Towards the Robustness of Dynamic Loop Scheduling on Large-Scale Heterogeneous Distributed Systems

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.

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, simulative experiments are more appropriate for studying the performance of applications. This motivates the question of How realistic are the simulations of executions of scientific applications using DLS on HPC platforms? In the present work, a methodology is devised to answer this question. It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC plat- forms, both via native and simulative experiments. The evaluation and analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the approach used to extract the computational effort of the application (FLOP- or time-based), the choice of application model representation into simulation (data or task parallel), and the available HPC subsystem models in the simulator (multi-core CPUs, memory hierarchy, and network topology). The minimum and the maximum percent errors achieved between the native and the simulative experiments are 0.95% and 8.03%, respectively.

An important research area in statistical computing is found in the literature for vector functional coefficient autoregressive models, a special case of vector nonlinear time series. Methods used are computationally intensive. As a result, analyses and simulations can run into weeks, or even months. Statisticians have been known to base empirical results on a relatively small number of simulation replications, sacrificing precision, accuracy and reliability of results in the interest of time and productivity. The simulations are amenable for parallelization; however, parallel computing technology has not yet been widely used in this specific research area. This paper proposes an approach to the parallelization of statistical simulation codes to address the challenge of long running times, without resorting to extensive code revisions. This approach takes advantage of recent advances in dynamic loop scheduling on workstation clusters to achieve high performance, even with the presence of unpredictable load imbalance factors. Preliminary results of applying this approach in the simulation of normal white noise and threshold autoregressive model obtains efficiencies in the range 95–98% on 8–64 processors.

Enhanced wireless communication improves the connectivity of vehicular networks in which vehicles are utilized as infrastructures for communication and computation. Thus, a new concept “Vehicular Edge Computing (VEC)” is formed. As VEC utilizes a collaborative multitude of near-user edge resources (i.e. vehicles) in the Internet of Vehicles, the capability of these joint resources becomes heterogeneous especially in their movements. Therefore, one critical problem is how to efficiently schedule each task under such mobile environments. For the reason, we propose a hybrid dynamic scheduling scheme (HDSS) that has the ability to optimize the task scheduling dynamically based on the changeable system environments. HDSS provides a decision function (DF) to select a better-performed scheduling algorithm from two provided candidates: the queue-based dynamic scheduling (QDS) algorithm and the time-based dynamic scheduling (TDS). QDS coincides with the Join-the-Shortest Queue scheme, which decides the scheduling by sorting out a server with the shortest queue-length; nevertheless, TDS is novel scheme that is designed to implement task allocation by estimating the waiting time of each server in order to select a server with the fastest response. Finally, this research generates formal models of each scheduling algorithm and the hybrid scheduling scheme to conduct performance evaluation with a fluid flow approximation technique. The analysis results in a superior performance of HDSS in the unstable VEC environments.

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.

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

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.

In this paper we investigate a new algorithm for the feedback-guided dynamic loop scheduling (FGDLS) method in the discrete case. The method uses a feedback-guided mechanism to schedule a parallel loop within a sequential outer loop. The execution times and the scheduling bounds for the current outer iteration are used to find the scheduling bounds of the next outer iteration. An O(p+log p) algorithm has been proposed for the discrete case where it was proved to achieve optimal bounds in only a few iterations. This articles introduces an O(log p) algorithm for the discrete case and presents some properties of it.

In a heterogeneous system, processor and network failure are inevitable and can have adverse effect on the complex applications executing on the systems. To reduce the rate of these failures, matching and scheduling algorithms should take into account the objectives of minimizing schedule length (makespan) and reducing the probability of failure. Equitable distribution of workload over resources contributes in reducing the probability of failure. The Heterogeneous Earliest Finish Time (HEFT) algorithm has been proved as a performance effective task scheduling algorithm, addressing the objective of minimizing makespan. Reliable Dynamic Level Scheduling (RDLS) algorithm is a bi-objective scheduling algorithm that maximizes the reliability more effectively. Though the reliable version of HEFT algorithm (RHEFT) considers failure rate in scheduling decision, the improvement in reliability is less, compared to that of RDLS. To overcome this deficiency, we propose to incorporate the task–processor pair finding step of RDLS in HEFT algorithm, since it meets both the objectives of minimizing the makespan and maximizing the reliability. We define the load on a processor as the amount of time the processor is engaged in completing the scheduled subtasks. In this paper, a modification to the HEFT is proposed as a new algorithm called Improved Reliable HEFT (IRHEFT) for minimizing the schedule length, balancing the load and maximizing the reliability of schedule. The algorithm is compared for its performance with RDLS algorithm for randomly generated task graphs and a real application task graph.

With the applications of Internet of Things (IoT) for smart cities, the real-time performance for a large number of network packets is facing serious challenge. Thus, how to improve the emergency response has become a critical issue. However, traditional packet scheduling algorithms cannot meet the requirements of the large-scale IoT system for smart cities. To address this shortcoming, this paper proposes EARS, an efficient data-emergency-aware packet scheduling scheme for smart cities. EARS describes the packet emergency information with the packet priority and deadline. Each source node informs the destination node of the packet emergency information before sending the packets. The destination node determines the packet scheduling sequence and processing sequence according to emergency information. Moreover, this paper compares EARS with a first-come, first-served, multilevel queue algorithm and a dynamic multilevel priority packet scheduling algorithm. Simulation results show that EARS outperforms these previous scheduling algorithms in terms of packet loss rate, average packet waiting time, and average packet end-to-end delay.

In this paper classic static and dynamic scheduling strategy is analyzed first, and then communication network of schedule ability judgment basis is given. An improved dynamic EDF scheduling algorithm is proposed in order to improve the scheduling task of real-time. The scheduling strategy is to change task priority according to the transmission error over deadline task when applying dynamic EDF scheduling strategy. True Time tool is used to build CAN network control system simulation platform. Dynamic EDF scheduling algorithm and improved scheduling algorithm are simulated respectively. The effectiveness of improved scheduling algorithm is verified by the simulation Keywords-Network control system; Scheduling Algorithm; True Time toolbox result.

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.

As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput.

A heuristic minimal path sets method for analyzing seismic reliability of lifeline networks is proposed. For large-scale lifeline networks reliability computation can become prohibitive or inaccurate using the current enumeration methods. Although a few studies have considered both node and link failures, none of these methods has utilized the minimal paths method. This paper proposes a minimal path algorithm to evaluate reliability of relatively large lifeline networks with unreliable nodes and links. As the primary task, the method can deduce all minimal paths of a directed/undirected network. In the algorithm, minimal paths for both nodes and links are traced performing several procedures. The terminal-pair reliability of the network is then evaluated by performing an efficient Monte Carlo simulation as an alternative to exact reliability calculation methods. In terms of computational speed, the results obtained compare well with existing algorithms. The algorithm requires minimum memory storage and minimum user-defined data to represent the topology of a network. A series of case studies illustrate that the method can be used to evaluate reliability of the large-scale lifeline systems subject to node and link failures, efficiently.

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.