Dynamic Scheduling for Data-Parallel Path Tracing of Large-Scale Instanced Scenes.

In heterogeneous environments, dynamic scheduling algorithms are a powerful tool towards performance improvement of scientific applications via load balancing. However, these scheduling techniques employ heuristics that require prior knowledge about workload via profiling resulting in higher overhead as problem sizes and number of processors increase. In addition, load imbalance may appear only at run-time, making profiling work tedious and sometimes even obsolete. Recently, the integration of dynamic loop scheduling algorithms into a number of scientific applications has been proven effective. This paper reports on performance improvements obtained by integrating the Adaptive Weighted Factoring, a recently proposed dynamic loop scheduling technique that addresses these concerns, into two scientific applications: computational field simulation on unstructured grids, and N-Body simulations. Reported experimental results confirm the benefits of using this methodology, and emphasize its high potential for future integration into other scientific applications that exhibit substantial performance degradation due to load imbalance.

Today, more and more distributed computer applications are being modeled and constructed using real‐time principles and concepts. In 1989, the Object Management Group (OMG) formed a Real‐Time Special Interest Group (RT SIG) with the goal of extending the Common Object Request Broker Architecture (CORBA) standard to include real‐time specifications. This group's most recent efforts have focused on the requirements of dynamic distributed real‐time systems.One open problem in this area is resource access synchronization for tasks employing dynamic priority scheduling. This paper presents two resource synchronization protocols that the authors have developed which meet the requirements of dynamic distributed real‐time systems as specified by Dynamic Scheduling Real‐Time CORBA (DSRT CORBA). The proposed protocols can be applied to both Earliest Deadline First (EDF) and Least Laxity First (LLF) dynamic scheduling algorithms, allow distributed nested critical sections, and avoid unnecessary runtime overhead. In order to evaluate the performance of the proposed protocols, we analyzed each protocol's schedulability. Since the schedulability of the system is affected by numerous system configuration parameters, we have designed simulation experiments to isolate and illustrate the impact of each individual system parameter. Simulation experiments show the proposed protocols have better performance than one would realize by applying a schema that utilizes dynamic priority ceiling update. Copyright © 2004 John Wiley & Sons, Ltd.

ABSTRACTThis article presents a dynamic algorithm for job scheduling in two-cluster tools producing multi-type wafers with flexible processing times. Flexible processing times mean that the actual times for processing wafers should be within given time intervals. The objective of the work is to minimize the completion time of the newly inserted wafer. To deal with this issue, a two-cluster tool is decomposed into three reduced single-cluster tools (RCTs) in a series based on a decomposition approach proposed in this article. For each single-cluster tool, a dynamic scheduling algorithm based on temporal constraints is developed to schedule the newly inserted wafer. Three experiments have been carried out to test the dynamic scheduling algorithm proposed, comparing with the results the ‘earliest starting time’ heuristic (EST) adopted in previous literature. The results show that the dynamic algorithm proposed in this article is effective and practical.

Since discrete manufacturing system (DMS) is a complicated dynamic network that comprises of processes, machines, and work in process, a coherent methodology for performance tracking and sustainable improvement at the system/network level is of great significance for manufacturers to respond rapidly in a mass customization paradigm. Fortunately, the radio frequency identification (RFID) technologies provide us the real-time tracking ability of the production process that suffers unpredictable and recessive disturbances. This paper proposes a dynamic scheduling approach based on multi-layer network metrics of RFID-driven DMS. Firstly, considering the elements of DMS (e.g., parts, manufacturing activities, and equipment) and relationships among them, a DMS model named complex manufacturing network (CMN) is proposed. Then, several multi-layer network metrics of the CMN are defined and analysed. The implications of these metrics lead to a better understanding of the current status and performance of DMS. Thirdly, a dynamic scheduling algorithm using these metrics as heuristic information is proposed to solve multi-resources and independent-task DMS. Finally, a Printing Machine manufacturing system is chosen as an example to illustrate the feasibility of the proposed approach.

Long‐term evolution (LTE) is a promising last mile access candidate technology for the smart‐metering communication architecture. However, when the mobile LTE network is used to support smart meters (SMs), the quality‐of‐service (QoS) requirements of the smart‐metering traffic as well as all the other typical mobile network traffic need to be ensured. This becomes problematic when the network users generate diverse traffic types that have different QoS requirements. Therefore, in this paper, we propose a dynamic bandwidth scheduling algorithm to ensure the required QoS of various traffic types arising from both SMs and mobile users. Our proposed dynamic bandwidth allocation algorithm integrates two schedulers that are designed for periodic and emergency SM traffic situations that have different SM traffic intensities and QoS requirements. Designing of two schedulers provides the advantages of leveraging the particular traffic characteristics of these two diverse operational situations and achieving the maximum use of resources to ensure QoS requirements. In addition, to alleviate potential problems created by simultaneous emergency SM traffic, we also propose a method that deploys a random delay for SM packet transmissions. We analyse the delay and packet drop ratio of diverse traffic types when both the LTE base station scheduler and the SMs deploy our proposed methods under either periodic or emergency SM traffic conditions in the smart grid. Our results show that our proposed mechanisms are capable of satisfying the QoS requirements of both mobile users and SMs under diverse traffic conditions.

This special issue is based on a collection of selected papers presented at the Fifth International Conference on Advanced Computing held in Chennai (Madras), India in December 97. The International Conference on Advanced Computing is organized annually in India by the Advanced Computing Society (ACS) which is a premier professional society set up in India to cater to the broad interests in the area of Advanced Computing. One of the significant activities of the Society is to organize regular Meetings/Conferences/Symposia. The Conferences are held annually in India during December. Out of the shortlisted papers presented at the Conference held in Chennai (Madras) during December 97, the following six papers were finally selected after a second round of detailed review of the papers. The first paper On designing and implementing collaborative system using Java-RMI by Rajeev R. Raje, Snehasis Mukhopadhyay, Michael Boyles and Nila Patel describe the use of Java-RMI in designing a collaborative information classifier. The principles of RMI, the details of the prototype, and some preliminary results are presented. The paper primarily concentrates on web-based computing paradigms. In the second paper A framework for performance-based program partitioning , Ram Subramanian and Santosh Pande present an efficient and accurate performance model based program-partitioning approach for parallel architectures. The interactions between computation and communication are captured through behavioral edges . The approach yields significant performance improvement. The third paper DDSCHED: a distributed dynamic real-time scheduling algorithm by Anup K. Bhattacharjee, K. Ravindranath, R. Mall and A. Pal presents a dynamic soft real-time scheduling algorithm for distributed systems. The algorithm is based on a local scheduler and a distributed scheduling scheme. The algorithm takes care of the network delays assuming that the communication protocol is CSMA/CD. In the fourth paper Generic operational decomposition for concurrent systems semantics and reflection , Annie Marcoux, Christine Maurel, Frederic Migeon and Patrick Salle discuss about the reflection in concurrent languages, particularly in actor-based languages. The main objective of the paper is to extend concurrent systems to reflection. The authors present a modular and dynamic description of entity operational management, and extend the decomposition to obtain reflective entities. The fifth paper Parallel programming using visual patterns by A. S. M. Sajeev proposes a two level approach to parallel programming within the context of a visual environment. The first level is the specification of patterns encapsulating process communation and synchronization, and the second level is the use of patterns for parallel programming. The approach encourages reuse of patterns. In the sixth paper An application framework for dynamic distributed software architectures , G. R. Ribeiro-Justo and P. R. F. Cunha present an object-oriented application framework for developing evolving (reconfigurable) software architectures for distributed systems. The application framework is called C++CL. Such a requirement is encountered in banking, industrial control and telecommunications applications. We are extremely thankful to the following reviewers who have helped us in reviewing the papers submitted for this special issue: Lawrence Jenkins N. Mohan Ram Nishit Narang Rajeev Shorey Sameer Adhikari K. Balaji K. Rajan Sumit Kasera Subhrajyothi Bhar Pankaj Sethi Ravindranath Kokku Guest Editors: L. M. Patnaik, Ravi Mittal and K. M. Mehta

A function model for the description of distributed end-to-end computations is called a task graph. Multiple functions with different criticality levels are supported by one electronic control unit (ECU), and one function is distributed over multiple ECUs in integrated automotive architecture. Considering the inherent heterogeneity, interaction, and diverse nature of such an architecture, automotive embedded systems have evolved to automotive cyber-physical systems (ACPS), which consist of multiple distributed automotive functions with different criticality levels. Efficient scheduling strategies can fully utilize ECUs in ACPS for high performance. However, ACPS should deal with joint challenges of heterogeneity, dynamics, parallelism, safety, and criticality, and these challenges are the key issues that will be solved in the next generation automotive open system architecture adaptive platform. This study first proposes a fairness-based dynamic scheduling algorithm FDS_MIMF to minimize the individual makespans (i.e., schedule lengths) of functions from a high performance perspective. FDS_MIMF can respond autonomously to the joint challenges of heterogeneity, dynamics, and parallelism of ACPS. To further respond autonomously to the joint challenges of heterogeneity, dynamics, parallelism, safety, and criticality of ACPS, we present an adaptive dynamic scheduling algorithm ADS_MIMF to achieve low deadline miss ratios (DMRs) of safety-critical functions from a timing constraint perspective while maintaining the acceptable overall makespan of ACPS from a high performance perspective. ADS_MIMF is implemented by changing up and down the criticality level of ACPS to adjust the execution of different functions on different criticality levels without increasing the time complexity. Experimental results indicate that FDS_MIMF can obtain short overall makespan, whereas ADS_MIMF can reduce the DMR values of high-criticality functions while still keeping satisfactory performance of ACPS.

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

Distributed method invocations require dynamic scheduling algorithms and efficient resource projections to provide timeliness guarantees to application objects. In this paper, we present a dynamic scheduling algorithm that examines the computation times, real times and resource requirements of the application tasks to determine a feasible schedule for the method invocations. The schedule is driven by the laxities of the tasks and the importance that the tasks have to the system. Tasks span processor boundaries, and request messages carry scheduling parameters (laxity values) from one processor to another, yielding a system-wide scheduling algorithm that requires only local computations. Experimental results validate our scheduling algorithm, and show that it has minimal overhead.

Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

With the rapid development of the Internet, in the face of the rapid increase of the number of visits and more complex transaction, the network server must have a very strong network computing processing power and functionality to provide a large number of concurrent access to services. The load balancing technology of linux virtual server will undoubtedly become an effective means to solve the bottleneck of the network server's performance. This paper mainly introduces the architecture and basic scheduling algorithm based on the LVS cluster, and also improve the existing load balancing algorithm and put forward a new load balancing algorithm which combines the static and dynamic scheduling algorithm. The simulation test results show that the algorithm has improves the comprehensive performance of the system to some extent and has a good adaption.

The rapid rise of microservice architecture poses severe challenges to workflow scheduling, resource allocation, and goal optimization. However, faults and failures usually happen during workflow running. To ensure the workflow's successful execution during scheduling microservices, this article proposes a dynamic workflow scheduling algorithm by integrating cognition cost and reliability QoS for using reinforcement learning (WS-CCR). First, we explore the 'restart policy' of containers in the Kubernetes architecture, which lays the foundation for our work that adopts the redundancy strategy to ensure workflow operation. Then we consider the cognitive cost based on the fact that users have a cognitive process for different microservices in selecting microservices. Additionally, another optimization goal is the reliability of workflows. On this basis, we design a reasonable reward function in reinforcement learning to generate dynamic strategies. Furthermore, following some generated strategies, our engine will schedule candidate microservices for tasks to execute step by step. A series of experiments on Alibaba and business areas workflows have proven the superior performance of our algorithm. Our WS-CCR can generate better Pareto solution sets than other baselines in terms of improving the reliability of running workflows. Finally, we give a case study to prove the practicability of our method.

Scheduling plays a significant role in improving observation effectiveness of imaging satellites.Although extensive satellite scheduling algorithms have been proposed, none of them focuses on emergency scheduling in dynamic environment.In this paper, a novel dynamic emergency scheduling model of multiple imaging satellites is established.To improve user's satisfaction ratio and resource utilization, we propose a novel task dynamic merging strategy, developing an alternative task set establishment (ATSE) algorithm used for dynamic merging.Besides, a novel dynamic emergency scheduling algorithm called DM-DES is presented, which considers the dynamic merging.To demonstrate the superiority of our DM-DES, we conduct extensive experiments by simulations to compare DM-DES with an existing algorithm--RBHA as well as a baseline algorithm--DES.The experimental results indicate that DM-DES improves the scheduling quality of others and is suitable for emergency scheduling.

Intelligent manufacturing plays a significant role in Industry 4.0. Dynamic shop scheduling is a key problem and hot research topic in the intelligent manufacturing systems, which is NP-hard. However, traditional shop scheduling mode, dynamic event prediction approach, scheduling model and scheduling algorithm, cannot cope with increasingly complicated problems under kinds of scales production disruptions in the real-world production. To deal with these problems, this paper proposes a new joint data-model driven dynamic scheduling architecture for intelligent workshop. The architecture includes four new and key characteristics in the aspects of scheduling mode, dynamic event prediction, scheduling model and algorithm. More specifically, the new scheduling mode introduces data analytics methods to quickly and accurately deal with the dynamic events encountered in the production process. The new prediction model improves the deep learning method, and further applies it predict the dynamic events accurately to provide reliable input to the dynamic scheduling. The new scheduling model proposes a new hybrid rescheduling and inverse scheduling model, which can cope with almost scales of abnormal production problems. The new scheduling algorithm hybridizes dynamic programming and intelligent optimization algorithm, which can overcome the disadvantages of the two methods based on the analysis of solution space. The dynamic programming is employed to provide high-quality initial solutions for the intelligent optimization algorithm by reducing the computation time greatly. To sum up, the presented architecture is a new attempt to understand the problem domain knowledge and broaden the solving idea, which can also provide new theories and technologies to manufacturing system optimization and promote the applications of the theoretical results.