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Dedicated infrastructures are commonly used for urgent computations. However, using dedicated resources is not always affordable due to budget constraints. As a result, utilizing shared infrastructures becomes an alternative solution for urgent computations. Since the infrastructures are meant to serve many users, the urgent jobs may arrive when regular jobs are using the necessary resources. In such a case, it is necessary to preempt the regular jobs so that urgent jobs can be executed immediately. Most conventional methods for job scheduling have focused on reducing the response times and waiting times of all jobs. However, these methods can delay urgent jobs and hinder them from being completed within a stipulated deadline. Furthermore, in heterogeneous systems with coprocessors, preemption becomes more difficult because coprocessors rely on several system software functionalities provided by the host processor. In this paper, we propose a parallel job scheduling method to effectively use shared heterogeneous systems for urgent computations. Our method employs an in-memory process swapping mechanism to preempt jobs running on the coprocessor devices. The results of our simulations show that our method can achieve a significant reduction in the response time and slowdown of regular jobs without substantial delays of urgent jobs.

In project management, especially of software development, human resource allocation is critical not only for project success including timely delivery and products quality but also for cost estimation to a software company to decide to contract projects or not. Many approaches have been proposed to provide assistance for human resource allocation in view of minimizing project duration and project cost or maximizing revenue and efficiency. These approaches may sometimes not be comprehensive in real-world environments without addressing some key factors like transferring cost, communication, and negative efficiency, which usually have crucial impacts on assignment applicability. This article is proposing a framework for assisting a software company to evaluate existing resource for making decisions on whether the estimation of the tender is feasible, and assisting to make human resource allocation for team formation in fixed project duration with labor skill and budget constraint. Within consideration of both the cost and the efficiency, the performance is maximized to verges on the customers’ expectations in software development project management on the basis of a real case. Before forming a team for a specific project with well-defined periods and numbers of people with different project roles, the values of key factors in the framework are calculated with individual foundation of computation. We formulate the decision process in integer programming models, and a simulation study is conducted to demonstrate the applicability of the proposed model to assist the decision making process of software companies for team formation. The results of four variants for maximum skill efficiency or minimum hiring cost are illustrated with different considerations of both key factors are included or not proposed in this paper, and demonstrate that these factors could bring out different sources to help companies in making further evaluations for allocating staff in a software development project.

Power distribution systems in the US are commonly supported by wood utility poles. These assets require regular maintenance to enhance the reliability of power delivery to support many dependent functions of the society. Limitations in budget, however, warrant efficient allocation of limited resources based on optimal preventive maintenance plans. A few studies have developed risk-based metrics to support risk-informed decision making in preventive maintenance planning for power distribution systems. However, integration of risk-based metrics and optimization for enhancing the life-cycle resilience of distribution systems has not been explored. To address this gap, this paper proposes a mixed-integer nonlinear programming (MINLP) model to maximize the life-cycle resilience of aging power distribution systems subject to multi-occurrences of hurricane events using an optimal risk-based maintenance planning. For this purpose, a risk-based index called the Expected Outages is proposed and integrated into the optimization problem to minimize the total expected number of power outages in the entire planning horizon. Various uncertainties in the performance of poles under stochastic occurrences of hazards are taken into account through advanced fragility models and an efficient recursive formulation that models the uncertainty of precedent pole failures. The proposed approach is applied to a large, realistic power distribution system for long-term maintenance planning given a total budget limit and different levels of periodic budget constraints. The resulting optimization problems are solved through the branch and bound algorithm. Results indicate that applying the presented methodology leads to a significant enhancement of the life-cycle resilience of distribution systems compared to the commonly implemented strength-based maintenance strategy set by National Electric Safety Code.

With the rapid development of mobile networks and the proliferation of mobile devices, Spatial Crowdsourcing (SC) has attracted the interest of industry and research groups. In addition to considering the specific spatial constraints in the existing research spatial crowdsourcing, each task has an effective duration, operational complexity, number of workers required, and incentive budget constraints. In this scenario, we studied the MQC-TA (Maximum Quality and Minimum Cost Task Assignment) problem. Firstly, the worker incentive model is established. The MQC-GAC algorithm is designed according to the MQC-TA problem to maximize the task completion quality and minimize the incentive budget. The algorithm combined the fast convergence of Genetic Algorithm and the positive feedback mechanism of Ant Colony Optimization Algorithm. Finally, the effectiveness and efficiency of the proposed method are verified by a comprehensive experiment on the data set.

Cloud computing is a promising platform to conduct large-scale workflow applications according to the pay-per-use model. Minimizing the energy consumption of precedence constrained workflows with cost budget constraints has become one of the popular topics in cloud data centers. Most existing scheduling algorithms mainly consider execution time or cost of a given workflow application under a budget constraint; however, these algorithms do not adequately consider energy saving. A reducing energy consumption strategy using a critical task remapping (RMREC) algorithm is proposed in this study. This algorithm is decomposed into two phases: energy consumption reduction and critical task remapping. In the first phase, the adjustable cost budget and spare cost are determined on the basis of cost budget, critical task path, and adjustable budget factor. All workflow tasks are further allocated to virtual machines (VMs) with the lowest energy consumption to achieve preliminary mapping between tasks and VMs while satisfying the adjustable cost budget constraint. In the second phase, critical tasks are remapped to VMs according to spare cost to decrease energy consumption caused by task migration. Experiments on two types of workflow applications with different scales demonstrate that the presented RMREC algorithm effectively reduces energy consumption without violating cost budget constraints compared with existing algorithms.

Along with the development of Information Technology, Online Social Networks (OSN) are constantly developing and have become popular media in the world. Besides communication enhancement benefits, OSN have such limitations on rapid spread of false information as rumors, fake news, and contradictory news. False information spread is collectively referred to as misinformation which has significant on social communities. The more sources and topics of misinformation are, the greater the number of users are affected. Therefore, it is necessary to prevent the spread of misinformation with multiple topics within a given period of time. In this paper, we propose a Multiple Topics Linear Threshold model for misinformation diffusion, and define a misinformation blocking problem based on this model that takes account of multiple topics and budget constraint. The problem is to find a set of nodes that minimizes the impact of misinformation at an allowed cost when blocking them from the network. We prove that the problem is NP-hard and the time complexity of the objective function calculation is $\#P$ -hard. We also prove that the objective function is monotone and submodular. We propose an approximation algorithm with approximation ratio $(1-1/\sqrt {e})$ based on these attributes. For large networks, we propose an extended algorithm by using a tree data structure for quickly updating and calculating the objective function. Experiments conducted on real-world datasets show efficiency and effectiveness of our proposed algorithms in comparison with other state-of-the-art algorithms.

In Real-time Bidding (RTB) based display advertising, demand side platforms (DSPs) estimate the click-through rate (CTR) of each advertisement impression, and then decide whether and how much to bid based on the information of the user and the advertiser. Typically, when a new campaign is launched, the CTR estimation module of the DSP needs to collect data to train an accurate estimator. The advertiser is charged for each ad impression in display advertising, therefore there is some cost for obtaining each training instance. Thus one crucial task is to actively train an accurate CTR estimator within the constraint of the budget. Traditional active learning algorithms fail to deal with such scenario because (i) acquiring training instances is implemented via performing real-time bidding for the corresponding auctions; (ii) RTB requires the bidding agent to make real-time decisions for sequentially coming bid requests; (iii) cost for each ad impression will be unveiled only after giving the bid price and winning the auction; (iv) training data gathered in post-bid stage has a strong bias towards the won impressions. In this paper, we propose a Bid-aware Active Real-time Bidding (BARB) algorithm to actively choose training instances by setting different bid prices for each ad auction, in order to efficiently train an accurate CTR estimation model within the budget constraint. The empirical study on different campaigns of three real-world datasets with three budget constraints shows the effectiveness of our proposed algorithm.

Mobile devices equipped with diverse sensors have emerged as ubiquitous data collection systems within the rising paradigm of Mobile CrowdSensing (MCS). In MCS, auctions are adopted as effective incentive mechanisms in order to secure an acceptable level of contribution from users in participatory MCS. Recent techniques in the literature have addressed several challenges in auctions-based task assignments in centralized MCS. In this research, towards effective task-participant matching, we focus on maximizing the number of completed tasks, the Clearance Rate (CR), which has not been addressed in the literature to date despite the impact it exercises on the satisfaction of service demanders. We propose new bidding procedures for the task allocation strategy. The proposed procedures generalize well to reputation-aware auctioning while handling practical scenarios experienced during campaigns with budget constraints. Particularly, we show that for campaigns that are held consecutively in time, the adoption of an intuitive look-back strategy, for budget transfer from previous campaigns, would remarkably influence the CR. Moreover, observing that tasks with a few bidders should be assigned a higher priority in order to get accomplished, we introduce a new factor for task redundancy. In addition to promoting the accomplishment of unpopular tasks, this factor spares the budget to accomplish more tasks by penalizing redundant task assignment. Extensive performance evaluation of the proposed methods is carried out under various system parameters, namely the number of tasks, auctions, and participants. We demonstrate the effectiveness of the suggested procedures through a significant-and-consistent increase, that ranges from 50% - 500%, in the attained CR compared to the most recent techniques in the literature.

Mobile Edge Computing (MEC) has emerged as a popular paradigm for the support of distributed interactive applications (DIAs), promising continuous service provision and availability to mobile users, that is otherwise not easily attainable under the centralized cloud-only model. Yet, in order to alleviate the app provider from unnecessary capital expenditure, a judicious location of the edge servers must be adopted, that can effectively cope with both the network demands and the deployment cost, and still abide by the budget constraints of the app provider. In this paper, we formally formulate the problem as a budget-constrained facility location problem. We then propose a distributed and scalable algorithm, that employs elastic server relocation, to address it. Trace-driven simulations under various MEC deployment scenarios verify the expected behavior and testify to the algorithm’s accountability in obeying the budget limits and converging to a placement solution close to the optimal in finite time slots, rendering it suitable for DIAs ecosystems.

Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, particularly when there is a spike in user demand at certain locations or an increase in number of vehicles due to special events. If such imbalances are not mitigated, many users will be unable to receive service. Recently there has been increasing research interest in the use of reinforcement learning to craft a dynamic incentive mechanism to encourage shared mobility users to slightly alter their travel behavior in exchange for a small monetary incentive. With the intention that these small changes will aid in rebalancing the vehicle supply in the shared mobility system such that overall service level can be increased. In this paper, we present summarized results of our extensive study of the effectiveness of reinforcement learning-based solutions to the rebalancing problem in terms of the resource and user volume/demand on the improvement in average service level per hour and average percentage improvement in service level per hour. In particular, our study focused on the rebalancing problem in a bikeshare system. Our empirical results show that the reinforcement learning-based incentive mechanism across all budget constraints and various state-action space performs well in terms of average percentage improvement in service level per hour (greater than 8%) when the resource is limited.

Edge co-operations around us are playing an increasingly crucial role in both personal and business activities, because they can bring us higher-quality services. However, more networks take part in an edge co-operation, more privacy protections the co-operation need to be supported to stimulate edge networks' participation. Edge co-operation has great potential in providing a high-performance, low-latency, and high-bandwidth service environment but requires strong incentives to stimulate more networks to participate in. To tackle this challenge, we design a novel privacy-preserving incentive mechanism (PIM) for edge co-operation at a fully distributed edge. The proposed mechanism allows the Internet Service Provider (ISP) to select suitable edge networks for service provision under a budget constraint while guaranteeing differential privacy, approximate truthfulness, computational efficiency, individual rationality. Through several simulations, we evaluate the performance and validate the properties of our mechanism.

Without a precise specification, an IT project might not remain on time and on budget constraints, or it might lead to a different outcome than desired. A number of established standards define how requirements must be written to avoid such issues. This paper describes our ongoing work to derive a comprehensive set of standardized criteria that IT-requirements must meet in accordance with IEEE 1233-1996 and ISO/IEC/IEEE 29148-2011. We also use a text-mining approach to identify IT-requirements that violate these standards. Our preliminary results are promising: In our biased dataset, we can use text features that are easy to compute, to filter out requirements that do not comply with the standards. Our beneficiaries are auditors, developers, Scrum teams, customers and other stakeholders whose projects are highly dependent on extensive IT-requirements specification.

As a new computing paradigm for hosting and executing large-scale scientific applications, cloud computing can provide users with scalable and elastic computational resources. Workflow scheduling is NP-complete and becomes even more challenging under the dynamic environment of the cloud since certain quality of service (QoS) needs to be met in most cases. In this paper, a meta-heuristic scheduling algorithm, named EMPSO (Elimination Mechanism-based PSO), is proposed to minimize the makespan while satisfying the budget constraint. Firstly, a new technique for choosing the global best solution is developed to avoid the premature convergence caused by the original totally greedy selection method, which improves the global search performance of our algorithm. Then, for accelerating the convergence speed towards the global optima, time-varying parameters are introduced to keep a good balance between global search and convergence by updating particles’ velocity adaptively during different optimizing stages. Finally, an elimination mechanism is embedded to further improve the overall performance by promoting the quality of inferior particles. Extensive experiments are conducted on well-known scientific workflows of different sizes and types. The experimental results demonstrate that EMPSO outperforms GA and PSO with respect to meeting the budget constraint and minimizing the makespan.

Edge Learning is an emerging distributed machine learning in mobile edge network. Limited works have designed mechanisms to incentivize edge nodes to participate in edge learning. However, their mechanisms only consider myopia optimization on resource consumption, which results in the lack of learning algorithm performance guarantee and longterm sustainability. In this paper, we propose Chiron, an incentive-driven long-term mechanism for edge learning based on hierarchical deep reinforcement learning. First, our optimization goal combines learning-algorithms metric (i.e., model accuracy) with system metric (i.e., learning time, and resource consumption), which can improve edge learning quality under a fixed training budget. Second, we present a two-layer H-DRL design with exterior and inner agents to achieve both long-term and short-term optimization for edge learning, respectively. Finally, experiments on three different real-world datasets are conducted to demonstrate the superiority of our proposed approach. In particular, compared with the state-of-the-art methods under the same budget constraint, the final global model accuracy and time efficiency can be increased by 6.5 % and 39 %, respectively. Our implementation is available at https://github.com/Joey61Liuyi/Chiron.

Large cities worldwide are facing many challenges, such as a growing population, which leads to an increase in traffic congestion. In many places, city road infrastructure does not cope well with growth in the number of vehicles leading to long waiting times on roads and decreasing fuel economy. Due to inflexibility to update city roads comfortably in most places, the adaptation of smart traffic intersections provides a promising approach to curb growing traffic and reduce wait times on road intersections. Still, faltering public budgets do not allow costly transport infrastructure expenditures, and deploying and maintaining sensors to enable smart intersections throughout a city is resource-intensive. In this paper, we explore the idea of efficiently deploying smart intersections within given budget constraints in a city. We propose a generic simulation-based framework that models city traffic based on historical patterns and utilizes agent-based modeling to select an optimal subset of road intersections to improve traffic conditions for a given city network. We validate our framework with state of the art urban mobility framework and demonstrate its utility with a simple road network. We further employ our framework to study the impact of variation in traffic characteristics and budget constraints on the efficiency of deploying smart lights.

This paper aims at scheduling bag-of-tasks (BoT) applications under budget constraints on hybrid clouds for minimizing the makespan. To solve this NP-hard problem, we propose a novel firefly algorithm (NFA) in which the evaluation of a firefly consists of two steps: (1) mapping a firefly to a scheduling solution (a task sequence); (2) calculating the solution's objective (its corresponding makespan). In the first step, different from the well-known ranked-order value (ROV) rule, we propose a distance-based mapping operator that relies on the distance between a firefly and the brightest one to determine the mapping relationship between a firefly and a solution. We use a probability model in which solutions corresponding to fireflies closer to the brightest one would have higher probabilities to inherit tasks from the current best solution. In this manner, these solutions can inherit more “good genes” hidden inside the current best solution to evolve into more high-quality solutions. In the second step, we employ an effective heuristic to evaluate solution objectives. We further develop a composite heuristic to generate the initial best solution, providing the proposed NFA with a good start. We also establish a new movement scheme such that fireflies distant from the brightest one can explore a wide range in the search space, whereas fireflies nearby the brightest one can search in a small neighborhood. Experimental results show that, by employing the above-mentioned strategies, NFA outperforms the standard firefly algorithm and the existing best algorithm, in terms of scheduling effectiveness and computational efficiency. Specifically, the distance-based mapping operator is verified to be both more effective and more efficient than the ROV rule. The composite heuristic is capable of generating a good initial solution, leading to the high quality of the final schedule. The movement scheme can further reduce the makespan of BoT applications.

Time plays a critical role in competitive influence maximization. Companies aim to promote their products before certain events, such as Christmas Eve or music concerts, to gain more benefit under competitions from other companies. Besides, these companies have a limited budget to spend on these product promotions. Therefore, in this paper, we examine a time-constrained competitive influence maximization where the parties wish to maximize their profits before the respective deadlines. Besides, the parties need to determine how to select the seed nodes and when to initiate information propagation in the network, such that the decision results in the optimal reward given the time and the budget constraint. To this end, we propose a novel reinforcement learning-based framework named seed-combination and seed-selection that is built on a nested Q-learning (NSQ) algorithm. This way, we can derive the optimal in both budget allocation and node selection that results in the maximum profit. In evaluating the proposed model, we consider the scenarios when the competitors’ strategy is known, unknown, and not available for training. The results show that the proposed NSQ algorithm could improve the rewards by up to 50% compared with the state-of-the-art algorithm, STORM-Q.

Labeling samples manually is a laborious, error-prone, and cost-consuming process, which is a traditional approach to preparing training data for supervised learning. Crowdsourcing provides an effective way to acquire labeled training data. In this paper, we propose an adaptive crowd labeling method to construct a set of the pairs of opinion target and opinion term iteratively under certain budget constraint. First, we assess the workers’ reliabilities with a small number of labeled samples based on an EM process, since the worker’s expertise varies widely on an open crowdsourcing platform. And then, the tasks are assigned to the high reliable workers for labeling the pairs of opinion target and opinion term. Finally, the responds of a sample from multiple workers are integrated to generate a final result based on the labelers’ reliabilities as well as the dependency relation between opinion target and opinion term in this sample. A series of the experimental results show that the proposed method can achieve better extracting effectiveness compared with the baselines and the state-of-the-art methods.

This paper addresses the problem of time-varying data distribution in a fully decentralized federated learning setting with budget constraints. Most existing work cover only fixed data distribution in the centralized setting, which is not applicable when the data becomes time-varying, such as in realtime traffic monitoring. More so, a lot of existing work do not address budget constraint problem common in practical federated learning settings. To address these problems, we propose an online Lagrangian descent algorithm. To provide privacy to the local model updates of the clients, local differential privacy is introduced. We show that our algorithm incurs the best regret bound when compared to other similar algorithms, while satisfying the budget constraints in the long term.

The wide deployment of machine learning (ML) models and service APIs exposes the sensitive training data to untrusted and unknown parties, such as end-users and corporations. It is important to preserve data privacy in the released ML models. An essential issue with today’s privacy-preserving ML platforms is a lack of concern on the tradeoff between data privacy and model utility: a private datablock can only be accessed a finite number of times as each access is privacy-leaking. However, it has never been interrogated whether such privacy leaked in the training brings good utility. We propose a differentially-private access control mechanism on the ML platform to assign datablocks to queries. Each datablock arrives at the platform with a privacy budget, which would be consumed at each query access. We aim to make the most use of the data under the privacy budget constraints. In practice, both datablocks and queries arrive continuously so that each access decision has to be made without knowledge about the future. Hence we propose online algorithms with a worst-case performance guarantee. Experiments on a variety of settings show our privacy budgeting scheme yields high utility on ML platforms.

Acceleration in energy consumption leads to a mismatch between supply and demand. This problem can be resolved by integrating Demand Side Management (DSM) programs with Energy Management Systems. DSM programs include planning, scheduling, implementing and monitoring of the demand load in a way that ensures benefits for the utility company as well as for the consumers. Tackling the Residential Load Management Problem (RLMP) is quite challenging especially while considering variants such as buildings' loads, demand uncertainty, consumers' preferences, tariff and time horizon. The Inclining Block Rate (IBR) tariff system is still used especially in developing countries. However, solving the RLMP under the IBR tariff has not received much attention in the literature. This paper addresses the RLMP under the IBR tariff while maximizing the fulfillment of the appliances' schedule with respect to budget limit. In this paper, a mixed integer programming model is proposed for scheduling of home appliances over one month under IBR as a standalone pricing tariff. The performance of the proposed model is tested using a dataset from a typical household in Egypt. Results show that the obtained schedule fulfills the consumer's usage target within the given budget limit. Various budget scenarios are investigated, and the obtained results show to what extend the budget can control the on/off status of each appliance in each time slot over one month

This paper envisages a novel proposal for micro-PMU placement in distribution network in a budget constraint environment to gear the single branch outage condition. The objectives of this work are to deploy minimum number of micro-PMUs in distribution network in order to get complete system observability considering channel cost of micro-PMUs for the sake of easing down the economic burden, to evaluate the total cost and to propose an algorithm serving situational coverage of single branch outage condition. In a reserved budget circumstance aiming at subduing the number of extra micro-PMUs under single branch outage condition, reliability of system components under normal and adverse weather conditions are introduced. Probability of unavailability of each branch is computed by Markov chain two state transition model. The probability assessment justifies the number of branches should be taken care off to meet the bound budget environment. An example with evaluation of cost has been provided for IEEE 34 test bus system.

Based on the popularity of intelligent embedded devices, mobile crowdsensing emerged as a new paradigm of crowdsourcing perception. However, it often faces the problem of low data quality caused by the insufficient number of participators. In this paper, we propose an incentive mechanism for mobile crowdsensing considering social networks. Our mechanism takes into account the social networks of mobile users, which is not only to reward users for data contribution, but also for solicitation behavior, so as to expand the number of participators under budget constraints, and to select users with high-capability. We prove by the theory that the proposed mechanism satisfies individual rationality, truthfulness, solicitation incentives, and budget effectiveness. Besides, the simulation results show that the mechanism can effectively motivate users' long-term participation and high-quality data collection.

At present, in Chinese emergency shelter planning, there is often a problem of ignoring the diversification of the initial needs of the victims and the time-varying escalation of the later needs, which is prone to the inefficient matching of the supply and demand of shelter resources. In this paper, three basic actions of disaster relief are analyzed according to the different needs of disaster victims, that is, stratifying evacuation of people after the disaster, planning and layout of different types of shelters, and distribution of daily medical supplies. Considering a complete coverage of demand, capacity and budget constraints, this paper constructed the joint optimization model to optimize victim evacuations, the location of the shelter and material distribution. The model can ensure the orderly and efficient operation of emergency rescue in the case of limited time and space, and realize the high-quality relocation of victims.

Facility location problems can be considered as a century-old science. Studies on this subject began in the 1950s and are still relevant today. In 2016, a new variant of the location problems, called “budget constraint multi-capacitated location problem” (BMCLP), is published to generalize the p-median one. In this work, we propose a generalization of the BMCLP into a bilevel location problem (plants-depots and depots-customers) in order to consider, the modern supply chain constraints. We therefore established a mathematical formulation for the new problem, named “bi-level multi-capacitated facility location problem” and we created data instances adapted to this new formulation based on semi-random factors and certain generation methods available in the literature. In order to validate the latter formulation, we proposed Branch & Cut solving method used by CPLEX solver. Finally, we present the obtained computational results, which prove the efficiency of the new formulation for the tested instances.

Coupon is a powerful tool for promotional marketing and customer targeting. Improving customer conversion effects by optimally allocating coupons to the right customers, however, is a nontrivial task. In particular, offering coupons to all customers may not be cost effective while offering coupons to randomly selected customers may not lead to optimal effects. In this paper, we address challenges in precision coupon targeting with a customer triage framework which would ration coupons precisely to customers who will be the most promising commercial conversions. Specifically, first we use random user-experiments to quantify the coupon effects of our interest. Then, we model the observation from user-experiments to predict the expected coupon effects on all customers. With the predicted coupon effects, we develop a joint decision strategy for customer triage to target customers with the most influential coupons under budget constraints. The joint decision strategy can dynamically balance exploitation and exploration in the decision process spanning multiple decision periods. Implemented on a real-world online takeout service system, our results show significant improvements in comparison with alternative approaches.

Driven by the advancement of science and technology, the issue of marine ship Internet of Things (IoT) users' assignment of computing tasks offloading has become more and more challenging. When faced with the complex and dynamic marine environment, considering the different quality requirements of maritime applications, we have addressed this issue in this paper. We first propose the space-air-ground-edge (SAGE) maritime communication network architecture. This novel architecture is used to offload computing-intensive applications and services for IoT users in the marine environment. At the same time, based on the Multi-Armed Bandits (MAB) problem, considering budget constraints and other marine environmental conditions such as delay and energy consumption, we propose an algorithm for selecting an edge server strategy. Simulation results of this paper show that the scheme has a better performance under different quality of service (QoS) requirements, which proves that the scheme is effective.

In a project, a mission completion time for each mission should be presented as stochastic due to uncertainly available machines/human resources. Besides, a charge for each mission is affected according to the completion time of the mission. In order to describe the uncertain completion time and charges, a stochastic completion time project (SCTP) is formulated in this paper. Then, SCTP reliability is defined as the probability that total completion time and budget constraints can be satisfied when completing a project. According to the previous reference, maximal and minimal capacity vectors bind all the feasible capacity vectors. Specifically, a previous reference calculated an estimated SCTP reliability in terms of an interval. On the other hand, we develop a breakdown approach such that all the feasible capacity vectors can be decomposed. Then, a recursive function is constructed based on the sum of disjoint products (SDP) principle. An algorithm is further presented to calculate the exact SCTP reliability. Project supervisors and teams can analyze and manage their projects in terms of SCTP reliability.

We consider the optimal interdiction problem in transportation networks as a game in which an attacker acts as the player who goes first and, subject to budget constraints, fails nodes (partially or fully) at time zero so as to maximize the total travel time of the mass. A centralized network operator then acts as the player who goes second and, subject to the system's dynamics, routes the mass so as to minimize its total travel time. We prove that the attacker's best action is to find the most consequential nodes and employ his resources to fail them fully, so that the optimal attack is both sparse and binary. We then propose an algorithm to numerically solve the optimal interdiction problem, and demonstrate the utility of our approach through illustrative examples.

The fourth industrial revolution (4IR) is expected to change our lives. One of the main players towards 4IR will be Internet of Things (IoT). The main limiting factor for minimum IoT application implementation in Sub-Saharan Africa rural areas is that many areas do not have communication infrastructures mainly due to budget constraints. Recently Long Range (LoRA) has been gaining research for such applications. In this paper we present the concept that multi-hop networks based on IEEE802.11 and Schedule before Contention Scheduling strategies can also provide backhaul communication infrastructure. The use of the RWS-AGE Strategy tested in multi-hop networks in this paper has shown a reduction in the number of collisions, packet loss and end-to-end latency compared to Enhanced Distributed Channel Access (EDCA).

Monitoring environmental variables in lower layers of the atmosphere is an important activity to measure changes that result from natural events, and human interventions. Volcano eruptions, commercial aviation, and the massive spread of pesticides using light aircraft are just some examples of low layer atmosphere polluters. Twice a day, every day of the year, weather balloons are released simultaneously from almost 900 locations worldwide to monitor environmental variables. The flight of these synthetic rubber balloons last for around 2 hours, then they become pollution too. Recent advances in small unmanned aerial vehicles (UAVs) with built in sensors, and their emerging role in business supply chain make UAVs ideal participants for environmental monitoring. In this paper, we present an incentive mechanism for UAV-Crowdsensing. The core of the proposed mechanism consists of a recurrent reverse action and a recruitment model. By these two components, the system encourages UAVs sensing from locations that maximize volume coverage within a given budget. Through extensive simulations, we evaluate the performance of the proposed incentive mechanism.

An SI 1 SI 2 S model represents the opinion dynamics for two opposing opinions over a social network. Two controllers with budget constraints, each representing one of the two opinions, wish to maximize a function of the number of nodes adopting their opinion at the end of a campaign that runs for a fixed duration. We determine the Nash control strategies for both the controllers and first prove a key structural result on these strategies that gives us insights on how campaigns should distribute their effort and ad spend. Next, via extensive simulation studies, we show that not being strategic may lead to a significant loss of effectiveness. We also observe that the relative values of the budgets of the two controllers are more significant than the absolute values of the budgets. Further, we study the allocation of budget across the duration of the campaign by the Nash strategies.

Today, attacks on sensitive data held by organizations and the resulting data breaches are unfortunately all too common. In response to these attacks the organization applies security controls (e.g., encryption) to secure its vulnerabilities. However, these controls are often applied haphazardly, without any idea of their reliability, or any guidance on how they should be applied to account for the priority of the vulnerabilities or a security control’s effect on the overall security posture of the organization. This work derives a mathematical model linking the reliability of the security controls to the overall security level of the organization. The paper then combines this model with a method to prioritize vulnerabilities, allowing the organization to more intelligently apply security controls and reach its desired security level goal within negotiated budgetary constraints. The paper illustrates this approach using an application example.

Industries have been facing ever-increasing challenges to do more with less under ongoing budget constraints. They are pushing the boundary by challenging the OEM recommended maintenance intervals and relaxing or tightening based on where it is needed. This is also evident in water sector where industries are trying to do targeted maintenance based on balancing costs, performances and risks. The unexpected failures, the down time associated with such failures, the environmental overflows and, the increasing maintenance costs are major challenges all wastewater reticulation and distribution networks. Industries have been working hard to increase the availability of equipment and reduce the life-cycle cost without compromising safety and environmental targets. Risk-based maintenance (RBM) strategy is useful for allocation of maintenance resources where first allocation occurs to the highest risk item and progressively allocated till it reached budget limits. This paper is based on findings from a study covering 186 sewerage pumping stations of Townsville Water in North of Queensland in Australia. This study covered identifying the critical subsystems and mitigating the risks of failure of those subsystems. Implementation of risk based maintenance strategy was useful in further enhancing reliability and reduction of maintenance costs.

Vehicle-based crowdsourcing is becoming a powerful paradigm that can outsource intensive tasks to vehicles by exploiting their on-board resources. In this paper, we focus on the problem of motivating vehicles to join the crowdsourcing system. Considering the various delay demands of tasks in smart cities, we design a delay-aware incentive mechanism to employ vehicles based on reverse auction. Specifically, by taking task delay into consideration, we model the utility of service requester as a function closely related to when its released tasks would be completed. In our mechanism, the participating vehicles bid for their preferred tasks by submitting not only the bidding prices, but also the estimated time of completion (ETC). To maximize the utility of the service requester under a budget constraint, the proposed delay-aware mechanism is cast as a nonmonotone submodular maximization problem with a knapsack constraint. Due to the NP-hardness of the formulated problem, we develop an approximate algorithm for bid selection and payment determination, which guarantees truthfulness, budget feasibility, individual rationality, profitability, and computational efficiency. Simulation results demonstrate the effectiveness of our proposed incentive mechanism.

The social and economic impact of natural catastrophes on communities is a concern for many governments and corporations across the globe. A class of financial instruments, parametric hedges, is emerging in the (re)insurance market as a promising approach to close the protection gap related to natural hazards. This paper focuses on the design of such parametric hedges, which have the objective of maximizing the risk transferred subject to a budget constraint. With Greece as a case study, one of the most seismic prone European regions, with limited seismic insurance penetration, this paper proposes a biased-randomized algorithm to solve the optimization problem. The algorithm hybridizes Monte Carlo simulation with a heuristic to generate a variety of solutions. A simulation stage allows for analyzing the payout distribution of each solution. Results show the impact of the problem resolution on the transferred risk and on the distribution of triggered payments.

In this paper, we consider sensor selection for (binary) hypothesis testing. Given a pair of hypotheses and a set of candidate sensors to measure (detect) the signals generated under the hypotheses, we aim to select a subset of the sensors (under a budget constraint) that yields the optimal signal detection performance. In particular, we consider the Neyman-Pearson detector based on measurements of the chosen sensors. The goal is to minimize (resp., maximize) the miss probability (resp., detection probability) of the Neyman-Pearson detector, while satisfying the budget constraint. We first show that the sensor selection for the Neyman-Pearson detector problem is NP-hard. We then characterize the performance of greedy algorithms for solving the sensor selection problem when we consider a surrogate to the miss probability as an optimization metric, which is based on the Kullback-Leibler distance. By leveraging the notion of submodularity ratio, we provide a bound on the performance of greedy algorithms.

We consider a security setting involving a defender who is required to invest (subject to a budget constraint) in protecting a given set of nodes against attacks. Each node has a certain value to the defender, along with a probability of being successfully compromised, which is a function of the investment in that node by the defender. In this setting, we consider the impacts of behavioral probability weighting (vis-a-vis the probability of successful attack) on the investment strategies; such probability weighting, where humans overweight low probabilities and underweight high probabilities, has been identified by behavioral economists to be a common feature of human decision-making. We show that under appropriate conditions on the probability of successful attack, the defender’s optimization problem is convex (even under probability weighting). Furthermore, we show that behavioral probability weighting causes the defender to shift more of her investments to the higher-valued nodes and underinvest in the low-value nodes, compared to the case where the defender perceives the probability of attack correctly. In particular, the number of nodes that have positive investment decreases as the defender becomes more behavioral.

In this paper, we consider the top-k route search with user's preferences. Specifically, given a set of POIs, our problem is to find k different routes from a source POI to a target POI such that the constraint on route cost and the POIs covered by the route can optimally satisfy the user-defined weighted feature preference. It has been shown that the problem is NP-hard. The challenge is how to select from plenty of POIs and construct an optimal route especially when the size of candidate POIs is large. In order to support top-k route search on a large dataset or with a looser budget constraint, we propose a parallel method on a single machine to speed up the search. Moreover, we adopt further effective pruning strategies to reduce the search space. The experimental results on real-world datasets show that our proposed parallel method is much efficient, about 10 times faster than the existing serial algorithm at best.

Mobile crowdsensing, through which a requester can recruit a group of crowd workers via a platform and coordinate them to perform some sensing tasks, has attracted lots of attention recently. However, most of the existing mobile crowdsensing systems assume that the qualities of workers are known in advance. Based on this assumption, they study the task assignment and worker recruitment problems. Unfortunately, the qualities of workers are generally unknown in reality, so the platform must find the tradeoff between exploring and exploiting the qualities by using reinforcement learning. At the same time, all sensing tasks are required to be covered in each round (covering constraint), and the requester usually has a limited budget (budget constraint). In this paper, we study how to recruit unknown workers under the budget and covering constraints so that the total expected achieved qualities can be maximized. To this end, we model the problem as a combination of a maximum weight matching problem and a special multi-armed bandit problem. We first consider that the recruitment costs of workers are homogeneous and propose a recruitment algorithm with a performance guarantee. Then, we study the heterogenous case and devise a heuristic algorithm. Finally, we demonstrate the performances of our algorithms through extensive simulations.

Scheduling the Scientific Workflows (SWf) task onto ideal cost-effective resources in a distributed system remains a significant problem that demand for auto-scaling in a cloud computing environment. Even though cloud computing has considered as a quintessential platform for scientific users to perceive deadline constrained large-scale computations. However, complexity increases with dynamic increase in network size which makes mapping of resources a NP-hard problem. To overcome this situation, a novel hybrid auto-scaling strategy is proposed. The hybrid auto-scaling comprises of on-demand and spot instances pricing model for SWf computation under deadline and budget constraint. Many auto-scaling strategy have been proposed in the earlier works, but nevertheless, there is an ample scope for new auto-scaling strategy for efficient scheduling SWf in cloud environment. Moreover, hybrid auto-scaling approach for SWf considering spot and on-demand instances pose a challenge, since the approach has to estimate the proper amount and type of virtual machine instances to acquire and dynamically allocate the number of instances under spot or on-demand pricing model depending on the SWf need. A flexible hybrid auto scaling policy is proposed for scheduling SWf efficiently. Experimental results reveal the promising potential on the proposed algorithm with regard to minimization in makespan and cost of SWf under deadline and budget constraint in cloud environment.