Execution Cost Research Articles

Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

A Centralized Multi-Criteria Method for Scheduling Tasks in a Cloud Computing Environment

Task scheduling determines the order of mapping tasks to virtual machines to meet objectives. In this paper, a batch mode heuristic method that is centralized, dynamic, and multi-objective has been presented for scheduling independent tasks with a deadline and belonging to several user levels, using the cloud elasticity in the public cloud environment. In this method, it has been intended to improve the objectives of makespan, deadline violation, total execution cost, and load balancing by considering the tasks’ prioritization based on the criteria of user level, deadline, task length, and selection of heterogeneous virtual machines according to processing power, workload and usage cost. The proposed method was simulated using the CloudSim tool. Besides, the method’s ability to achieve the mentioned goals has been evaluated in comparison with similar methods. The evaluation results, established on standard test data, show that the proposed method has a good performance in improving its objectives.

Reinforcement Learning-Based Fixed-Time Trajectory Tracking Control for Uncertain Robotic Manipulators With Input Saturation.

A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied. The designed RL control algorithm is implemented by a radial basis function (RBF) neural network (NN), in which the actor NN is used to generate the control strategy and the critic NN is used to evaluate the execution cost. A new nonsingular fast terminal sliding mode technique is used to ensure the convergence of tracking error in fixed time, and the upper bound of convergence time is estimated. To solve the saturation problem of an actuator, a nonlinear antiwindup compensator is designed to compensate for the saturation effect of the joint torque actuator in real time. Finally, the stability of the closed-loop system based on the Lyapunov candidate is analyzed, and the timing convergence of the closed-loop system is proven. Simulation and experimental results show the effectiveness and superiority of the proposed control law.

Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning

Performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. It is challenging to identify suitable orders prior to query execution due to the huge search space of possible orders and unreliable execution cost estimates in case of data skew or data correlation. We demonstrate ADOPT, a novel query engine that integrates adaptive query processing with a worst-case optimal join algorithm. ADOPT divides query execution into episodes, during which different attribute orders are invoked. With runtime feedback on performance of different attribute orders, ADOPT rapidly approaches near-optimal orders. Moreover, ADOPT uses a unique data structure which keeps track of the processed input data to prevent redundant work across different episodes. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments, ADOPT outperforms baselines, including commercial and open-source systems utilizing worst-case optimal join algorithms, particularly for complex queries that are difficult to optimize.

Dynamic Mission Planning Algorithm for UAV Formation in Battlefield Environment

The limited time duration and spatial instability of TSTs increase the difficulty of mission planning for unmanned aerial vehicle (UAV) formations, which in turn affects the task execution abilities of UAVs. Aiming at this problem, a multi-UAV dynamic mission planning algorithm based on the time window mechanism is proposed under a distributed formation structure. The proposed algorithm comprehensively considers a variety of complex constraints involved in actual application scenarios and constructs a UAV formation mission planning model with minimum task execution cost and path planning cost as the optimization goal. In this work, particle swarm optimization algorithm is improved by introducing a specific particle coding method and a competitive co-evolutionary population updating strategy, to enhance the solution speed and accuracy of the mission planning model. In this way, the optimal task allocation strategy and task execution path are obtained to meet the mission requirements, and to achieve the multi-UAV cooperative reconnaissance, strike and evaluation of multiple TSTs. Furthermore, the effectiveness of the proposed algorithm is validated via simulations.

Enabling Trust and Security in Digital Twin Management: A Blockchain-Based Approach with Ethereum and IPFS.

The emergence of Industry 5.0 has highlighted the significance of information usage, processing, and data analysis when maintaining physical assets. This has enabled the creation of the Digital Twin (DT). Information about an asset is generated and consumed during its entire life cycle. The main goal of DT is to connect and represent physical assets as close to reality as possible virtually. Unfortunately, the lack of security and trust among DT participants remains a problem as a result of data sharing. This issue cannot be resolved with a central authority when dealing with large organisations. Blockchain technology has been proposed as a solution for DT information sharing and security challenges. This paper proposes a Blockchain-based solution for digital twin using Ethereum blockchain with performance and cost analysis. This solution employs a smart contract for information management and access control for stakeholders of the digital twin, which is secure and tamper-proof. This implementation is based on Ethereum and IPFS. We use IPFS storage servers to store stakeholders' details and manage information. A real-world use-case of a production line of a smartphone, where a conveyor belt is used to carry different parts, is presented to demonstrate the proposed system. The performance evaluation of our proposed system shows that it is secure and achieves performance improvement when compared with other methods. The comparison of results with state-of-the-art methods showed that the proposed system consumed fewer resources in a transaction cost, with an 8% decrease. The execution cost increased by 10%, but the cost of ether was 93% less than the existing methods.

Knowledge-driven adaptive evolutionary multi-objective scheduling algorithm for cloud workflows

Workflow scheduling in cloud platforms is a highly challenging issue because it faces multiple conflicting optimization objectives and large-scale decision variables. Most of the existing multi-objective workflow scheduling algorithms regard the focused problems as black boxes, and optimize large-scale decision variables as a whole. This leads to inefficiency in searching solution spaces that grow exponentially with the increase of decision variables. To compensate the above deficiency, this paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm, KAMSA for short, to optimize makespan and cost of workflow execution in cloud platforms. Specifically, we excavate the knowledge that adjustment of a task’s execution only affects its successor tasks to divide large-scale decision variables into a series of groups, so as to give play to the strengths of divide-and-conquer technology to improve the evolutionary search efficiency. Moreover, we develop an adaptive resource allocation scheme to reward more evolution opportunities for groups with high contributions to further improve the evolutionary search efficiency. We compare the proposed KAMSA with five state-of-the-art competitors in the context of 20 real-world workflows and the Amazon elastic compute cloud (EC2). The comparison results verify the KAMSA’s advantages by prevailing over the five competitors on 18 out of the 20 test cases with respect to the metric hypervolume.

Experimental quantum natural gradient optimization in photonics.

Variational quantum algorithms (VQAs) combining the advantages of parameterized quantum circuits and classical optimizers, promise practical quantum applications in the noisy intermediate-scale quantum era. The performance of VQAs heavily depends on the optimization method. Compared with gradient-free and ordinary gradient descent methods, the quantum natural gradient (QNG), which mirrors the geometric structure of the parameter space, can achieve faster convergence and avoid local minima more easily, thereby reducing the cost of circuit executions. We utilized a fully programmable photonic chip to experimentally estimate the QNG in photonics for the first time, to the best of our knowledge. We obtained the dissociation curve of the He-H+ cation and achieved chemical accuracy, verifying the outperformance of QNG optimization on a photonic device. Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.

Secure and Privacy in Healthcare Data Using Quaternion-based Neural Network Cryptography with the Blockchain Mechanism

In the healthcare industry, security and privacy are major concerns. For effective diagnosis the medical practitioners need to access the healthcare data of the patient and similarly, the patient also needs to access the data. Sharing such a sensitive health data needs to be secured and privacy-preserving is ensured. Before uploading and downloading private health data, the data should be secured. To ensure security and privacy-preserving of health data stored in the cloud by implementing the improved Quaternion-based neural network with the blockchain mechanism. Improved Quaternion neural network cryptography is executed for encrypting the shared health data. Encrypted data’s secret key is stored by utilizing blockchain which is converted into blocks and the SHA algorithm is employed for identifying key events in the blocks which are stored in cloud storage. The modified genetic algorithm is used to generate a key which would be used for encryption and decryption. The authorized patient or physician can access the medical data by using the secret key to decrypt and download the medical data. The proposed network is evaluated in terms of time taken and cost and compared with the existing study. The results show that the time taken for the encryption and decryption process and the transaction and execution cost are reduced compared with the existing study.

FASTgres: Making Learned Query Optimizer Hinting Effective

The traditional and well-established cost-based query optimizer approach enumerates different execution plans for each query, assesses each plan with costs, and selects the plan that promises the lowest costs for execution. However, the optimal execution plan is not always selected. To steer the optimizer in the right direction, many query optimizers provide configuration parameters called query optimizer hints. These hints can be set for every single query separately. To show the great potential of these hints for the optimization of analytical queries, we present results of a comprehensive and in-depth evaluation using three benchmarks and two different versions of the open-source database system PostgreSQL. In particular, we highlight that query optimizer hinting is a non-trivial challenge. To solve this challenge, we propose FASTgres , a learning-based context-aware classification strategy for hint set prediction. Compared to related work, FASTgres provides transparent and direct hint set predictions with consistent performance improvements. In our end-to-end evaluation, we demonstrate that FASTgres effectively reduces benchmark runtimes by a factor of up to 3.25x with only steering the cost-based optimizer.

ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning

The performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. Selecting good orders before query execution is hard, due to the large space of possible orders and unreliable execution cost estimates in case of data skew or data correlation. We propose ADOPT, a query engine that combines adaptive query processing with a worst-case optimal join algorithm, which uses an order on the join attributes instead of a join order on relations. ADOPT divides query execution into episodes in which different attribute orders are tried. Based on run time feedback on attribute order performance, ADOPT converges quickly to near-optimal orders. It avoids redundant work across different orders via a novel data structure, keeping track of parts of the join input that have been successfully processed. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments with various data sets and queries, it outperforms baselines, including commercial and open-source systems using worst-case optimal join algorithms, whenever queries become complex and therefore difficult to optimize.

Распределенные реестры в интернет-торговле как ключевые технологии Индустрии 4.0 и объект правового регулирования

Introduction. The scientific paper traces the modern development of Internet space, which is characterized by the fact that in most spheres of public relations this space becomes indispensable. The technology of data processing and storage based on decentralized distributed registries (blockchain) is highly effective in commercial application. Unfortunately, in the Russian Federation, the technology of decentralized distributed registries is not given enough attention. The purpose of the study is to search for legal solutions for sustainable business development, ensuring its competitiveness, where there is a need for the mandatory use of digital technologies in the activities of legal entities. The authors set a goal to consider the problems of the legal framework governing this technology, as well as the relations that arise from remote purchase and sale transactions carried out by legal entities and individuals on the Internet. Methods. The methodological framework for the study is a set of methods of scientific cognition, among which the main ones are the methods of historicism, systematicity, analysis, and comparative law. Results. Based on the accumulated law enforcement experience and civil doctrine, the authors have proposed recommendations for resolving the most common conflict of laws issues arising from the use of blockchain technology and their regulation at the national and international level. The standards of electronic payments in online commerce at the international level are considered. Conclusions. As a result of the study, the role of digitally fixing the main stages of transactions with many commodities traditionally sold on exchanges, on the blockchain platform is revealed, which ultimately contributes to increasing transparency and security of operations. In this regard, blockchain solutions are of undoubted interest to participants in transactions with classic exchange-traded goods. The authors conclude that blockchain technologies reduce transaction execution costs and risks, contribute to the emergence of new business models and increase the efficiency of transactions.

A Fully Hybrid Algorithm for Deadline Constrained Workflow Scheduling in Clouds

With the migration of more and more workflows to clouds, the workflow scheduling in clouds (WSC) becomes a critical problem. Although many algorithms have been presented for WSC, there is still room and need for improvement. This paper formulates WSC as a constrained optimization problem that optimizes workflow execution cost within a workflow deadline constraint and proposes a fully hybrid workflow scheduling algorithm, called HPCP-PSO to solve it. Unlike previous works, HPCP-PSO is based on the repeated and alternated execution of two different methods, namely, the heuristic IaaS Cloud Partial Critical Paths (IC-PCP) and meta-heuristic Particle Swarm Optimization (PSO). Moreover, HPCP-PSO incorporates with two novel designs: 1) a new solution encoding strategy not only to sufficiently embody the elasticity of cloud resources, but also to reflect the scheduling relationship between assigned and unassigned tasks; 2) a solution repair strategy on each infeasible lease process to utilize a user-defined deadline more effectively and enhance the solution efficiency of the algorithm. Extensive experiments are conducted on four real-world scientific workflows and the results show that compared with IC-PCP, PSO, and HGSA, the proposed algorithm outperforms them on average by 35.83%, 70.53%, and 87.71% in terms of workflow execution cost.

SMART CONTRACT IMPLEMENTATION USING BLOCKCHAIN IOV FOR VEHICLE ACCIDENT INVESTIGATION

Recent advancements in digital accident forensics, a conceptual evidence management paradigm developed using smart contracts and interplanetary file system in iov. This paper comprehensively summarizes the Smart contract implementation blockchain framework for vehicle accident investigation in IoV. We investigate comparing some review papers to find the classification of the smart contract. Using blockchain, evidence management provides an immutable and auditable method for investigating and resolving accident cases. Precisely we first investigate the security and privacy threats; therefore, Smart contracts provide effective access control for proof data and reports. On both the public and private Ethereum blockchains, the cost of setting up and executing transactions using smart contracts is assessed. However, we utilized the Inter Planetary File System most efficiently while minimizing memory and execution costs. Finally, we draw open research directions for building future digital-proof management.

Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling

Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

TRANSACTION COSTS OF TRADING IN ORGANIZED STOCK MARKETS: STRUCTURE AND IMPLICATIONS FOR MARKET EFFICIENCY

The article substantiates the origin of transaction costs of stock trading from the point of view of the microstructure of the stock market. The article is devoted to the problem of improving the efficiency of decision-making in the field of portfolio investment, in particular in terms of reducing the cost of conducting operations. The object is the microstructure of the stock market, which allows to study in detail the process of making transactions with securities. The origin of transaction costs of stock trading was determined from the point of view of the microstructure of the market: as an appropriate fee for urgency and certainty in the purchase and sale process; as the premium for participation in market trading and conducting the exchange to stimulate potential counterparties; as the result of potential discrepancy in terms of information that the participants of exchange operations have. The classification of transaction costs is systematized according to various criteria: clarity of identification and assessment (explicit – commissions of intermediaries and the stock exchange, taxes, etc.and implicit – costs of spread, timeliness, market impact, missed opportunities); according to the reason for the various transaction costs (taxes, commissions, transaction execution costs and costs of missed opportunities). It is established that implicit costs add a significant level of uncertainty to the investment decision-making process, and therefore their evaluation and control is a priority issue for the investment entity. The main approaches to quantification of implicit transaction costs – missed opportunity costs, spread costs, and market impact costs-are summarized. Prospects for improving the tools for estimating transaction costs of operations in organized stock markets are determined.

Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.

The effects of factors on the motivations for knowledge sharing in online health communities: A benefit-cost perspective.

Online health communities (OHCs) provide knowledge for users, enabling conversations across a broad range of health topics. The development of OHCs depends on users' motivations to share health knowledge. Yet little literature has explored how perceived benefits and costs affect users' motivations for sharing both general and specific knowledge. Based on social exchange theory, we propose a research model that comprises intrinsic benefits (sense of self-worth, satisfaction), extrinsic benefits (social support, reputation, and online attention), cognitive cost, and executional cost to investigate the effects of these factors on users' motivations for general and specific knowledge sharing. We compare the different effects of these factors on users' motivations for knowledge sharing. Results demonstrate positive effects of intrinsic and extrinsic benefits on users' motivations for general and specific knowledge sharing. Differences exist in the negative effects of cognitive and executional costs on users' motivations for general and specific knowledge sharing. This study contributes to promoting the enrichment of online health knowledge and provides implications for the development of OHCs.

Portfolio liquidation with delayed information

This study investigates the liquidation of a portfolio when there is delayed information such as prices and trading signals. Its motivation stems from the calendar-time effect of information or the momentum effect on market prediction. When the stock price series is modeled by high-order vector autoregressive models, it converges to a system of stochastic delay differential equations in a continuous-time economy. The optimal solution indicates that the agent should trade gradually toward a path-dependent target portfolio to minimize execution costs. We numerically show a surprising novel pattern in which the agent with a liquidation mission sometimes purchases assets first to take advantage of the price predictability stemming from the delay effect. Moreover, optimally utilizing the delay effect has impacts on the time synchronization of trading in different assets and leads to significant cost reduction when the initial market trend is informative.

An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users’ jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.