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Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention. Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources. Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions. Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources. Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint. Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms. In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints. At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms. Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).

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.

It has become a strategic consensus of the international community for accelerating the deployment of 5G network. This paper presents an approach for the deployment of 5G base stations under the considerations of both the cost and the signal coverage. We formulate an optimization problem for the site selection and location of 5G macro and micro base stations. An implementation procedure is proposed in the paper for the cooperative operation and deployment scheme of optimizing the location of 5G heterogeneous base stations, which aims to optimally reduce the setup cost and strengthen the signal coverage while deploying 5G base stations. A series of numerical examples are solved in the paper to demonstrate the proposed approach, and a cost-benefit analysis is also conducted to determine the optimal deployment plan for the number of macro and micro base stations. In the conclusion, a balanced executable solution is presented to make the signal strength of all demand points in the studied 5G network reach the strongest under the budget constraint.

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.

With the development of the room rental market, many room rental websites have been created, e.g., SpareRoom and EasyRoommate. On these websites, people find not only rooms for rent but also suitable roommates. Inspired by the rental mode in practice, a benchmark room allocation model was introduced by Chan et al., in which 2n agents must be allocated to n rooms that have the same capacity and each agent can be allocated to any room. However, in practice, rooms may differ in terms of capacity, e.g., college dorms or apartments may contain both two-bed rooms and four-bed rooms. Moreover, an agent can only be allocated to a room of which the rent does not exceed the agent's budget. In this scenario, we must consider not only the agents' preferences but also the capacity diversity of the rooms and the budget constraints while allocating the rooms. Therefore, this paper investigates the room allocation problem with capacity diversity and budget constraints. We mainly focus on finding an allocation that maximizes social welfare. First, this paper demonstrates that finding an allocation that maximizes the social welfare is NP-hard (i.e., non-deterministic polynomial-time hard), even if only one room's capacity is larger than 1 and the other rooms' capacities are all 1. Second, this paper presents a (c* + 2)/2 + ε-factor approximation algorithm (with ε > 0) for the case in which the capacity of each room does not exceed a constant c*. Third, this paper proposes a heuristic algorithm based on the local search for the general case in which the capacity of each room is not bounded by a constant. The experimental results demonstrate that the proposed algorithm can produce near-optimal solutions. Finally, this paper investigates how to find a roommate stable or room envyfree allocation with a social welfare guarantee. Published version

The selfishness and randomness of users in the mobile crowd sensing network could cause them unwilling to participate in sensing activities and lead to lower completion rates of sensing tasks. In order to deal with these problems, this paper proposes a novel incentive mechanism based on a new auction model for mobile crowd sensing, which consists of two consecutive stages. In the first stage, a novel Incentive Method based on Reverse Auction for Location-aware sensing (IMRAL) is proposed to maximize user utility. By introducing a task-centric method to determine the winning bids, it can provide higher user utility and higher task coverage ratio. To ensure the truthfulness of IMRAL, we design a unique payment determination algorithm based on critical payment for the incentive platform. In the second stage, we propose a user-interaction incentive model (UIBIM) to cover the situation that a user may drop out of the sensing activity. This new incentive model includes a dynamic double auction framework prompting users' interaction and a user matching algorithm based on a bipartite graph. The proposed new mechanism achieves the goal of improving task completion rates without increasing the cost of the incentive platform. The simulation results show that comparing with other solutions, such as a truthful auction for location-aware collaborative sensing in mobile crowdsourcing and incentive mechanism for crowdsourcing in the single-requester single-bid-model, IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio, and the UIBIM can significantly improve task completion rates.

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.

Mobile Crowdsensing (MCS) is a new mode of sensing for the Internet of Things, which has become a research hotspot. In an MCS market, there are usually three parties, i.e. requesters, workers and the platform. Each party of the crowdsensing market wants to obtain more benefits, so different mechanisms of task assignment need to be provided respectively to meet the different needs of the three parties. Great efforts have been invested on task assignment mechanisms from the perspective of the platform or requesters, i.e. a user recruitment algorithm of profits-maximizing for the platform under budget constraint, an efficient and truthful pricing mechanism for team formation and so on. However, to the best of our knowledge, there is rare mechanism for the task scheduling or planning from the perspective of workers, without considering how to maximize the benefits of workers in the case of multitasking. In this paper, a theoretical analysis on the calculation model of workers' benefits is conducted to investigate the influence factors of workers' income and its relation. Consequently, a heuristic multi-task scheduling algorithm based on Ant Colony Optimization algorithm (ACO) is proposed to determine a task scheduling strategy to maximize the workers' benefits. Finally, extensive experiments are carried out by using the STSP dataset available online, and it is shown that the proposed algorithm significantly reduces the cost of completing multiple tasks, and substantially improves the workers' benefits.

Owing to the accuracy and flexibility, mobile advertising has become a very attractive marketing method based on smart mobile terminals. The more common mobile advertisement distribution methods are based on location and content. But most of them donart take into account the limited duration time of mobile users and the timeliness of advertising, which makes mobile advertisement distribution suffer from low propagation efficiency. To make up for this deficiency, we propose the advertisement platformars expected income maximization problem (EIMP), and prove its NP-hardness. As far as we know, EIMP is a combination of the 0-1 multi-dimensional and multiple knapsack problem, which is even harder. In dealing with this difficulty, we fortunately find that this problem could be transformed into a maximizing monotone submodular set function, which is subject to partition matroid constraints. Then we propose a simple but effective greedy algorithm TIMAO (Time-sensitive Mobile Advertisement Offloading) which has a constant approximation ratio of 1/3 to provide a performance guarantee for this issue. Finally, extensive evaluations are conducted. The results show that compared with the random selection method, TIMAO increases the expected income of the platform by about two times. And it achieves 99.2% of the near optimal results obtained by the CPLEX tool-box. Moreover, we propose an important metric, the duty cycle, to evaluate system performance in time domain. The results show that our scheme could increase the time duty cycle by about average 10% compared with the random selection.

With the pervasiveness of mobile devices, satisfying spatial-temporal coverage requirements in interested regions while considering the quality of the sensing data and the budget constraint is a major research challenge in mobile crowdsensing (MCS). In this paper, we define a novel coverage metric, quality coverage, which considers both the fraction of subareas covered by sensor readings and the quality of sensing data in each covered subarea, for diversified location-based MCS tasks. In our system, as participants with high reliability level can contribute a lot to sensing tasks, we design an incentive model that provides higher bonus to more reliable participants. After knowing the reward for the per amount of sensing data, each worker can submit an interested task set that contains several unit tasks. The platform assigns MCS tasks to these mobile workers by solving the task allocation optimization problems, which select the optimal mobile worker set under the budget of data requesters to maximize diverse and spatial coverage level in the whole sensing area. As the task allocation problems are proven to be NP-hard, we propose a heuristic and a polynomial-time greedy approximation algorithm to solve optimization problems in two different application scenarios. Simulations using both real and synthetic data sets show that our algorithms outperform existing approaches.

This paper proposes three hierarchical levels of a competitive big-data market model. We consider that a service provider gathers data from multiple data sources and provides valuable information from refined data as a service to its customers. Under our approach, a service provider determines optimal data procurement from multiple data sources within its budget constraint. The multiple data sources follow the service provider's action by independently submitting bidding prices to the service provider. Further, customers decide whether to subscribe or not based on the subscription fee, their willingness-to-pay, and the quality of the refined data. We study the economic benefits of such a market model by analyzing the hierarchical decision making procedures as a Stackelberg game. We show the existence and the uniqueness of the Nash equilibrium (NE), and the NE solution is given as a closed form. Finally, we reveal that the obtained unique equilibrium solution maximizes the payoff of all market participants.

We investigate how to leverage limited local information for mobile advertisement popularization, where users are motivated to propagate advertisements with rewards or credits distributed from a centralized platform. Previous solutions on social networks failed to be applicable for mobile advertisement propagation because of the mobility, which would lead to extremely high overhead and low propagation efficiency. Participants need to be selected carefully and efficiently in dealing with the highly dynamic network and uncertain contacts among users. Even worse, the propagation effects are difficult to quantify with the increased number of mobile users. In tackling these difficulties, we propose $\alpha $ MAP ( $\alpha $ here means efficient mobile advertisement popularization with local information), a lightweight but effective propagation user selection scheme with local information. Two key technologies inspire us to achieve efficient and effective mobile advertisement propagation. First, we advocate propagation effects instead of influence for user selection, where mobile users with strong information dissemination ability could be selected. Second, we use local information instead of the global information to achieve near optimal performance for propagation. In our proposed scheme, the information potentials proposed by Loukas et al. are leveraged to find the influential users with local information. With extensive experimental study, we find that $\alpha $ MAP could effectively improve the mobile advertisements delivery ratio. Using the propagation instead of popularization is validated with different aspects of investigations when the budget is constrained. To evaluate the impact of mobility, we leverage the mobile trace data set for comprehensive evaluations. $\alpha $ MAP performs fairly well when more realistic experimental settings are incorporated.