Task Nodes Research Articles

Privacy-Preserving Distributed Multi-Task Learning against Inference Attack in Cloud Computing

Because of the powerful computing and storage capability in cloud computing, machine learning as a service (MLaaS) has recently been valued by the organizations for machine learning training over some related representative datasets. When these datasets are collected from different organizations and have different distributions, multi-task learning (MTL) is usually used to improve the generalization performance by scheduling the related training tasks into the virtual machines in MLaaS and transferring the related knowledge between those tasks. However, because of concerns about privacy breaches (e.g., property inference attack and model inverse attack), organizations cannot directly outsource their training data to MLaaS or share their extracted knowledge in plaintext, especially the organizations in sensitive domains. In this article, we propose a novel privacy-preserving mechanism for distributed MTL, namely NOInfer, to allow several task nodes to train the model locally and transfer their shared knowledge privately. Specifically, we construct a single-server architecture to achieve the private MTL, which protects task nodes’ local data even if n-1 out of n nodes colluded. Then, a new protocol for the Alternating Direction Method of Multipliers (ADMM) is designed to perform the privacy-preserving model training, which resists the inference attack through the intermediate results and ensures that the training efficiency is independent of the number of training samples. When releasing the trained model, we also design a differentially private model releasing mechanism to resist the membership inference attack. Furthermore, we analyze the privacy preservation and efficiency of NOInfer in theory. Finally, we evaluate our NOInfer over two testing datasets and evaluation results demonstrate that NOInfer efficiently and effectively achieves the distributed MTL.

Dynamic Task Scheduling Scheme for Processing Real-Time Stream Data in Storm Environments

Owing to the recent advancements in Internet of Things technology, social media, and mobile devices, real-time stream balancing processing systems are commonly used to process vast amounts of data generated in various media. In this paper, we propose a dynamic task scheduling scheme considering task deadlines and node resources. The proposed scheme performs dynamic scheduling using a heterogeneous cluster consisting of various nodes with different performances. Additionally, the loads of the nodes considering the task deadlines are balanced by different task scheduling based on three defined load types. Based on diverse performance evaluations it is shown that the proposed scheme outperforms the conventional schemes.

A Dynamic Scheduling Workflow Algorithm Based On Critical Path

With the explosion in the size of scientific workflows, local workflows can no longer satisfy existing computing needs, and cloud computing platforms have become the first choice for scientific workflows. Compared with traditional local computing, cloud computing platform not only needs to consider the loss of scheduling time and transmission time, but also involves the cost of cloud resources. Therefore, whether the scheduling scheme is reasonable becomes the decisive factor of workflow efficiency. Aiming at how to achieve efficient scheduling under cost constraints, a dynamic scheduling algorithm based on critical path is proposed. The algorithm uses the optimized Dijkstra algorithm to classify the task nodes, guarantees the completion time of key nodes in the scheduling process, adjusts the priority of nodes dynamically, and selects the best resources according to the loss weight. Experiments show that the optimization rate increases with the increase of the number of task nodes. The algorithm proposed in this paper is suitable for large-scale workflow operation and can effectively reduce the scheduling time.

Multiple Task Assignment and Path Planning of a Multiple Unmanned Surface Vehicles System Based on Improved Self-Organizing Mapping and Improved Genetic Algorithm

This paper addresses multiple task assignment and path-planning problems for a multiple unmanned surface vehicle (USVs) system. Since it is difficult to solve multi-task allocation and path planning together, we divide them into two sub-problems, multiple task allocation and path planning, and study them separately. First, to resolve the multi-task assignment problem, an improved self-organizing mapping (ISOM) is proposed. The method can allocate all tasks in the mission area, and obtain the set of task nodes that each USV needs to access. Second, aiming at the path planning of the USV accessing the task nodes, an improved genetic algorithm (IGA) with the shortest path is proposed. To avoid USV collision during navigation, an artificial potential field function (APFF) is proposed. A multiple USV system with multi-task allocation and path planning is simulated. Simulation results verify the effectiveness of the proposed algorithms.

Flexible optimal Kalman filtering in wireless sensor networks with intermittent observations

This paper is concerned with the distributed Kalman filtering over the wireless sensor networks (WSNs) in the presence of intermittent observations and different sensing states, where only task nodes are required to estimate the state of a linear time-invariant discrete-time system. A class of flexible binary values is used to develop the adaptability of flexible optimal Kalman filtering (FOKF) for variable sensing states. Based on the minimum error covariance trace principle, two classes of FOKFs have optimal collaborative estimation via their own and community observations, including the original FOKF and the FOKF with uncertain noise variance. The performance analysis of these two types of filters show that they have high estimation accuracy, strong robustness, low energy consumption and user-friendliness. The proposed algorithms are applied to estimate and track the position of a moving target in WSNs. The simulation illustrates that the proposed filters have superior performance, compared with the existing algorithms.

Energy-Efficient Task Offloading in Massive MIMO-Aided Multi-Pair Fog-Computing Networks

The energy-efficient task offloading problem of a massive multiple-input multiple-output (MIMO)-aided fog computing system is solved, where multiple task nodes offload their computational tasks to be solved via a massive MIMO-aided fog access node to multiple processing nodes in the fog for execution. By considering realistic imperfect channel state information (CSI), we formulate a joint task offloading and power allocation problem for minimizing the total energy consumption, including both computation and communication power consumptions. We solve the resultant non-convex optimization problem in two steps. First, we solve the computational task allocation and computational resource allocation for a given power allocation. Then, we conceive a sequential optimization framework for determining the specific power allocation decision that minimizes the total energy consumption of the fog access node. Given the computational tasks, the computational resources, and the power allocation, we propose an iterative algorithm for the system optimization. The simulation results show that the proposed scheme significantly reduces the total energy consumption compared to the benchmark schemes.

Parallel Swarm Intelligent Motion Planning with Energy‐Balanced for Multirobot in Obstacle Environment

Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly‐shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy‐balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO‐AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

Cooperative computing schemes in wireless sensor networks

This study proposes three-node cooperative computing schemes for wireless sensor networks (WSNs), including parallel offload cooperative computing (POCC) and serial offload cooperative computing (SOCC). The cooperation system includes a task node with execution tasks and two peer help nodes. Specifically, the authors fully mobilise the computing and communication resources in the network by means of cooperative computing to minimise the energy consumption of processing tasks in the WSN while satisfying the delay constraints. The authors characterise these problems as optimal problems and find the optimal solutions using corresponding iterative algorithms, respectively. The simulation results show the superior performance of the proposed schemes in terms of energy consumption and node fairness. For different cases the superiority of POCC and SOCC is verified separately, and the SOCC is more suitable for the case where task node is located at the edge of WSN. Furthermore, the authors introduce a trade-off scheme between POCC and SOCC, which can always achieve the optimal scheme in different cases.

Online Task Scheduling and Resource Allocation for Intelligent NOMA-Based Industrial Internet of Things

Fog computing (FC) has the potential to process computation-intensive tasks in Industrial Internet of Things (IIoT) systems. In parallel with the development of FC, non-orthogonal multiple access (NOMA) has been recognized as a promising technique to significantly improve the spectrum efficiency. In this paper, a NOMA-based FC framework for IIoT systems is considered, where multiple task nodes offload their tasks via NOMA to multiple nearby helper nodes for execution. We formulate a joint task scheduling and subcarrier allocation problem, with an objective to minimize the total cost in terms of the delay and energy consumption, while taking into account the practical communication and computation constraints. Note that the task scheduling includes task, computation resource, and power allocations. Since the task and subcarrier allocations involve binary variables, it is challenging to obtain an optimal solution for such a combinatorial problem. To this end, we solve the task scheduling and subcarrier allocation problem in an online learning fashion. During the online learning process, we propose an iterative algorithm to jointly optimize the subcarrier allocation and task scheduling in each time episode. Simulation results show that the proposed scheme can significantly reduce the sum cost compared to the baseline schemes.

POST: Parallel Offloading of Splittable Tasks in Heterogeneous Fog Networks

Fog computing has been promoted to support delay-sensitive applications in future Internet of Things (IoT). For a general heterogeneous fog network consisting of many dispersive fog nodes (FNs), it may well happen that some of them have delay-sensitive tasks to process, i.e., task nodes (TNs), and some have spare resources to help the TNs to process tasks, i.e., helper nodes (HNs). It remains a fundamental challenge to effectively map multiple tasks or TNs into multiple HNs to minimize every task’s service delay in a distributed manner, i.e., the multitask multihelper (MTMH) problem. The problem becomes more challenging as tasks are splittable, i.e., tasks can be divided into multiple subtasks and offloaded to multiple HNs to further reduce the service delay via the scheme similar to distributed computing, because it introduces the more complicated task division problem which results in a much larger and more complex solution space. To tackle this challenge, in this article, a generalized Nash equilibrium problem (GNEP), called parallel offloading of splittable tasks (POST), is formulated and studied thoroughly. The structural properties of the problem are characterized and thus the existence of generalized Nash equilibrium (GNE) is proven via the fixed-point theorem. Furthermore, the corresponding distributed task offloading algorithm is developed via the Gauss–Seidel-type method. The simulation results show that the proposed POST algorithm can offer much better performance in terms of the system average delay, individual delay, delay reduction ratio (DRR), and number of beneficial TNs, compared with the existing solution to the counterpart problem for nonsplittable tasks.

JOTE: Joint Offloading of Tasks and Energy in Fog-Enabled IoT Networks

Fog computing is a promising solution to enable delay-sensitive applications in the Internet of Things (IoT). In this article, based on the simultaneous wireless information and power transfer (SWIPT) technology, we investigate the joint offloading of tasks and energy (JOTE) in fog-enabled IoT networks. Specifically, the task node is allowed to offload energy and tasks to multiple neighboring helper nodes in a time-division multiple access (TDMA) manner. When there are no task queues in the nodes, the offloading decision for each task is independent. We first find the offloading strategy to minimize the task execution delay as well as the energy consumption for a specific task and then, analyze the condition under which the JOTE is beneficial. We show that it becomes more and more desirable to offload both the tasks and the energy from the task node as the number of helper nodes gets large. When there are task queues in the nodes, the offloading decision for each task becomes temporally correlated. We then characterize the optimal strategies to offload the tasks and energy jointly over multiple time slots. An online offloading policy based on the Lyapunov optimization is then proposed to minimize the time average expected delay while stabilizing the system operation. Comprehensive numerical results corroborate our theoretical results and demonstrate the superior performance of the proposed JOTE algorithms.

A virtual execution platform for OpenFlow controller using NFV

The Software Defined Networking (SDN) paradigm decouples the network control functions from the data plane and offers a set of software components for flexible and controlled management of networks. SDN has promised to provide numerous benefits in terms of on-demand provisioning, automated load balancing, streamlining physical infrastructure, and flexibility in scaling network resources. In order to realize these network service offerings, there is an important need for developing an efficient, robust, and secure execution platform. As a primary contribution, we present a novel virtual execution platform for the OpenFlow controller using Network Function Virtualization (NFV). Theoretically, NFV can apply to any network function, which can simplify the managing of the heterogeneous data plane. The characteristics of our proposed architecture include pipe-lined processing of network traffic, virtualized and replicated execution of network functions, isolation between task nodes, and random mapping of traffic to task nodes. The proposed architecture has two major components: a Network Packet Schedulers (NPS) and a Task Engine (TE). The TE consists of Task Nodes (TNs) which are responsible for executing different network functions on various traffic flows and each TN is realized as a virtual machine. Upon receiving traffic from the data plane, NPS analyses the functional requirements of the traffic and different controller performance parameters. Then it allocates the traffic to appropriate TNs for executing necessary network functions. In this respect, it provides performance benefits, robustness, fine-grained modularity, and strong isolation security in the processing of traffic flows on the SDN platform. Efficacy of our proposed architecture has been demonstrated with a case study.

Resource Allocation Algorithm of Cloud Computing Infrastructure Services based on Continuous Optimization Algorithm

The cloud environment is extremely complex, dynamic and changeable. How to use cloud services efficiently to create economic benefits for enterprises and even society is a matter that most scholars and manufacturers pay close attention to at present. Therefore, the resource allocation of cloud computing becomes the focus of research. The purpose of this paper is to further study the resource allocation algorithm of cloud computing infrastructure services. Firstly, the IAAs mode in three main application modes of cloud computing is introduced in detail. Based on the understanding of optimization theory, the resource allocation algorithm of cloud computing infrastructure services with continuous optimization algorithm is established. The experimental results show that the number of iterations of the three algorithms is generally on the rise with the increase of the number of task nodes. In general, the number of iterations of ant colony algorithm is less than that of genetic algorithm, and the performance of continuous optimization algorithm is better than that of both, which shows that it can promote the overall efficiency of the algorithm.

A Novel GLS Consensus Algorithm for Alliance Chain in Edge Computing Environment

Edge computing devices are widely deployed. An important issue that arises is in that these devices suffer from security attacks. To deal with it, we turn to the blockchain technologies. The note in the alliance chain need rules to limit write permissions. Alliance chain can provide security management functions, using these functions to meet the management between the members, certification, authorization, monitoring and auditing. This article mainly analyzes some requirements realization which applies to the alliance chain, and introduces a new consensus algorithm, generalized Legendre sequence (GLS) consensus algorithm, for alliance chain. GLS algorithms inherit the recognition and verification efficiency of binary sequence ciphers in computer communication and can solve a large number of nodes verification of key distribution issues. In the alliance chain, GLS consensus algorithm can complete node address hiding, automatic task sorting, task automatic grouping, task node scope confirmation, task address binding and stamp timestamp. Moreover, the GLS consensus algorithm increases the difficulty of network malicious attack.

An Improved Genetic Algorithm on Hybrid Information Scheduling

Background: Patents suggest that efficient hybrid information scheduling algorithm is critical to achieve high performance for heterogeneous multi-core processors. Because the commonly used list scheduling algorithm obtains the approximate optimal solution, and the genetic algorithm is easy to converge to the local optimal solution and the convergence rate is slow. Methods: To solve the above two problems, the thesis proposes a hybrid algorithm integrating list scheduling and genetic algorithm. Firstly, in the task priority calculation phase of the list scheduling algorithm, the total cost of the current task node to the exit node and the differences of its execution cost on different processor cores are taken into account when constructing the task scheduling list, then the task insertion method is used in the task allocation phase, thus obtaining a better scheduling sequence. Secondly, the pre-acquired scheduling sequence is added to the initial population of the genetic algorithm, and then a dynamic selection strategy based on fitness value is adopted in the phase of evolution. Finally, the cross and mutation probability in the genetic algorithm is improved to avoid premature phenomenon. Results: With a series of simulation experiments, the proposed algorithm is proved to have a faster convergence rate and a higher optimal solution quality. Conclusion: The experimental results show that the ICLGA has the highest quality of the optimal solution than CPOP and GA, and the convergence rate of ICLGA is faster than that of GA.

POMT: Paired Offloading of Multiple Tasks in Heterogeneous Fog Networks

By providing shared and flexible communication, computation, and storage resources along the cloud-to-things continuum, fog computing has become an attractive technology to support delay-sensitive applications in Internet of Things (IoT) and future wireless networks. Consider a typical heterogeneous fog network consisting of different types of fog nodes (FNs), wherein some task nodes (TNs) have computation-intensive and delay-sensitive tasks, while some helper nodes (HNs) have spare computation resources for sharing with their neighboring nodes. In order to minimize the delay of every task, these TNs and HNs should be effectively associated in a distributed manner, which is the fundamental multi-task multi-helper (MTMH) problem. To tackle this challenging problem, a potential game called paired offloading of multiple tasks (POMT) is formulated and studied. Theoretical analysis proves the existence of the Nash equilibrium (NE) for this proposed game. Further, the corresponding POMT algorithm is developed for every TN to achieve the NE of the general game. The analytical and simulation results show that our POMT algorithm can offer the near-optimal performance in system average delay and delay reduction ratio (DRR), and achieve more number of beneficial TNs, at two orders of magnitude lower complexity than a centralized optimal algorithm for computation offloading.

Autonomous Parallelization of Resource-Aware Robotic Task Nodes

Robot task programming often leads to inefficient plans, as opportunities for parallelization and precomputation are usually not exploited by the programmer. This inefficiency is often especially obvious in mobile manipulation, where path planning and pose estimation algorithms are time-consuming operations. In this letter, we introduce the concept of resource-aware task nodes (RATNs), a powerful descriptive action model for robots. Next, we propose an algorithm that executes so-called concurrent dataflow task networks (CDTNs), robot plans consisting of RATNs. It optimizes programmed plans based on two sources of information: 1) The control flow represented in the original task plan, whose constraints are relaxed to generate opportunities for parallelization and precomputation. 2) Dependencies between actions pertaining to resources, data flows, and world model changes, the latter being equivalent to preconditions and effects. CDTNs have been integrated in our open-source task programming framework RAFCON, and we show that it leads to 11%–29% improvement in terms of execution time in two simulated mobile manipulation scenarios.

DOTS: Delay-Optimal Task Scheduling Among Voluntary Nodes in Fog Networks

Through offloading the computing tasks of the task nodes (TNs) to the fog nodes (FNs) located at the network edge, the fog network is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based networks. Unlike most existing researches based on the command-mode offloading and full capability report, this paper develops a general analytical model of the task scheduling among voluntary nodes (VNs) in fog networks, wherein the VNs voluntarily contribute their capabilities for serving their neighboring TNs. A novel delay-optimal task scheduling (DOTS) algorithm is proposed to obtain the delay-optimal offloading solution according to the reported capabilities of the VNs. Extensive simulations are carried out in a fog network, and the numerical results indicate that the proposed DOTS algorithm can effectively provide the optimal set of the helper nodes, subtask sizes, and the TN transmission power to minimize the overall task processing delay. Moreover, compared with the command-mode offloading, the voluntary-mode achieves more balanced offloading and a higher fairness level among the FNs.

A deadline constrained scheduling algorithm for cloud computing system based on the driver of dynamic essential path.

To solve the problem of the deadline-constrained task scheduling in the cloud computing system, this paper proposes a deadline-constrained scheduling algorithm for cloud computing based on the driver of dynamic essential path (Deadline-DDEP). According to the changes of the dynamic essential path of each task node in the scheduling process, the dynamic sub-deadline strategy is proposed. The strategy assigns different sub-deadline values to every task node to meet the constraint relations among task nodes and the user’s defined deadline. The strategy fully considers the dynamic sub-deadline affected by the dynamic essential path of task node in the scheduling process. The paper proposed the quality assessment of optimization cost strategy to solve the problem of selecting server for each task node. Based on the sub-deadline urgency and the relative execution cost in the scheduling process, the strategy selects the server that not only meets the sub-deadline but also obtains much lower execution cost. In this way, the proposed algorithm will make the task graph complete within its deadline, and minimize its total execution cost. Finally, we demonstrate the proposed algorithm via the simulation experiments using Matlab tools. The experimental results show that, the proposed algorithm produces remarkable performance improvement rate on the total execution cost that ranges between 10.3% and 30.8% under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than IC-PCP, DCCP and CD-PCP.

An operation sequence-based temporal multilayer networks model for production process in flexible manufacturing systems

This paper focuses on flexible manufacturing systems, which is a typical discrete event system. Different from traditional approaches for the system, this paper provides a multidimensional, ordinal, temporal topology structure to depict the dynamic production process from the perspective of complex networks. Firstly, the factors of real manufacturing environment are mapped to the multilayer networks and the frameworks of the model are given. The proposed temporal multilayer networks model includes intra-layers and inter-layers, involving two aspects of task and resource nodes and time-depending graphs respectively. Secondly, the generation rules to construct the networks are presented. A novel approach for evaluating bottleneck resources in time blocks is presented to evolve the networks model. Finally, the temporal multilayer networks model is generated based on theory of constraints. The proposed model based on operation sequence is superior in measurement of time-dependent performance of the system. Moreover, an application case for energy consumption evaluation is confirmed that the proposed model can support exploratory analysis of the time-related performance criterion in discrete manufacturing.