Execution Cost Research Articles

Making motion matching stable and fast with Lipschitz-continuous neural networks and Sparse Mixture of Experts

Motion matching has become a widely adopted technique for generating high-quality interactive animation systems in video games. However, its current implementations suffer from significant computational and memory resource overheads, limiting its scalability in the context of modern video game performance profiles.Our method significantly reduces the computational complexity of approaches to motion synthesis, such as “Learned Motion Matching”, while simultaneously improving the compactness of the data that can be stored and the robustness of pose output. As a result, our method enables the efficient execution of motion matching that significantly outperforms other implementations, by 8.5× times in CPU execution cost and at 80% of the memory requirements of “Learned Motion Matching”, on contemporary video game hardware, thereby enhancing its practical applicability and scalability in the gaming industry and unlocking the ability to apply on large numbers of animated in-game characters.In this paper, we expand upon our published paper “Learning Robust and Scalable Motion with Lipschitz Continuity and Sparse Mixture of Experts”, where we successfully proposed a novel method for learning motion matching that combines a Sparse Mixture of Experts model architecture and a Lipschitz-continuous latent space for representation of poses. We present further details on our method, with extensions to our approach to expert utilization within our neural networks.

Framework for Agent-Based Multistage Application Partitioning Algorithm in Mobile Cloud Computing

The idea of computational offloading is quickly catching on in the world of mobile cloud computing (MCC). Today’s applications have heavy demands on power and computing resources, creating issues with energy consumption, storage capacity, and mobile device performance. Mobile devices may efficiently offload their calculations to cloud servers using the offloading paradigm and then get the processed results back onto the device. The investigation relates to identifying the specific application components that should be offloaded and run remotely and which parts are supposed to be treated locally. The applications need to be partitioned to differentiate between remote and local codes. In this paper, an agent-based multistage graph partitioning (ABMP) scheme is proposed. The framework of the scheme is based on three-tier architecture that includes mobile, cloudlet, and cloud for the execution of application tasks. The main goal is to provide an efficient partitioning and offloading scheme in the mobile cloud computing area. The results show that incorporating both agent-based multistage graph partitioning and offloading algorithms yields superior performance as compared to previous methods in terms of reducing execution costs and conserving battery life for mobile devices.

Optimal liquidation using extended trading close for multiple trading days

The extended trading close (ETC) provides institutional investors an opportunity to trade at the closing price after the regular trading session (RTS) and disclosing the order imbalances to other market participants. ETCs exist in the Nasdaq, the SSE STAR, the SZSE ChiNext and the TWSE. To help a risk-averse institutional investor take advantage of the RTS and the ETC for liquidation, we develop a multistage dynamic programming model including the ETC, and derive recursive solutions for the multiple trading days scenario with closed-form solutions for the scenario with only two trading days. We also verify that the ETC is able to mitigate extreme price movements caused by fast liquidation, which is also a goal of the ETC set out by the SSE STAR and the SZSE ChiNext. Finally, we derive three results. First, an institutional investor can reduce execution costs after the introduction of the ETC. Second, a critical trading day exists, and to avoid prematurely revealing trading intentions, the investor should not trade in the ETC until such day. Third, even though the ETC orders submitted by the investor are unfilled, implementation of the ETC encourages the investor to change the liquidation strategy in the RTS, which reduces extreme price movements. In summary, the practical implications of this paper are that the investor should not trade during the ETC on the front few days to avoid prematurely revealing the investor’s trading intention by unfilled orders in the ETC and that introducing the ETC can reduce liquidation costs and extreme price movements.

Multi-objective and multi constrained task scheduling framework for computational grids

Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.

DataMesh+: A Blockchain-Powered Peer-to-Peer Data Exchange Model for Self-Sovereign Data Marketplaces.

In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model's acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance.

A communication link lifetime prediction-supported V2V partial computation offloading scheme for autonomous driving

Vehicle Edge Computing (VEC) is a promising technique to improve the quality of service (QoS) and quality of experience (QoE) in autonomous driving by exploiting the resources at the network edge. However, the high mobility of the vehicles leads to stochastic communication link duration, and the tasks generated by various applications in autonomous driving incur fierce competition for resources. These challenges cause excessive task completion delays. In this paper, we propose a vehicle-to-vehicle (V2V) partial computation offloading scheme that leverages the prediction results of the communication link lifetime between vehicles. A History track, Current interactions and Future planning trajectory-aware Gated Recurrent Units (HCF-GRU) network is built to capture the essential factors to improve the prediction accuracy. Then, we design a GRU-based Proximal Policy Optimization (GRU-PPO) algorithm to obtain an optimal one-to-many offloading decision to minimize the task execution cost. The HCF-GRU prediction algorithm is evaluated on a real world vehicle trajectory dataset, and the performance of the GRU-PPO algorithm is analyzed on extensive numerical simulations. Experimental results demonstrate that our prediction network and offloading decision algorithm outperform the baseline methods in terms of prediction accuracy and task execution cost.

Overlaying Control Flow Graphs on P4 Syntax Trees with Gremlin

Our overall research aim is to statically derive execution cost and other metrics from program code written in the P4 programming language. For this purpose, we extract a detailed control flow graph (CFG) from the code, that can be turned into a full, formal model of execution, to extract properties -- such as execution cost -- from the model. While CFG extraction and analysis is well researched area, details are dependent on code representation and therefore application of textbook algorithms (often defined over unstructured code listings) to real programming languages is often non-trivial. Our aim is to present an algorithm for CFG extraction over P4 abstract syntax trees (AST). During the extraction we create direct links between nodes of the CFG and the P4 AST: this way we can access all information in the P4 AST during CFG traversal. We are utilizing Gremlin, a graph query language to take advantage of graph databases, but also for compactness and to formally prove algorithm correctness.

Task level energy and performance assurance workload scheduling model in distributed computing environment

Scientific workload execution on distributed computing platform such as cloud environment is time intense and expensive. The scientific workload has task dependencies with different service level agreement (SLA) prerequisite at different levels. Existing workload scheduling (WS) design are not efficient in assuring SLA at task level. Alongside, induce higher cost as majority of scheduling mechanisms reduce either time or energy. In reducing, cost both energy and makespan must be optimized together for allocating resource. No prior work has considered optimizing energy and processing time together in meeting task level SLA requirement. This paper present task level energy and performance assurance (TLEPA)-WS algorithm for distributed computing environment. The TLEPA-WS guarantees energy minimization with performance requirement of parallel application under distributed computational environment. Experiment results shows significant reduction in using energy and makespan; thereby reduces cost of workload execution in comparison with various standard workload execution models.

Enhancement in the safety and reliability of Pressurized Water reactors using Machine Learning approach

The increasing interest in artificial intelligence and automation within the nuclear industry stems from the hope of elevating the safety and reliability of nuclear power plants by minimizing the impact of human errors. This paper explores the capabilities of machine-learning based fault detection and diagnosis (FDD) models in accurately classifying transient events in a nuclear power plant using the Classification Learners Application from MATLAB. The scope of the research is narrowed to the identification of transient events of the same nature occurring at various locations within the plant. To accomplish this, the Generic Pressurized Water Reactor (GPWR) from the Western Services Cooperation (WSC) was used to simulate 14 transients in the primary system and 10 transients in the secondary system, resulting in two distinct datasets of 108,000 and 79,200 observations. Nine types of classifiers, including Decision Trees, Discriminant Analysis, Support Vector Machines, Logistic Regression, Nearest Neighbors, Naive Bayes, Kernel Approximation, Ensembles, and Neural Networks, with a total of 33 predefined models, were trained and validated. The K-Nearest Neighbors model achieved the highest accuracy of 97% in the primary system, while the Efficient Linear Discriminant and Logistic Regression models achieved the highest average accuracy of 99% in the secondary system. The assessment of the top-performing models was conducted through a comprehensive analysis employing key classification model evaluation metrics, such as the confusion matrix, accuracy, precision, recall, and the F1 score. In an effort to optimize the models, the study integrated feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm. Additionally, the investigation explored different validation schemes, including both holdout and k-fold cross-validation schemes, resulting in a substantial reduction in training time without significantly compromising the overall accuracy of the models. The optimized models in this study demonstrated remarkable prediction accuracies, consistently exceeding 94%. Despite the complexity of the models and the intricacies involved in the training process, the training times ranged from 10 to 1800 s, reflecting the efficiency and robustness of the implemented optimization techniques. The results of this study underscore the potential of machine learning models in identifying transient events in nuclear reactors, showcasing their promising capabilities while maintaining low computational and execution costs. This suggests their efficacy in optimizing safe and efficient operational practices within nuclear facilities.

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.

Efficient Top-k Frequent Itemset Mining on Massive Data

Top-k frequent itemset mining (top-k FIM) plays an important role in many practical applications. It reports the k itemsets with the highest supports. Rather than the subtle minimum support threshold specified in FIM, top-k FIM only needs the more understandable parameter of the result number. The existing algorithms require at least two passes of scan on the table, and incur high execution cost on massive data. This paper develops a prefix-partitioning-based PTF algorithm to mine top-k frequent itemsets efficiently, where each prefix-based partition keeps the transactions sharing the same prefix item. PTF can skip most of the partitions directly which cannot generate any top-k frequent itemsets. Vertical mining is developed to process the partitions of vertical representation with the high-support-first principle, and only a small fraction of the items are involved in the processing of the partitions. Two improvements are proposed to reduce execution cost further. Hybrid vertical storage mode maintains the prefix-based partitions adaptively and the candidate pruning reduces the number of the explored candidates. The extensive experimental results show that, on massive data, PTF can achieve up to 1348.53 times speedup ratio and involve up to 355.31 times less I/O cost compared with the state-of-the-art algorithms.

ST-HO: Symmetry-Enhanced Energy-Efficient DAG Task Offloading Algorithm in Intelligent Transport System

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered.

Optimal trade execution in cryptocurrency markets

Novel technologies allow cryptocurrency exchanges to offer innovative services that set them apart from other exchanges. In this paper we study the distinct features of cryptocurrency fee schedules and the implications for optimal trade execution. We formulate an optimal execution strategy that minimizes the trading fees charged by the exchange. We further provide a proof for the existence of an optimal execution strategy for this type of fee schedule. The optimal strategy involves both market and limit orders on various price levels. The optimal order distribution scheme depends on the market conditions expressed in terms of the distribution of limit order execution probabilities and the exchange’s specific configuration of the fee schedule. Our results indicate that a strategy kernel with an exponentially decaying allocation of trade volume to price levels further away from the best price provides superior performance and potential reduction of trade execution cost of more than 60%. The robustness of these results is confirmed in an empirical study. To our knowledge, this is the first study of optimal trade execution that takes into consideration the full fee schedule of exchanges in general.

Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.

A predictive energy-aware scheduling strategy for scientific workflows in fog computing

Fog computing paradigm provides diverse processing resources and storage close to the edge of Internet of Things (IoT) networks. Workflow scheduling is an open issue in fog computing as it addresses the deployment of IoT workflow tasks on fog cells. Deploying workflow tasks on fog cells in an inefficient manner can have negative consequences. It can lead to bandwidth waste, resource depletion, and significant operating expenses. Additionally, the majority of scheduling evolutionary algorithms suffer from premature convergence and sticking in local optima. In this paper, a predictive energy-aware scheduling framework is proposed as a MAPE-K control model consisting of the four Monitor, Analyzer, Planner and Executer components with a shared Knowledge base in fog computing. First, a prediction method applying an Adaptive Network-based Fuzzy Inference System (ANFIS) into an analyzer component is introduced to predict future resource load. Second, a resource management strategy based on predicted resource load is presented to reduce energy consumption. Third, the Improved Ant Lion Optimizer (ALO) and weighted Grey Wolf Optimizer (GWO) are combined into a planner component called I-ALO-GWO for workflow scheduling. In the end, the decisions made in the previous steps are executed on the fog cells. The effectiveness of the I-ALO-GWO evolutionary algorithm has been tested on the IEEE CEC2019 benchmark functions. In addition, the proposed approach is evaluated practically under several famous scientific workflows using the iFogSim tool. Experimental results indicate that I-ALO-GWO improves the makespan up to 18 %, the energy consumption up to 17 %, the total execution cost up to 11 % and 26 % in terms of efficiency in comparison with the second-best results.

Hierarchical Pruning of Deep Ensembles with Focal Diversity

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study and apply deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This article presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal ensemble diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble team, which can guide ensemble pruning. Second, we design a focal ensemble diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high ensemble execution efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better classification generalizability while being more time and space efficient in ensemble decision making. We have released the source codes on GitHub at https://github.com/git-disl/HQ-Ensemble .

Delta4Ms: Improving mutation‐based fault localization by eliminating mutant bias

AbstractFault localization is a complex, costly and time‐consuming task in software debugging. Numerous automated techniques have been developed to expedite this process. Mutation‐based fault localization (MBFL) is one of the most widely studied techniques which uses mutation analysis to generate mutants for revealing potential faults in the program. However, our theoretical analysis exposes an inherent conflict between the fundamental assumption and the essential meaning of existing MBFL suspiciousness. This conflict is caused by mutant bias. Intuitively, the suspiciousness can be corrected by eliminating the mutant bias for more accurately measuring the faulty probability of the corresponding mutant statement. In this paper, we introduce Delta4Ms, a fault localization approach designed to eliminate mutant bias. Delta4Ms integrates the principles of signal theory, modelling the actual suspiciousness and mutant bias as the desired and false signal components, respectively. Based on theoretical derivation, the average suspiciousness of mutants serves as an estimate of mutant bias. Delta4Ms effectively mitigates mutant bias, extracting the desired signal and yielding corrected suspiciousness for fault localization. To precisely estimate mutant bias, higher order mutants (HOMs) are incorporated. We conduct an extensive experimental evaluation of Delta4Ms on 320 real‐fault programs from Codeflaws. The results indicate that our model significantly outperforms existing SBFL and MBFL techniques, showing a considerable improvement in fault localization effectiveness. We further assessed the robustness of Delta4Ms by examining different HOM ratios and HOM generation strategies. Moreover, Delta4Ms achieves a substantial reduction in mutation execution cost and minimal accuracy loss through the implementation of test case reduction. Finally, we perform preliminary experiments on 15 real‐fault programs from the Defects4J benchmark to assess the generalization of the model's fault localization effectiveness.

Uncertainties estimation in surveying measurands: application to volumes

Volume represents a measurand of great interest in civil engineering and construction works. The estimation of this measurand is a problem already solved by surveying engineering, but the quantification of its uncertainty has been overlooked. As a result, the inaccurate estimation of the volume can lead to significant deviations in the execution costs of earthworks. Moreover, it is not possible to comply with the internationally accepted requirements concerning the expression of measures with an indication of its uncertainty, i.e. the guidelines of the Bureau International des Poids et Mesures. In this context, this paper presents a methodology for the quantification of uncertainty in the surveying measurement of volumes, which is generally carried out through digital terrain models processed by computer-aided design or specific surveying software. Two methods for volume estimation are presented and the variance–covariance propagation laws are applied to each of them, leading to the computation of volume uncertainty from measures of position coordinates for which uncertainties are known. Then, the developed methods for uncertainty estimation are successfully tested in different scenarios. The conceptual and mathematical developments for the uncertainty quantification in the computation of volumes resulted in closed-form algorithms implemented in MATLAB that can be potentially incorporated into commercial surveying software.

Timed-release encryption anonymous interaction protocol based on smart contract

Timed-release encryption (TRE) is a cryptographic primitive that can control the decryption time and has significant application value in time-sensitive scenarios. To solve the reliability issue of nodes in existing TRE anonymous interaction schemes, we propose a blockchain-based TRE protocol for anonymous query time trapdoors. In our protocol, the recipient divides the encrypted trapdoor request information into n ciphertext fragments using secret sharing technology near the decryption time, and employs the idea of onion routing to perform layer-by-layer encryption, creating onion-type data transmitted through middlemen selected from the smart contract. After receiving the ciphertext fragments, the time server integrates them to obtain the trapdoor request information and returns the corresponding time trapdoor to the recipient. This allows the recipient to query any time trapdoor anonymously. Our protocol provides a normative design for the smart contract and specific constraints on the participants’ behavior. Compared with the related anonymous query trapdoor schemes, our protocol improves the probability of successful queries. Security analysis shows that our protocol can resist release-ahead attack, interruption attack, eavesdropping attack, and replacement attack. Performance analysis shows that our protocol outperforms related protocols regarding anonymity, efficiency, and flexibility, achieving highly efficient anonymous interactions. Finally, we conducted an experiment in the Ethereum Rinkeby test network. For the settings of ciphertext fragment number n=3 and ciphertext fragment threshold t=2, the gas consumption for a user to execute the contract was $5.66, which was higher than the contract cost of related schemes, but the contract execution cost was within an acceptable range.

Design of computer algorithms based on parametric models

Abstract In this paper, the objective function is effectively optimized by improving the fitness function in the computer algorithm. The improvement mainly focuses on adjusting the weighting coefficients of completion time, load balance and execution cost. The article then proceeds to optimize the algorithm’s parameters based on the algorithm’s optimal parameterization criterion, and designs a hybrid hill-climbing-simulated annealing optimization algorithm based on the parameterized model. To verify the safety of the algorithm, avalanche effect experiments were conducted in the study. The experimental results show that when the number of iterations is 14, the number of changed bits reaches 32, indicating that the algorithm is susceptible to the avalanche effect. In addition, for the application effect of the algorithm, the study was tested in the Oliver30 dataset. When the number of iterations reaches 25000 times, the algorithm has reached the optimal value distance 432.82, which shows the superiority in urban path recognition performance. Meanwhile, the algorithm has achieved an accuracy 0.8286 for feature recognition in remote sensing datasets, with a maximum classification accuracy of more than 90%. The study provides critical practical data and theoretical support for further application of the computer algorithm.