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This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature. [ABSTRACT FROM AUTHOR]

The display module process is characterized by having to deal with a wide range of customers and product groups with a short turnaround time (TAT). In this study, a new concept of simulation tool reflecting the characteristic of the module process was developed. This tool reflects the TAT of the equipment using the cumulative distribution function of the process time, and the frequency and time of down of the equipment are modeled using statistical techniques. This tool has confirmed a high accuracy of 92% in comparison with actual production. The tool provides rule‐based operation guidance to manufacturing engineers. However, rule‐based operation is not easy to adequately respond to all manufacturing situations in the actual field. Thus, reinforcement learning (RL) was applied to the module scheduler system for optimal operation. We applied RL algorithm such as deep Q‐Networks (DQN), double DQN, dueling network. Through a test comparing reinforcement learning‐based operations and rule‐based operations under the same conditions, it was found that reinforcement learning‐based operations are better in terms of productivity and efficiency than rule‐based operations. This result shows that the paradigm of module scheduling has shifted from traditional rule‐based operations to reinforcement learning‐based AI operations. [ABSTRACT FROM AUTHOR]

This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler's prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler's adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks. [ABSTRACT FROM AUTHOR]

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms.

Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task's quality standards. Because this scheduling involves a Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm's exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance. [ABSTRACT FROM AUTHOR]

Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system's inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty. [ABSTRACT FROM AUTHOR]

The scheduler has been extensively studied from the point of view of their schedules, using operations research. However, the scheduler not only fulfils a decision-maker role but also an informational role, responding to requests and disruptions, both from the supply and the demand side. Responding in a timely manner to such requests and disruptions is paramount for the scheduling job. Using a field study with job shadowing of schedulers in a Fortune 500 chemical company, we characterise the scheduler's workflow in a simulation model; this allows us to discriminate between contextual factors of the scheduler's job and behavioural factors inherent to them. Furthermore, it explicitly assesses the effect of increasing the frequency sensing of the outer world on responsiveness. Our findings show that the studied schedulers prioritise sensing activities related to checking emails rather than monitoring for disruptions in a decision support system. Thus, we find a higher potential for relative improvement in terms of responding to possible disruptions shown in decision support systems rather than responding to email requests. Moreover, we show that by adjusting email frequency checking and preemption behaviours, it is possible to revert an out of control situation to finite response times. [ABSTRACT FROM AUTHOR]

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building. [ABSTRACT FROM AUTHOR]

In case of an emergency situation, it is required to come up with solutions quickly. The supporting decision-making process should therefore be based on relevant data sources which are fed to data processing pipelines. These data sources may however be located in different domains of distinct organizations. Although the technical realisation of cross-organizational data pipelines is possible, for example using a federated Kubernetes cluster managed by a central operator, it is unclear how those pipelines should be scheduled among the participating organizations. Firstly, the cross-organizational infrastructure is unknown and highly heterogeneous, and secondly, there may be undiscovered scheduling preferences present. The first issue can be solved using software probes, while the second issue will be solved through an extension of this probing concept. During the proposed inter-domain scheduling process, organizations may specify monetary reward requirements and requirements on the maximum load they wish to bear. The former allows administrators to specify a lower limit with respect to payment they request for their contribution, the latter allows the specification of an upper limit on the workload an organization wishes to process. This way, mismatching expectations on contribution level, which may potentially cause harm from the hosting organization perspective, are prevented, increasing the trust level of contributors. These kinds of requirements have nothing to do with the technical assessment of a node, but they do impact scheduling decisions and performance of an application, as shown in our evaluation. The proposed scheduling flow not only allows organizations to steer scheduling decisions, but also to negotiate requirements among each other, giving rise to ad hoc conflict resolution all the while collaborating in solving the emergency situation. [ABSTRACT FROM AUTHOR]

We consider guaranteeing end-to-end (E2E) latency bounds to flows in a network. It is desirable that flows are isolated from other flows. The bursts from other flows or the network utilization level should not affect a flow’s latency bound. The fair queuing technique, which includes Packetized Generalized Processor Sharing (PGPS) and Virtual Clock (VC), is based on the concept of ideal packet service completion time called the Finish Time (FT). The fair queuing is known to provide the near perfect flow isolation but has to maintain the flow states. Alternative schemes were suggested, in which the entrance node in a network generates FT for a packet and records it in the packet with other necessary information. Subsequent nodes, based on these records, decide the service eligibility and service order of the packets. A packet is served only when it is eligible, thus the system is non-work conserving. In this paper, a simpler framework for deriving such FTs in core nodes without flow state is presented, in which initial FT is updated by adding a delay factor per node, which is a function of parameters of nodes and flow. The proposed scheduler is work conserving and has the property that, for a certain choice of the delay factor, the expression for E2E latency bound can be found. This E2E latency bound function is the same as that of a network with stateful fair queuing schedulers in all the nodes. Moreover, utilizing the fact that the service order of packets passing through the same path can be unaltered in the middle of the path, we also present a FIFO-based architecture whose performance is similar to that of a priority queue-based architecture. The extensive simulations prove that the proposed framework shows an ideal flow isolation performance over a wide range of delay factor values, with superior scalability.

The verification of reachability properties of fuzzy systems is usually based on the fuzzy Kripke structure or possibilistic Kripke structure. However, fuzzy Kripke structure or possibilistic Kripke structure is not enough to describe nondeterministic and concurrent fuzzy systems in real life. In this paper, firstly, we propose the generalized possibilistic decision process as the model of nondeterministic and concurrent fuzzy systems, and deduce the possibilities of sets of paths of the generalized possibilistic decision process relying on defining of schedulers. Then, we give fuzzy matrices calculation methods of the maximal possibilities and the minimal possibilities of eventual reachability, always reachability, constrained reachability, repeated reachability and persistent reachability. Finally, we propose a model checking approach to convert the verification of safety property into the analysis of reachabilities. [ABSTRACT FROM AUTHOR]

Machine learning profoundly impacts every aspect of our lives. As machine learning evolves, many techniques, such as deep learning, are improving its accuracy and performance. Nonetheless, large data computations with large memory footprints will always be a bottleneck for deep learning applications. One of the most computationally demanding DNN operations is matrix multiplication, such as the convolution layer and fully connected layer, which preserve the image arrangement and obtain a partial image as an input feature. Our goal is to find an effective method for programmers to improve the performance of such matrix multiplication layers. Halide is an image processing programming language that separates the algorithm from its schedule. With the use of Halide, one can easily enhance the performance of their code with built-in scheduling primitives. In this paper, we propose sparse matrix compression schedule primitives with different compression schemes in Halide and find a method to improve convolution with the im2col method. With this design, we can compress the matrix to enhance the performance of convolution. We can also optimize natural language processing (NLP) with proposed compression scheduling. The word embedding training model can convert words into multidimensional vectors and transform words that do not have meaning into vectors with meaning. We focus on the word representation application in FastText, in which general matrix-vector multiplication (GEMV) is one of the most computationally intensive operations. We refine the software architecture of FastText and preprocess the pretrained model ahead of time. Our experiments show that the convolution and GEMV performance can be enhanced by the proposed design. [ABSTRACT FROM AUTHOR]

This study delves into the EA-SAS platform, a digital twin environment developed by our team, with a particular focus on the EA-SAS Cloud Scheduler, our bespoke program designed to optimize ETL (extract, transform, and load) scheduling and thereby enhance automation within industrial systems. We elucidate the architectural intricacies of the EA-SAS Cloud Scheduler, demonstrating its adeptness in efficiently managing computationally heavy tasks, a capability underpinned by our empirical benchmarks. The architecture of the scheduler incorporates Docker to create isolated task environments and leverages RabbitMQ for effective task distribution. Our analysis reveals the EA-SAS Cloud Scheduler’s prowess in maintaining minimal overhead times, even in scenarios characterized by high operational loads, underscoring its potential to markedly bolster operational efficiency in industrial settings. While acknowledging the limitations inherent in our current assessment, particularly in simulating real-world industrial complexities, the study also charts potential future research pathways. These include a thorough exploration of the EA-SAS Cloud Scheduler’s adaptability across diverse industrial scenarios and an examination of the integration challenges associated with its reliance on specific technological frameworks.

In recent times, distributed systems have demonstrated their efficiency in processing a large number of tasks in as little time as possible. In this context, the scheduler has the most influence because it manages how the respective tasks are processed on various resources. We set out to treat a planning problem with tasks and resources as main entities. Between these two entities we established certain constraints to model a real problem, and to solve the planning problem we opted for the development of a genetic algorithm. To evaluate the performance of the algorithm, we analyzed how it manages to reduce the processing time and at the same time fulfill the set constraints. We also analyzed the impact of adding additional resources in the schedule for the same tasks.

Communication platform systems as, e.g., advanced telecommunication computing architecture (ATCA) standard blades located in standardized chassis, provides high level communication services between system peripherals. Each ATCA blade brings dedicated functionality to the system but can as well exist as separated host responsible for servicing set of task. According to platform philosophy these parts of system can be quite independent against another solutions provided by competitors. Each system design can be different and can face with many computer systems design problems. One of the most difficult design problems to solve is system integration with a set of components running on different operating system levels. This paper presents Linux scheduler improvement possibility to make user space application classified as time demanding (required to be serviced by CPU in given amount of time) running in user space together with complicated kernel software structure in the system.

A group of organizations wishing to collaborate urgently, for example in case of a crisis, need to have a way to quickly deploy applications which enable them to speed up a potentially crisis-resolving decision-making process. A cross-organizational Kubernetes cluster, which is orchestrated by a central operator, allows to initiate these deployments in an ad hoc way. Performance issues may however arise at runtime, for example, a video pipeline belonging to a CCTV camera may produce a too low number of frames per second. The ad hoc cross-organizational collaboration case is especially prone to such issues as the set of candidate nodes and the environment in which they run may not be fully known to the operator. This article therefore motivates and describes the usage of a probe swarm architecture, which allows the operator to quickly generate an overview of the resource capabilities of a set of nodes, by executing code fragments locally. The obtained measurements can then enable the operator to decide on rescheduling operations. Evaluation of an illustrative probe swarm intervention shows that the performance of an example application could improve with factor five, ten or hundred when the pod would be rescheduled. This indicates that the proposed probe swarm architecture may complement other performance bottleneck detection techniques to improve performance of applications that need to be deployed urgently. [ABSTRACT FROM AUTHOR]

Digital growth during the Corona pandemic has generated massive data. The Hadoop in big data has to be more efficient in resource handling and job scheduling. This article proposes the improved job scheduler which is more efficient in fair job scheduling even with the heterogeneous resources. The faster job execution depends upon the localization too. The nearer the slots are, the faster is the execution. So, this article proposes a hybrid metaheuristic algorithm with fair scheduling and data locality as the two objectives in job scheduling. The dominant resource fairness policy in Hadoop YARN is updated by hybrid generalized particle swarm optimization and simulated annealing for minimum locality and maximum fairness in scheduling. The algorithm is tested on various workloads for heterogeneous resources. [ABSTRACT FROM AUTHOR]

• An imputation technique for extreme or non-normal data in the diffusion models algorithm is suggested by the authors. The improvement process involves modifications to noise creation, the loss function, and the noise scheduler. • It is suggested that natural generative Perlin noise be created at every diffusion stage. Scheduler improvement and loss function adaptation with Perlin noise are also provided. The Perlin noise generates high-slope normal distribution characteristics, causing non-normally distributed data to become more contaminated by noisy data to resemble a Gaussian distribution. • Non-normally distributed data can be forced into a normal distribution by Perlin noise during the forward phase of the diffusion model. As a result, the gradual introduction of noisy data into deep learning models yields a more regularly distributed output. The process of transforming original data into data with maximum noise can be slowed down by the modified scheduler function. The deep learning network is exposed to and learns from the noisy data at step ttt before the noise reaches its maximum level. The step procedure of the modified scheduler function causes the data in the deep learning network to transition to noise more slowly compared to the standard scheduler (linear or quadratic). • The proposed approach is assessed by simulating the missing data rate under various scenarios: Missing at Random (MAR), Missing Completely at Random (MCAR), and Missing Not At Random (MNAR), and Each case is addressed similarly, with 20% to 80% of the data being missing. In comparison to other deep learning imputation methods, the proposed methods and improvements contribute to a reduction in RMSE values by up to 10% for the imputation of non-normally distributed data. Imputation is a critical method for enhancing dataset quality, essential for ensuring accurate analysis and insights. This research proposes an advanced imputation algorithm utilizing a Diffusion Model enhanced with Perlin noise generation. We introduce Perlin noise at each step of the diffusion process and incorporate a cosine scheduler to optimize performance. Our approach demonstrates improvements in imputing non-normal data, validated through tests on ten real datasets. Due to the substantial slope of distribution properties produced by perlin noise, noisy data gradually contaminates nonnormally distributed data, making it more like to a Gaussian distribution. The Perlin noise distribution increases the normality of the noisy data provided noise when it enters the deep neural process of diffusion imputation. We assess our proposed approach by simulating the missing data rate using three scenarios: Missing Completely at Random (MCAR), Missing Not At Random (MNAR), and Missing at Random (MAR). Every case is handled similarly, with 20 % to 80 % missing data. Compared to other deep learning imputation methods, our proposed methods and improvements contribute to lowering the RMSE value up to 10 % on non-normal distributed data imputation. [ABSTRACT FROM AUTHOR]

Multipath communication is a well-developed technology that enhances communication effectiveness and resilience. Moreover, it can flexibly utilize network resources through load balancing among available paths. However, traditionally, deploying such load balancing functions on network devices is costly due to the required configuration changes and complicated signaling mechanisms on the devices’ control planes. Programming Protocol-independent Packet Processors (P4) has recently emerged as a programming language that enables programmability on the data plane, with the potential to relieve such issues in multipath communication. This work introduces and implements three P4-based multipath schedulers that can split traffic over several paths in wireless networks. The first is P4-based Random Splitting, which distributes traffic randomly. The second is P4-based Weighted Round Robin, with path scheduling based on weights in accordance with path capability. The last is P4-based Dynamic Weighted Round Robin (DWRR), which can improve bandwidth utilization by shifting the weights following dynamic changes in the available bandwidth (i.e., when congestion occurs). We have extensively evaluated the implementation of these three P4-based schedulers in a Mininet-WiFi/P4 environment with User Datagram Protocol (UDP) traffic. The results show that these schedulers can achieve multipath communication with the designed scheduling mechanisms.

Currently, there is uncertainty in the modeling techniques of cyber-physical systems (CPS) when faced with the multiple possibilities and distributions of complex system behavior. This uncertainty leads to the system’s inability to handle uncertain data correctly, resulting in lower reliability of the system model. Additionally, existing technologies struggle to verify the activity and safety of CPS after modeling, lacking a dynamic verification and analysis approach for uncertain CPS properties.This paper introduces a generalized possibility decision process as a system model. Firstly, the syntax and semantics of generalized possibility temporal logic with decision processes are defined. Uncertain CPS is extended by modeling it based on time-based differential equations and uncertainty hybrid time automaton. After that, model checking is performed on the properties of activity and safety using fuzzy linear time properties. Finally, a cold–hot hybrid constant-temperature system model is used for simulation experiments. By combining theory and experiments, this paper provides a new approach to the verification of uncertain CPS, effectively addressing the state explosion problem. It plays a crucial role in the design of uncertain CPS and offers a key solution for model checking in the presence of uncertainty.

Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building.

With the recent advances in computing devices such as smartphones and laptops, most devices are equipped with multiple network interfaces such as cellular, Wi-Fi, and Ethernet. Multipath TCP (MPTCP) has been the de facto standard for utilizing multipaths, and Multipath QUIC (MPQUIC), which is an extension of the Quick UDP Internet Connections (QUIC) protocol, has become a promising replacement due to its various advantages. The multipath scheduler, which determines the path to which each packet should be transmitted, is a key function that affects the multipath transport performance. For example, the default minRTT scheduler typically achieves good throughput, while the redundant scheduler gains low latency. While the legacy schedulers may generally give a desirable performance in some environments, however, each application renders different requirements. For example, Web applications target low latency, while video streaming applications require low jitter and high video quality. In this paper, we propose a novel MPQUIC scheduler based on deep reinforcement learning using the Deep Q-Network (DQN) that enhances the quality of multimedia streaming. Our proposal first takes into account both delay and throughput as a reward for reinforcement learning to achieve a low video chunk download time. Second, we propose a chunk manager that informs the scheduler of the video chunk information, and we also tune the learning parameters to explore new random actions adequately. Finally, we implement our new scheduler on the Linux kernel and give results using the Mininet experiments. The evaluation results show that our proposal outperforms legacy schedulers by at least 20%. [ABSTRACT FROM AUTHOR]

Modern Linux operating systems are being used in a wide range of fields, from small IoT embedded devices to supercomputers. However, most machines use the default Linux scheduler parameters implemented for general-purpose environments. The problem is that the Linux scheduler cannot utilize the features of the various hardware and software environments, and it is therefore, difficult to achieve optimal performance in the machines. In this paper, we propose STUN, an automatic scheduler optimization framework. STUN modifies the five scheduling policies of the Linux kernel and 10 parameters automatically to optimize for each workload environment. STUN decreases the training time and enhances the efficiency through a filtering mechanism and training reward algorithms. Using STUN, users can optimize the performance of their machines at the OS scheduler level without manual control of the scheduler. STUN showed an execution time and improved FPS of 18.3% and 22.4% on a face detection workload, respectively. In addition, STUN showed 26.97%, 54.42%, and 256.13% performance improvements for microbenchmarks with 4, 44, and 120 cores for each. [ABSTRACT FROM AUTHOR]

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Summary: The seamless conveying of information's considering internet as a virtual space, connecting all users from the world is cloud computing. In simpler terms it is a means of storing and accessing information over internet irrespective of place and time. The cloud proceeds with the process of storing, retrieving and allowing access, on demand as a paid service. As the benefits of cloud is attractive, the number of users adopting to the cloud also increases, so the workflow management becomes tedious and more challenging in the cloud. The decision to provide with an enhanced work flow management requires proper scheduling recollecting value of the information. So the paper proposes with an efficient method of managing the work flow considering the value of the information by categorizing between the much value and non‐value information's and framing the algorithm that functions as scheduler using the parallel implementation in natural process of genetic algorithm (GA) with secured frame work for the information's of high value. The proposed work shows an overwhelmed performance than the conventional techniques in the time taken for the execution and its total cost. The performance of the scheduler is validated in the WORKFLOWSIM on the grounds of time of execution and total cost. [ABSTRACT FROM AUTHOR]

Big data has ushered in the era of the tera, during which massive amounts of data are being gathered at accelerating rates. The size of the world's data is increasing in zeta-bytes as a result of improvements in processing speed, storage capacity, and data availability. One of the big data technologies is Hadoop, which uses the Map-Reduce and Hadoop Distributed File System to analyse data. An essential task for effective cluster resource management is job scheduling. Schedulers for Hadoop are pluggable parts that allocate resources to jobs. The default FIFO, Fair, and Capacity schedulers are prevalent in a variety of schedulers. A thorough analysis of the various work scheduling algorithms has been carried out in this paper. Additionally, their comparative parametric analysis was conducted while highlighting the essential features that these schedulers have in common. [ABSTRACT FROM AUTHOR]

Multipath transmission control protocol (MPTCP) is a transport layer protocol that transmits TCP segments on more than one path in multihomed devices. It was designed with the aim of bandwidth aggregation and redundant connections. Currently, multihomed devices have wireless interfaces of heterogeneous nature. MPTCP is not able to give its optimal performance in heterogeneous networks. This paper presents an experimental performance study of four different schedulers, namely roundrobin, default, blest, and redundant. The testbed comprises ethernet, LTE, and wifi networks to connect multihomed devices. The authores have compared the scheduler performance in terms of throughput, download time, and path utilization rate in homogenous and heterogenous scenarios. Results showed that round robin provides optimal throughput in homogenous networks and also performs bandwidth aggregation by utilizing both the paths but fails to perform in heterogenous networks. Blest provides best throughput among the four schedulers but prefers fast path only. [ABSTRACT FROM AUTHOR]

Subject of Research. The paper presents the distributed deep learning particularly convolutional neural network problem for resource-constrained devices. General architecture of convolutional neural network and its specificity is considered, existing constraints that appear while the deployment process on such architectures as LeNet, AlexNet, VGG-16/VGG-19 are analyzed. Deployment of convolutional neural network for resource-constrained devices is still a challenging task, as there are no existing and widely-used solutions. Method. The method for distribution of feature maps into smaller pieces is proposed, where each part is a determined problem. General distribution model for overlapped tasks within the scheduler is presented. Main Results. Distributed convolutional neural network model for a resource-constrained cluster and a scheduler for overlapped tasks is developed while carrying out computations mostly on a convolutional layer since this layer is one of the most resource-intensive, containing a large number of hyperparameters. Practical Relevance. Development of distributed convolutional neural network based on proposed methods provides the deployment of the convolutional neural network on a cluster that consists of 24 RockPro64 single board computers performing tasks related to machine vision, natural language processing, and prediction and is applicable in edge computing.

Video surveillance system is the integration of computers, networks, communications, and video CODEC, etc. Because of its distributed architecture, parallel image processing and ease of installation and expansion, it is widely used in many fields such as education, transportation and industry. However, there are some challenges of video surveillance applications in smart cities such as large scale of video events, low quality and big delay of video data transmission, and the loss of video surveillance data integrity. In order to solve the above problems, this paper designs a series of optimization algorithms and scheduling strategies based on Unmanned Aerial Vehicle (UAV) cluster. Firstly, we construct a full device coverage network with UAV cluster in heterogeneous communication environment of smart cities. Secondly, we formulate the scheduling problem of UAV cluster as bi-objective fragile bin packing problem, and design an optimal scheduling algorithm with constant approximation performance ratio. The simulation experimental results fully demonstrate the effectiveness, feasibility and robustness of the proposed solution in terms of system life cycle, video decodable frame rate, the ratio of UAV flight time to system life cycle, throughput and delay.