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The trend of drone applications in recent years has begun to change. The application of Drone or UAV (Unmanned Vehicle) brings flexibility in those areas that are difficult to reach or manage. The use of drones enables the structure of a semi-autonomous system, where workers in the performance of many tasks can be replaced by drones, affecting time savings and flexibility at work. This research proposes an efficient algorithm that can be applied to drones to transport materials into the indoor environment. This algorithm optimizes the time and reduces power consumption when sharing and completing tasks between different drones. In this research, the results will be achieved based on the "Earliest Time Algorithm". We have modified this algorithm, where we have managed to get much better results in terms of saving time while performing various tasks from the drone. It achieves by changing the logic of the impact of weight on the lifetime of the drone battery from linear to dynamic. Also, we have implemented the possibility of a partial charge of the drone battery. The experiments are performed in a test environment for various tasks performed by the drone. In this case, the impact of weight on algorithm performance as well as the impact of partial charging on time optimization in performing various tasks from the drone will be tested. The performance of the algorithm is tested and analyzed for three different types of drone tasks depending on the weight the drone carries. The results achieved with our algorithm are compared with the results of the existing algorithm. [ABSTRACT FROM AUTHOR]

Real-time task scheduling is a critical part of systems since processes must finish their work at a certain time. Large data streams require high energy efficiency and rapid response times. Real-time system performance must be improved through task scheduling on resources. Numerous researchers have recently and rapidly proposed enormous efforts with enhancements in numerous task scheduling methods. Round Robin algorithm has been commonly utilized in task scheduling. In this paper, a Modified Median Mean Round Robin (MMMRR) algorithm is proposed to significantly enhance the performance of the Round Robin algorithm. The proposed algorithm finds an ideal dynamic time quantum └(median+ mean)/2)┘ and generated for each round depending on the remaining burst time of the processes. The system performance has been enhanced in terms of waiting time, turnaround time and context switching. The experimental results show that the proposed algorithm outperforms ADRR, HYRR, EDRR, MARR and MMRR algorithms. [ABSTRACT FROM AUTHOR]

SUMMARY: This research article formulates contemporary approach named multi‐objective reliability‐based workflow scheduler. Numerous strategies have been proposed in the past to prioritize and map the tasks to cloud resources. Though the recent studies lead to efficient solutions however they are restrained in terms of performance due to lack of resource consideration based on utilization rate and reliability index. It is crucial to consider reliability parameter while mapping tasks onto the virtual machines and not just the reliability value, but the cost incurred must also be minimized. To this end, the proposed strategy has been categorized into four modules, (i) scrutiny of reliable VMs, (ii) task ranking, (iii) optimizing the task re‐ordering using flower pollination optimization, and (iv) task mapping onto the VM. It intends to map task onto the most suitable machine in terms of makespan, efficiency, and incurred cost. In the experimental setup, four scientific workflows have been considered namely, LIGO, Genome, Cybershake, and Montage, they have been tested on the proposed approach while making comparison with the existing approaches namely FPA, GWO, and GA. The simulation results justified the claims by allocating resources to the cloudlets efficiently and stabilizing all the aforementioned parameters by attaining performance measures adequately. [ABSTRACT FROM AUTHOR]

This paper proposes a three‐stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK‐iD‐N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual‐objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK‐D) with neighbourhood insertion algorithm into task allocation, to get high‐quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK‐iD‐N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK‐D to get better solutions of the iK‐iD‐N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K‐iD‐N) that iK‐ iD‐N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions. [ABSTRACT FROM AUTHOR]

Various researches have been conducted to discover the machinery that led to the evolvement of non-symmetric formation of groups by uncountable marine animals. The huge of tasks comes per unit of time brought obstacles to assign each to particular server, while task assignment have needed a fast strategy to make decision. Artificial fish affect the environment through their behavior and the behavior of their peers. Creating a synthetic fish model has two parts: variables and functions which could be used for task assignment. This paper present improved fish swarm algorithm (IFSA) for task assignment to reduce the latency in cloud computing that could achieve one green computing goals. The research trying to reduce the pending job numbers compared with exist research. [ABSTRACT FROM AUTHOR]

Partial domain adaptation (PDA) is a framework for mitigating the covariate shift problem when target labels are contained in source labels. For this task, adversarial neural network (ANN) methods proposed in the literature have been proven to be flexible and effective. In this work, we adapt such methods to tackle the more general problem of open-set domain adaptation (OSDA), which further allows the existence of target instances with labels outside the source labels. The aim in OSDA is to mitigate the covariate shift problem and to identify target instances with labels outside the source label space. We show that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and we propose an adaptation for effective OSDA. [ABSTRACT FROM AUTHOR]

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect. [ABSTRACT FROM AUTHOR]

The descriptions of complex events usually span sentences, so we need to extract complete event information from the whole document. To address the challenges of document-level event extraction, we propose a novel framework named Document-level Event Extraction as Relation Extraction (DEERE), which is suitable for document-level event extraction tasks without trigger-word labelling. By well-designed task transformation, DEERE remodels event extraction as single-stage relation extraction, which can mitigate error propagation. A long text supported encoder is adopted in the relation extraction model to aware the global context effectively. A fault-tolerant event integration algorithm is designed to improve the prediction accuracy. Experimental results show that our approach advances the SOTA for the ChFinAnn dataset by an average F1-score of 3.7. The code and data are available at https://github.com/maomaotfntfn/DEERE. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. Literature shows that HEFT gives extraordinary results in terms of quality of schedule and execution time. However, in some cases, the average computation cost and selection of the first idle slot may not produce a good solution. Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. This paper suggests enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines. Our findings also suggest that enhanced versions of the HEFT algorithm perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines. [ABSTRACT FROM AUTHOR]

In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user's requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double- Q -learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min–max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal–dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min–max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time. [ABSTRACT FROM AUTHOR]

A number of scheduling algorithms have been proposed for real-time parallel tasks modeled as Directed Acyclic Graphs (DAGs). Many of them focus on scheduling DAG tasks with implicit deadlines. Fewer studies have considered DAG tasks with constrained deadlines or arbitrary deadlines. In this study, we propose a scheduling strategy based on fluid scheduling theory and we target DAG tasks with constrained or arbitrary deadlines. We prove that the proposed algorithm has a capacity augmentation bound of $\frac{1}{2}(1+\beta +\sqrt{(1+\beta)^2-\frac{4}{m}})$ 1 2 (1 + β + (1 + β) 2 - 4 m ) when scheduling multiple DAG tasks with constrained deadlines, in which $m$ m is the number of processors and $\beta$ β is the maximum ratio of task period to deadline. This value is lower than the current best result $\beta +2\sqrt{(\beta +1-\frac{1}{m})(1-\frac{1}{m})}$ β + 2 (β + 1 - 1 m) (1 - 1 m) . We also prove that a capacity augmentation bound of $\frac{1}{2}(1+\sqrt{2}+\sqrt{(1+\sqrt{2})^2-\frac{4\sqrt{2}}{m}})$ 1 2 (1 + 2 + (1 + 2) 2 - 4 2 m ) is guaranteed by our algorithm in the case of scheduling multiple DAG tasks with deadlines greater than periods. To the best of our knowledge, this is the first capacity augmentation bound that has been proven for scheduling multiple DAG tasks with deadlines greater than periods. Our experiments show that our algorithm outperforms the state of the art scheduling algorithms in the percentage of schedulable task sets. [ABSTRACT FROM AUTHOR]

In this work, we extend the topology-based framework and method for the quantification and classification of general resilient asynchronous complexity. We present the arbitrary resilient asynchronous complexity theorem, applied to decision tasks in an iterated delayed model which is based on a series of communicating objects, each of which mainly consists of the delayed algorithm. In order to do this, we first introduce two topological structures, delayed complex and reduced delayed complex, and build the topological computability model, and then investigate some properties of those structures and the computing power of that model. Our theorem states that the time complexity of any arbitrary resilient asynchronous algorithm is proportional to the level of a reduced delayed complex necessary to allow a simplicial map from a task's input complex to its output complex. As an application, we use it to derive the bounds on time complexity to approximate agreement with n + 1 processes. [ABSTRACT FROM AUTHOR]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

With the advancement of networking technologies and large-scale social computing, multi-user collaboration is becoming popular. In large-scale and dynamic collaborative environments, users may arbitrarily join or quit a collaboration group and work on common tasks for a long time. When users synchronize their work with the other collaborators, a large number of concurrent updates from the collaborators must be reconciled or merged to produce a consistent document state, which could challenge existing consistency maintenance approaches. Our idea is to accelerate the commutative replicated data type (CRDT)-based consistency maintenance algorithms with multi-core processors. We theoretically prove a general commutative condition for CRDT operations. One typical CRDT algorithm, replicated growable array (RGA), is transformed into Parallel RGA (PRGA) under the guidance of the general commutative condition. PRGA allows a batch of RGA operations to be executed in parallel. Experimental results showed that when processing millions of concurrent updates, PRGA achieves approximately 4 × performance gains over RGA on an ordinary four-core processor. [ABSTRACT FROM AUTHOR]

Efficiently implementing the divide-and-conquer pattern of parallelism in distributed memory systems is very relevant, given its ubiquity, and difficult, given its recursive nature and the need to exchange tasks and data among the processors. This task is noticeably further complicated in the presence of multi-core systems, where hybrid parallelism must be exploited to attain the best performance, and when unbalanced and deep workloads are considered, as additional measures must be taken to load balance and avoid deep recursion problems. In this manuscript a parallel skeleton that fulfills all these requirements while providing high levels of usability is presented. In fact, the evaluation shows that our proposal is on average 415.32% faster than MPI codes and 229.18% faster than MPI + OpenMP benchmarks, while offering an average improvement in the programmability metrics of 131.04% over MPI alternatives and 155.18% over MPI + OpenMP solutions. [ABSTRACT FROM AUTHOR]

In recent years, there have been constant disputes over college student cadres. Starting from the public opinion crisis of College Students' autonomy and using the "K-core" algorithm, this paper found that the college student cadres could not realize their own correct roles. The fuzzy cognitive map (FCM) model was used to analyze 49 influencing factors of N University's retreat action. It is found that the five factors of mass support, ability, ideology and morality, work tasks, and performance evaluation are important factors leading to the retreat action. This research proposes role reconstruction and system rectification strategies for the five variables in order to achieve a true feeling of student autonomy and good governance. [ABSTRACT FROM AUTHOR]

Logistics problems lie at the core of industries' everyday operations. These types of problems are also one of the main points of interest in the operational research field of study. In recent years, the workforce scheduling and routing problems (WSRP) class of optimization problems has gained significant attention from researchers. In this paper, a recently proposed WSRP is studied: the multiperiod WSRP with dependent tasks. In this problem, a given company provides services by means of heterogeneously skilled mobile teams. Service requests are known beforehand and each service is composed of one or more activities. Execution of one activity may depend on the completion of another. A feasible schedule in which the requested activities are completed in the minimum amount of days must be provided. Two matheuristic algorithms are proposed. Several new upper bounds for a set of instances from the literature are discovered. [ABSTRACT FROM AUTHOR]

The task allocation process in the Witkey mode is dynamic and open, in which Witkey is a rational person. Due to Witkey's individual rationality, in order to make the result of task allocation stable, task allocation must reach the Nash equilibrium. However, the Nash equilibrium point does not necessarily have the highest total system revenue. In order to make the task allocation result stable and have a high total system revenue, this paper proposes an incentive measure based on integral ranking to improve user participation. When Witkey adopts the best response strategy to select task, the order of participating in the selection will affect the individual income of Witkey to a certain extent. The higher the order, the greater the probability of obtaining higher system income. Based on this idea, a dynamic task allocation algorithm with complex tasks in the Witkey mode is proposed by combining incentive measures with best response strategy and reasonable benefit allocation strategy. The finally simulation results verified the effectiveness of the proposed algorithm, and the impact of incentive measures on the total revenue of the system was also examined. [ABSTRACT FROM AUTHOR]

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. [ABSTRACT FROM AUTHOR]

In the era of enhancing demand for online resources cloud is emerging as the greatest cutting edge technologies to serve the requirements. With volatility in the usage of the cloud resources, it is a tough task to serve the user requirements. Heuristic algorithms, Meta heuristic algorithms are trending these days, as they are helping to find the nearby optimal solutions within a reasonable time. But they suffer from either slow convergence or with premature solutions. It is evident that NP-complete algorithms take exponential time to find an efficient and optimal solution. This paper hybridizes grey wolf optimization along with Crow search algorithm. This balances the exploration and exploitation. The experimental results prove that the proposed algorithm is on par with existing algorithms and at times it shows better performance. [ABSTRACT FROM AUTHOR]

We consider the problem of fairly dividing a set of indivisible items. Much of the fair division literature assumes that the items are "goods" that yield positive utility for the agents. There is also some work in which the items are "chores" that yield negative utility for the agents. In this paper, we consider a more general scenario in which an agent may have positive or negative utility for each item. This framework captures, e.g., fair task assignment, where agents can experience both positive and negative utility for each task. We demonstrate that whereas some of the positive axiomatic and computational results extend to this more general setting, others do not. We present several new and efficient algorithms for finding fair allocations in this general setting. We also point out several gaps in the literature regarding the existence of allocations that satisfy certain fairness and efficiency properties and examine the complexity of computing such allocations. [ABSTRACT FROM AUTHOR]

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing. [ABSTRACT FROM AUTHOR]

Well connected users are generally discovered in communities which is one of the most important tasks for network data analytics and has tremendous real applications. In recent years, community search in attributed graphs has begun to attract attention, which aims to find communities that are both structure and attribute cohesive. Meanwhile, searching a community that is structure cohesive but attribute diversified, denoted as attribute diversified community search, is still at an early stage. In this paper, we introduce our recent effort for discovering attribute diversified community. In fact, for different applications, the needs of attribute diversification for modelling the community are quite different. We introduce three attribute diversified community models in which attribute diversification takes different roles for presenting as an objective and as a constraint. We also discuss major techniques for speeding up the attribute diversified community search. We conduct extensive experiments to show the effectiveness and efficiency of our algorithms for finding attribute diversified communities in various settings. [ABSTRACT FROM AUTHOR]

Multi-label classification is the task of inferring the set of unseen instances using the knowledge obtained through the analysis of a set of training examples with known label sets. In this paper, a multi-label classifier fusion ensemble approach named decision templates for ensemble of classifier chains is presented, which is derived from the decision templates method. The proposed method estimates two decision templates per class, one representing the presence of the class and the other representing its absence, based on the same examples used for training the set of classifiers. For each unseen instance, a new decision profile is created and the similarity between the decision templates and the decision profile determines the resulting label set. The method is incorporated into a traditional multi-label classifier algorithm: the ensemble of classifier chains. Empirical evidence indicates that the use of the proposed decision templates adaptation can improve the performance over the traditionally used combining schemes, especially for domains with a large number of instances available, improving the performance of an already high-performing multi-label learning method. [ABSTRACT FROM AUTHOR]

As one of the most popular parallel data processing models, data analysis system MapReduce has been widely used in many fields. Task scheduling is the core module in MapReduce system, and the quality of the scheduling algorithm directly affects the processing capacity of the system. Since new nodes need to be continuously added in the cluster to improve the processing capacity of the cluster, objectively, the heterogeneity of the cluster is caused. Heterogeneous environment is common in practical application scenarios, but there has been little research on task scheduling in heterogeneous environment. For this reason, this paper presents an in-depth study of task scheduling in heterogeneous environment and proposes a new task scheduling algorithm HTD. First, we give a formal definition of the throughput-driven task scheduling problem in a heterogeneous environment. Second, we design the scheduling algorithm HTD, which quickly obtains the completion sequence of a jobs set and optimizes the task scheduling details in heterogeneous environment. Finally, a series of experiments show the efficiency and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Estimating the reliability of cognitive task datasets is commonly done via split-half methods. We review four methods that differ in how the trials are split into parts: a first-second half split, an odd-even trial split, a permutated split, and a Monte Carlo-based split. Additionally, each splitting method could be combined with stratification by task design. These methods are reviewed in terms of the degree to which they are confounded with four effects that may occur in cognitive tasks: effects of time, task design, trial sampling, and non-linear scoring. Based on the theoretical review, we recommend Monte Carlo splitting (possibly in combination with stratification by task design) as being the most robust method with respect to the four confounds considered. Next, we estimated the reliabilities of the main outcome variables from four cognitive task datasets, each (typically) scored with a different non-linear algorithm, by systematically applying each splitting method. Differences between methods were interpreted in terms of confounding effects inflating or attenuating reliability estimates. For three task datasets, our findings were consistent with our model of confounding effects. Evidence for confounding effects was strong for time and task design and weak for non-linear scoring. When confounding effects occurred, they attenuated reliability estimates. For one task dataset, findings were inconsistent with our model but they may offer indicators for assessing whether a split-half reliability estimate is appropriate. Additionally, we make suggestions on further research of reliability estimation, supported by a compendium R package that implements each of the splitting methods reviewed here. [ABSTRACT FROM AUTHOR]

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of | G | groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl 2) time for k iterations, p repetitions and l tasks. [ABSTRACT FROM AUTHOR]

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The effectiveness of the Wolf Pack Algorithm (WPA) in high-dimensional discrete optimization problems has been verified in previous studies; however, it usually takes too long to obtain the best solution. This paper proposes the Multi-Population Parallel Wolf Pack Algorithm (MPPWPA), in which the size of the wolf population is reduced by dividing the population into multiple sub-populations that optimize independently at the same time. Using the approximate average division method, the population is divided into multiple equal mass sub-populations whose better individuals constitute an elite sub-population. Through the elite-mass population distribution, those better individuals are optimized twice by the elite sub-population and mass sub-populations, which can accelerate the convergence. In order to maintain the population diversity, population pretreatment is proposed. The sub-populations migrate according to a constant migration probability and the migration of sub-populations are equivalent to the re-division of the confluent population. Finally, the proposed algorithm is carried out in a synchronous parallel system. Through the simulation experiments on the task assignment of the UAV swarm in three scenarios whose dimensions of solution space are 8, 30 and 150, the MPPWPA is verified as being effective in improving the optimization performance. [ABSTRACT FROM AUTHOR]

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling. [ABSTRACT FROM AUTHOR]

The computing power of mobile devices is too limited to execute computation tasks fast. Mobile edge computing (MEC) allows mobile devices to offload tasks to near servers to reduce the completion time of the tasks (a.k.a makespan). The input data of some critical tasks should be encrypted before the offloading. Aiming at the security critical tasks in the MEC composed of multiple servers, this paper addresses to minimize the makespan by scheduling security-critical tasks. We provide the formulation of the problem which is generally an integer programming problem, and three effective composite heuristics CH1–CH3 are proposed to solve the problem. Task permutations are considered as solutions. We construct a greedy heuristic algorithm to calculate the value of the objective. These three composite heuristics consist of two phases: solution initialization and solution improvement. In the first phase, the solutions of all the proposed algorithms are generated by a task arrangement rule called Biggest data Task First (BTF), and then in the second phase, improved by three searching methods based on different neighborhoods including a insertion neighborhood, a swap neighborhood and a hybrid neighborhood, respectively. Experimental results show that CH1–CH3 outperform the well-known RoundRobin algorithm. Particularly, BTF is demonstrated to initialize highly qualified solutions, making contributions to the high effectiveness. Meanwhile, all the improvement methods are justified to be effective and the method based on the hybrid neighborhood achieves the best effectiveness. [ABSTRACT FROM AUTHOR]

Cloud computing will continue to play a critical role due to its ability to deliver various types of on-demand information technology (IT) services according to customer needs. Unfortunately, cloud computing has faced many challenges. The task scheduling problem is considered one of the main challenges because the user has to pay for a resource. Therefore, a good mapping of users' tasks on the available resources is required to reduce the total execution time of the users' tasks (i.e., reduce makespan), execution cost, enhance load balancing, increase resource utilization, etc. Many heuristic algorithms have been existed to solve task scheduling problems. Although, the Heterogeneous Earliest Finish Time (HEFT) heuristic algorithm is considered one of the best task scheduling algorithms in a heterogeneous environment, it does not concern load balancing. In this paper, a modification has been introduced to the HEFT algorithm to satisfy load balancing. The modified algorithm is called Load Balancing HEFT (LB-HEFT). A comparative study has been done among the proposed LB-HEFT, the Enhancement Heterogeneous Earliest Finish Time (E-HEFT), and HEFT algorithms to evaluate the performance of the proposed LB-HEFT algorithm. According to the comparative results, it is found that the proposed LB-HEFT algorithm outperforms the existing E-HEFT and HEFT algorithms by improving load balancing by 43.49% and 72.59%, respectively in average, enhancing resource utilization by 2.28% and 5.61%, respectively in average, and reducing makespan by 7.55%, and 3.75% respectively in average. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Task scheduling is the main problemin cloud computing that reduces systemperformance; it is an important way to arrange user needs and perform multiple goals. Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation, task scheduling, security, privacy, etc. To improve system performance, an efficient task-scheduling algorithm is required. Existing task-scheduling algorithms focus on task-resource requirements, CPU memory, execution time, and execution cost. In this paper, a task scheduling algorithm based on a Genetic Algorithm (GA) has been presented for assigning and executing different tasks. The proposed algorithm aims to minimize both the completion time and execution cost of tasks and maximize resource utilization. We evaluate our algorithm's performance by applying it to two examples with a different number of tasks and processors. The first example contains ten tasks and four processors; the computation costs are generated randomly. The last example has eight processors, and the number of tasks ranges from twenty to seventy; the computation cost of each task on different processors is generated randomly. The achieved results show that the proposed approach significantly succeeded in finding the optimal solutions for the three objectives; completion time, execution cost, and resource utilization. [ABSTRACT FROM AUTHOR]

This study deals with the task assignment problem in a heterogeneous unmanned aerial vehicle system under the multiple constraints of limited resources, effective task execution scope, obstacle avoidance and task priority. Considering the nonlinearity, multimoding and high complexity of the task assignment problem, many existing optimization methods may fall into local optimum and even fail to provide satisfactory solutions. To address the issues, the discrete adaptive search whale optimization algorithm is proposed. First, we present the obstacle-avoiding distance estimation method to provide precise task scenario information and reduce the computational complexity. Then, the crossover-based updating method is presented to enable the algorithm to solve discrete problems such as task assignment. In addition, the search intensity adaptive mechanism is presented to optimize and balance the exploration and exploitation intensities in the solution space reasonably, thereby achieving high-quality task assignment results quickly. Further, joint leadership mechanism and local search mechanism are proposed to enhance the exploration ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: With the emergence of intelligent vehicles, how to satisfy the demands of the vehicles with computing‐intensive and delay‐sensitive tasks has become a challenging issue. Vehicular edge computing (VEC) is proposed as an advanced paradigm to improve the service of vehicles through offloading the task to the VEC servers. Nevertheless, VEC servers always have limited computation resources and do not satisfy the offloading requirements of vehicles. To this end, in this paper, we propose a more flexible offloading scheme by jointly considering the offloading strategies and the price strategy. In the proposed scheme, where the task can be dynamically divided into two parts parallel executed at the vehicles and VEC servers. A multi‐leader and multi‐follower Stackelberg game ‐based distributed algorithm is proposed to maximize the utilities of the vehicles and the VEC servers under the delay constraint. Finally, the game equilibrium is analyzed and achieved. Extensive experiments demonstrate that the proposed offloading scheme converges fast and always outperforms the existing schemes in terms of the vehicular utility under different network conditions. For instance, the proposed scheme achieves the utility improvement over 56.62% compared to the fixed selection strategy and achieves the utility improvement up to 161.0% compared to the complete offloading with fixed price strategy when the number of vehicles is 10. Additionally, the effects of key parameters such as the offloading strategies and, the price strategy, and the computation resource on the average utility of vehicles are also discussed and analyzed based on the simulation results. [ABSTRACT FROM AUTHOR]

Recent advancements of virtualization technologies for parallel processing involve scheduling containerized tasks in a workflow. Since a container can include multiple tasks, it can be reused or shared among applications. If every task in a workflow uses its dedicated container without sharing among any tasks, each container image must be downloaded for each task. As a result, many computational resources are required to process and the communication latency related to container image downloading can become a bottleneck for the makespan. In task scheduling algorithms for workflows, this characteristic produces a new challenging issue that how effectively shares containers among tasks to avoid redundant container image download processes and redundant task allocations. One of the fundamental problems is that no policy has been established for simultaneously satisfying effective container sharing, maintaining the degree of task parallelism, and effective computational resource utilization. In this paper, we propose a clustering-based containerized task scheduling algorithm for clouds, namely, shareable functional task clustering for utilizing virtualized resources (SF-CUV). The objective of SF-CUV is to minimize the makespan with less computational resources and containers than other algorithms by clustering tasks and sharing each container among tasks. SF-CUV consists of two phases: (i)task clustering and pre-virtual CPU (vCPU) allocation phase to derive an accurate scheduling priority, and (ii)task ordering and actual task reallocation phase. Experimental results obtained via simulation and in a real environment show that SF-CUV can utilize both vCPUs and containers with a shorter makespan compared with other approaches. [ABSTRACT FROM AUTHOR]

Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms. [ABSTRACT FROM AUTHOR]

The problem of precisely computing the worst-case blocking time that tasks may experience is one of the fundamental issues of schedulability analysis of real-time applications. While exact methods have been proposed for more sophisticated protocols, the problem is indeed complex in case of the Priority Inheritance Protocol, even restricting the attention to uniprocessor systems, non-nested resource accesses, and non-self-suspending tasks. Besides a very simple method leading in general to loose upper bounds, only one algorithm of exponential complexity has been so far reported in literature to tighten such bounds. In this article, we describe a novel approach which, leveraging an operational research technique for modeling the problem, computes the same tight bounds in polynomial time. We then discuss the scenarios in which, assuming no conditional statements in the tasks’ code, the computed bounds derive from an actually impossible blocking chain, and we refine the initial model to more precisely compute the worst-case blocking times for any task set in any possible operating condition. [ABSTRACT FROM AUTHOR]

Fleets of cooperative drones are a powerful tool in monitoring critical scenarios requiring early anomaly discovery and intervention. Due to limited energy availability and application requirements, drones may visit target points in consecutive trips, with recharging and data offloading in between. To capture timeliness of intervention and prioritize early coverage, we propose the new notion of Weighted Progressive Coverage, which is based on the definition of time dependent weights. Weighted progressive coverage generalizes classic notions of coverage, as well as a new notion of accumulative coverage specifically designed to address trip scheduling. We show that weighted progressive coverage maximization is NP-hard and propose an efficient polynomial algorithm, called Greedy and Prune (GaP), with guaranteed approximation. By means of simulations we show that GaP performs close to the optimal solution and outperforms a previous approach in all the considered performance metrics, including coverage, average inspection delay, energy consumption, and computation time, in a wide range of application scenarios. Through prototype experiments we also confirm the theoretical and simulation analysis, and demonstrate the applicability of our algorithm in real scenarios. [ABSTRACT FROM AUTHOR]

The Cloud-IoT framework offers on-demand service for numerous applications with the aid of data gathered by IoT and the computing resources of cloud computing. The quality of service (QoS) degrades due to task-VM mismatch due to the heterogeneous service request from IoT devices. The tasks processed by an inappropriate VM may cause delay and affect the Quality of Service (QoS). The proposed task allocation and scheduling algorithm aim is to improve the QoS of education service offered by Cloud-IoT in an educational organisation. In the task allocation stage, task VM pairs are prioritized initially and task-VM pairs are selected based on the minimum of the expected completion time (ECT) with the approach named Priority Based Task Allocation and Buffering (PBTAB) Algorithm. In this stage, at each of the clouds, the selected task-VM pairs are placed on queues based on the proximal value of the MCT. In the scheduling stage, task-VM pair matching (T-VMBS) Algorithm schedules the task with the selection of the best of the VM from the total clouds to speed up the task execution. The PBTAB and T-VMBS algorithm achieved throughput performance of more than 90% with larger dataset and huge number of VM. The proposed approach achieved a decreased makespan of less than 50%. Similarly deadline violation rate and average reliability exhibited a better performance. [ABSTRACT FROM AUTHOR]

Radar sea clutters are echoes reflected from a patch of ocean surface, which may significantly interfere with the signals from targets, and seriously degrade the performance of marine radar remote sensing. Thus, it is vital to eliminate the effects of sea clutter. In this paper, we aim at normalizing sea clutter to a uniform level. Firstly, a detailed analysis about the characteristics and differences of clutter and targets is presented; then, we present a heuristic processing scheme which works by solving the task of sea clutter normalization as a classification problem followed by energy normalization. Multiscale and speed-up strategies are incorporated into the dynamic clustering algorithm to found a robust real-time normalization method. Finally, extensive experiments show state-of-the-art results on challenging sea clutter echoes, which demonstrate the feasibility and robustness of the proposed adaptive clustering normalization method. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved. [ABSTRACT FROM AUTHOR]

To create a precise model structure and perform fault monitoring algorithms for a wide range of complex systems along with dynamic behavioural characteristics, the causal graph-based methods were considered herein. In this paper, a new scheme was devised based on fast fault detection mechanism relying on the Directed Hypergraph Observer model. The performance of the suggested pattern is illustrated by a case study including systems with single energy. Noting that the modern methods possess a large range of applications for the reliability and energy effectiveness analysis related to multi-energy systems. The Directed Hypergraph model architecture was exploited to generate the diagnostic condition based on a graphical observer. [ABSTRACT FROM AUTHOR]

• Addressing the worst-case performance of labeling methods like the Coffman-Graham algorithm. • Focus on delayed precedence constraints in the single- and multi-processor case. • New lower bounds on worst-case approximation guarantees are derived. • The results extend to more general level-based or critical-path scheduling techniques. • An open problem by Bernstein, Rodeh, and Gertner from 1989 is resolved. There is some evidence that labeling schemes as employed for instance in the famous Coffman-Graham algorithm may provide superior worst-case approximation guarantees than purely path- or level-based list schedules in the presence of (delayed) precedence constraints. In 1989, Bernstein, Rodeh, and Gertner proved that this also holds true for their labeling scheme targeting unit execution time tasks on a single processor provided that all delays imposed by a single task on all of its successors are uniformly either zero or some fixed positive integer. They further conjectured that this superiority is preserved when allowing the delays imposed by a task to differ successor-wise. It is shown in this note however that their labeling scheme as well as more general ones may perform as bad as any list schedule in this case. Moreover, a new lower bound on the worst-case performance of labeling methods in the multiprocessor setting is derived. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the crowdsensing platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users’ preference and reliability. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user’s preference and reliability into consideration. We first present a hybrid preference metric to characterize users’ preference by exploiting their implicit feedback. Then, to profile users’ reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack users’ historical information, we further propose a matrix factorization method to infer the users’ reliability levels on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to the benchmarks in both user profiling and personalized task recommendation. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) is a popular way of data collection, which forms the large-scale sensing system by smart mobile terminal users and provides multimodal sensor data. In the sensing scenario, there are various sense resource requirements of tasks released by the platform. One of the most urgent issues in MCS is how to choose corresponding users with appropriate sense resources to accomplish assigned tasks. In this article, cooperating among a host of users to perform sense tasks is considered. Firstly, the cooperation among users to accomplish the sense tasks is described as an overlapping coalition formation game (OCF game). In addition, an initial coalition method of using social networks (SN) is proposed to accelerate the formation of coalition. Finally, the cooperation degree is used to describe the cooperative relationships among users, and virtual terminal coalition formation (VTCF) algorithm is proposed to simplify the process of coalition formation. The simulated results show that the proposed approach effectively improves the system's utility under the constraints of task cost and sense quality. [ABSTRACT FROM AUTHOR]