women and those who reported having debts to finance the migration costs to Brazil. The high proportion of positive answers to the BSFS questions by Haitians seems to indicate a situation of social vulnerability and food insecurity, especially among Haitian women, as well as among those who had debts to migrate to Brazil and those who have lived in the country for more than a year.

An Efficient Switch Migration Scheme for Load Balancing in Software Defined Networking

Software-defined networking (SDN) provides increased flexibility to network management through distributed SDN control, and it has been a great breakthrough in network innovation. Switch migration is extensively used for workload balancing among distributed controllers. The time-sharing switch migration (TSSM) scheme proposes a strategy in which more than one controller is allowed to share the workload of a switch via time sharing during overloaded conditions, resulting in the mitigation of ping-pong controller difficulty, a reduced number of overload occurrences, and better controller efficiency. However, it has increased migration costs and higher controller resource consumption during the TSSM operation period because it requires more than one controller to perform. Therefore, we have proposed a strategy that optimizes the controller selection during the TSSM period based on flow characteristics through a greedy set coverage algorithm. The improved TSSM scheme provides reduced migration costs and lower controller resource consumption, as well as TSSM benefits. For its feasibility, the implementation of the proposed scheme is accomplished through an open network operating system. The experimental results show that the proposed improved TSSM scheme reduces the migration cost and lowers the controller resource consumption by about 36% and 34%, respectively, as compared with the conventional TSSM scheme.

Improved least-squares migration through double-sweeping solver

Based on a recently developed approximate wave-equation solver, we have developed a methodology to reduce the computational cost of seismic migration in the frequency domain. This approach divides the domain of interest into smaller subdomains, and the wavefield is computed using a sequential process to determine the downward- and upward-propagating wavefields — hence called a double-sweeping solver. A sequential process becomes possible using a special approximation of the interface conditions between subdomains. This method is incorporated into the least-squares migration framework as an approximate solver. The associated computational effort is comparable to one-way wave-equation approaches, yet, as illustrated by the numerical examples, the accuracy and convergence behavior are comparable to that of the full-wave equation.

The Hidden Costs of Unaccompanied Child Migration

Abstract Dramatic reductions in refugee resettlement numbers, restrictions on asylum access and growing externalisation of humanitarian protection have significantly curtailed legal migration pathways for distress migrants. This continues to be the case despite well-established international legal obligations requiring states to protect humanitarian migrants and afford children, in particular, a series of protective procedures. We argue that unaccompanied child migrants are disproportionately disadvantaged by these restrictive measures because other avenues for legal migration – for work, for family reunification, for educational and cultural activities – are generally not open to them. As restrictions increase, unaccompanied children adopt self-initiated, challenging strategies to pay the significant costs of the journeys they embark on, a departure from more linear, “traditional” trajectories towards safety. This article presents qualitative data collected in Greece, site of the largest European distress migrant arrivals in recent years, to discuss the growing cost of irregular mobility for unaccompanied children and the role that exploitation-funded earning plays in enabling their migrations today.

A two-phase heuristic algorithm for power-aware offline scheduling in IaaS clouds

The paper aims at mitigating hot-spots during Offline Scheduling in IaaS (Infrastructure-as-a-Service) cloud systems. Unlike previous studies, the research focuses on identifying and resolving hot-spots not at servers, but at server racks. A two-phase algorithm for performing power-aware offline scheduling is proposed. The first phase aims at identifying and mitigating hot-spots at racks, while the second phase performs VM consolidation, i.e. minimization of the number of occupied servers while maintaining a feasible VM mapping and low migration costs. The proposed algorithm takes into account the dynamic nature of VM's resource consumption: it does not only resolve detected hot-spots, but also tries to avoid hot-spots in a reasonable future time period. The algorithm was tested with the data from a real IaaS cloud with different sets of algorithm's parameters. Experimental evaluation showed that the statistical estimates of the future VM's resource consumption provide the most reliable mapping, which is a result of minimization of the number of new hot-spot occurrences.

The Role of Spatial Inequalities on Youth Migration Decisions: Empirical Evidence from Nigeria

We combine nationally representative data from Nigeria with spatiotemporal data from remote sensing and other sources to study how young migrants respond to observable characteristics of potential destinations, both in absolute terms and relative to origin locations. Migrants prefer destinations with better welfare, land availability and intensity of economic activity. We also find that migrants prefer shorter distances and those destinations with better urban amenities and infrastructure. However, responses vary by type of migrant and migration. For example, rural-rural migrants are more responsive to land availability and agricultural potential, while rural-urban and urban-urban migrants are more responsive to welfare and economic vibrancy (measured by nightlight intensity) in destinations. Distance induces varying impact on migration choices of poor and non-poor migrants as well as across more educated and less educated migrants. Longer distances discourage migration for female migrants, poorer migrants, and less educated migrant while the implication for the non-poor and more educated migrants appears to be negligible. This is intuitive because poorer and less educated migrants have liquidity constraints to finance high migration costs. Our results suggest potential scope for predicting how labour mobility responds to alternative regional development policies.

A coupled agent-based model for France for simulating adaptation and migration decisions under future coastal flood risk

In this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent’s bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration.

What price Romania has to pay for being one of the main providers of physicians in Europe?

PurposeThe migration of physicians is a global interest, causing imbalances between developed and developing countries. Romania is one of Europe's major providers of physicians, not because there is a surplus, but because physicians are drawn to places with better living and working conditions. Medicine in Romania is increasingly highly advanced, and Romanian physicians are well appreciated all over the world. Despite being one of the countries with the most medical graduates in the world, Romania is suffering a doctor exodus. After joining the EU, the problem of physician migration became widespread, resulting in a deficient and inefficient healthcare system. Therefore, the purpose of this study is to estimate the losses registered by Romania because of physicians' decision to migrate.Design/methodology/approachThese losses were calculated in two ways: utilizing the statistical life value and the amount of money invested in training a medical graduate.FindingsAccording to the findings, the losses in 2018 were 104.16 million euros, approximately 0.12% of GDP.Originality/valueThe originality of this paper consists in data, being provided by the College of Physicians from Romania and the method used, this study being the only one that estimates the cost of Romanian physicians' migration. The paper adds to existing knowledge an empirical results regarding quantifying the value reflecting the departure of physicians, using value of statistical life and the amount of money invested in preparing a medical graduate.

The role amenities play in spatial sorting of migrants and their impact on welfare: Evidence from China.

From 2005 to 2015, China's high-skilled labor was increasingly concentrated in cities with high wages and high rents, while a narrowing of the wage gap between high- and low-skilled labor showed an opposite trend to an increase in geographic sorting. In this research, I estimated a spatial equilibrium structural model to identify the causes of this phenomenon and its impact on welfare. Changes in local labor demand essentially led to an increase in skill sorting, and changes in urban amenities further contributed to this trend. An agglomeration of high-skilled labor raised local productivity, increased wages for all workers, reduced the real wage gap, and widened the welfare gap between workers with different skills. In contrast to the welfare effects of changes in the wage gap driven by exogenous productivity changes, changes in urban wages, rents, and amenities increased welfare inequality between high- and low-skilled workers, but this is mainly because the utility of low-skilled workers from urban amenities is constrained by migration costs; if migration costs caused by China's household registration policy were eliminated, changes in urban wages, rents, and amenities would reduce welfare inequality between high- and low-skilled workers to a greater extent than a reduction in the real wage gap between these two groups.

Empty Nests: A Study on the Left-Behind Parents of Emigrated Children from Punjab (India)

The ever-occurring phenomenon of migration has great impact on the lives of migrants and their left-behind families. The mechanism of migration has physically separated many families. The present study is an attempt to understand how transnational migration has impacted the lives of left-behind parents living in the village Khizarabad, Punjab, India. Data from 30 parents were collected using in-depth interviews and quasi-participant observation methods. Couple of interviews were counted as one parent and as one interview. Experiences of parents were documented using narratives. The findings of the study revealed that the emotional cost of migration is suffered by the left-behind parents. The remittances received by the parents improved their economic conditions but at the same time decreases physical and emotional support. The paper also draws a conclusion that technology has played a vital role in such situations as it strengthens family ties across borders through phone calls and video calls.

A Vehicle Service Migration Strategy Algorithm in 5G NR-V2X

In recent years, a new technology called mobile edge computing (MEC) is proposed to alleviate mobile devices’ increasing demands on computing resources and service latency. However, users may stay away from the fixed MEC server or even leave the service scope of the MEC server during the mobile process, which will lead to service performance degradation or service interruption. Such an interruption is intolerable for vehicle in V2X. Therefore, it is necessary to consider the issue of service migration. In this paper, we model the service migration problem in V2X as a multidimensional Markov decision process problem and consider a variety of metrics including computing cost, migration cost and energy consumption. A novel user mobility model is also considered to accurately reflect the motion state of users in reality. Then, a novel service migration algorithm is proposed to solve this multidimensional MDP problem. Our proposed algorithm applies Actor-Critic and entropy to ensure both convergence and exportability. Experimental results show that our proposed algorithm outperforms the baselines and has relatively strong robustness.

A UAV migration-based decision-making scheme for on-demand service in 6G network

In the context of the 6th generation (6G) network, UAV-aided networking is emerging as a promising solution to burst traffic offloading and on-demand service coverage. Given the dynamic networking environment, a UAV migration scheme needs to be as flexible, efficient, and adaptive as possible. To address this issue, a UAV migration-based decision-making scheme is put forward to make a tradeoff between migration costs and the satisfaction of service requirements. In the scheme, a UAV adaptive migration strategy (UAMS) is adopted to improve the migration efficiency. Meanwhile, the corresponding signaling interaction process is designed according to different UAV migration scenarios. The experimental results show that the proposed approach makes a significant improvement in the performance of the whole system and takes obvious advantage over traditional solutions.

A Bio-inspired VM Migration using Re-initialization and Decomposition Based-Whale Optimization

Virtual Machine (VM) migration has become quite prevalent in cloud computing. Numerous VM migration-oriented techniques are required for efficient VM allocation but continue to struggle due to incorrect energy usage in the cloud model. Bio-inspired R&D-WOA (Re-initialization and Decomposition-Whale Optimization Algorithm) is proposed by integrating WOA with NSGA-II algorithm to discover Pareto-optimal solutions. The proposed algorithm optimizes the allocation of the task to VM which diminishes the number of VM migrations, thereby lowering the migration cost and energy consumption. The efficiency of the Bio-inspired R&D-WOA is 67%, 7%, 1%, and 1.98% improved than the performance of existing WOA with regard to load, migration cost, energy utilized, and resource abundance respectively. At a similar level of load, it provided 24.80% efficiency in energy, 31.77% reduction in migration cost with maximal resource availability of 0.9917 compared to the ChicWhale algorithm.