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Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

Assigning jobs to optimum resources in a grid environment is the main aim of a grid scheduler. Communication cost has always been an important issue in grid environments. Proposing new scheduling algorithms to consider this cost accurately and allocate jobs to an optimum resource efficiently has always been of great importance. In this paper we have proposed a grid scheduling algorithm that is aware of the costs for different network paths. We have also considered soft real-time characteristics of jobs in our proposed algorithm. We have simulated and compared our algorithm with some key scheduling algorithms such as Least Load First (LLF), Random, and First Come First Served (FCFS) in Gridsim by considering soft real-time jobs. Results show the superiority of our scheduling algorithm due to its ability to predict network cost and to satisfy the demands of soft real-time tasks.

Task scheduling is one of the key technology of cloud computing, its main goal is to satisfy the fairness of the system resources and tasks more effectively, and at the same time as much as possible to realize the load balance of system resources and reduce the completion time. Combined with the advantages of genetic Algorithm, we put forward a Fair and Balanced and effective genetic Scheduling Algorithm FBEGSA (Fair Balanced Efficient Genetic Scheduling Algorithm).The experimental results show that the algorithm is useful to task scheduling.

Optical Grid that provides a way to integrate the geographical distributed resources for large- scale grid applications. This paper mainly focuses on the task scheduling issue in Optical Grid and proposes two task scheduling algorithms to mini-mize the total time or scheduling length required for a given task. A simulated optical grid has been addressed to demon-strate the effectiveness of the schedulin g approach. The performance tests and anal ysis of the two algorithms are investi-gated to show the effect of the routing policy on the schedulin g length. A more flexible policy is able to further improve scheduling length. Keywords: Optical Grid, Task Scheduling Algorithm, Communication Contention I INTRODUCTION Distributed computing, also known as computational Grid, is a technology that makes it possible to utilize the geographi-cal distributed computational resources to solve the intensive computation of large-scale data. Most Grid systems were built over the IP packet switched networks in the past. In the situation when some data-intensive Grid applications re-quire moving Gigabytes or even larger of data, the low speed of the network becomes the bottleneck of Grid system, which could not allow the remote resources to be used efficiently. Nowadays the optical network with Synchronous Digital Hierarchy (SDH), Wavelength-D ivision Multiplexing (WDM), and Automa tic Switched Optical Network (ASON) infrastructure can provide a much larger amount of bandwidth than the electronic network, such as IP packet switched network. Therefore, it is significant to use the optical network as the network infrastructure of the computational Grid. We refer to this infrastructure as Optical Grid, which employs optical network to interconnect grid computational re-sources (e.g. supercomputers, computer clusters, storage and display devices) with dynamically provisioned lightpaths [1]. It has been realized that task scheduling is a fundamental issue in achieving high performance in grid environments [6]. Task scheduling aims to optimize the overall performance of a system, e.g., minimizing the average job response time and maximizing the number of jobs exec uted in certain period of time. Optical grid task scheduling is quite different from classic grid task scheduling model. In Optical Grid system, we can treat the optical network as a kind of resource, the scheduling algorithm in Optical Grid system should schedule the tasks to appropriate resources as we ll as scheduling the communications between tasks to appropriate optical links. The objec-tive of task scheduling is to minimize the completion time (we called it as scheduling length or make span ) of an appli-cation. Usually, this problem is modeled and solved under several assumptions. Unless the assumptions are simplified enough, the problem has been proven to be NP-complete [8]. Therefore, many heuristic algorithms have been proposed. But these heuristics either are based on the assumption that the processors are fully connected, or just don’t take into ac-count the optical network’s characters, es pecially the characters of WDM optical network. While actually, in the Optical Grid system, the computational resources are more likely not fully connected, and the communication contention is an important factor that affects the completion time of a Grid application. Thus, a new heuris tic algorithm that can schedule tasks and also the communications are needed. In this paper, we mainly focus on the static scheduling problem, which means the character of the Grid applica tion, including the task execution times, task dependencies, task communications and synchronization are known a priori.

Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regression to censored data, particularly for upper quantiles. The minimum distance approach, utilizing dual-kernel estimation and the inverse cumulative distribution function, shows promise in addressing these challenges, especially with higher-dimensional covariates. Methods: This study contrasts two methods within the realm of quantile linear regression for survival analysis: check-based modeling and the minimum distance approach. Effectiveness is assessed across various scenarios through comprehensive simulation. Results: The simulation results showed that using the quantile regression model with the minimum distance approach reduces the percentage of root mean square error in parameter estimation compared to the quantile regression models based on the check loss function. Additionally, a larger sample size and reduced censoring percentage led to decreased root mean square error in parameter estimation. Conclusion: The research highlights the benefits of using the minimum distance approach for quantile regression. It reduces errors, improves model predictions, captures patterns, and optimizes parameters even with complete data. However, this approach has limitations. The accuracy of estimated quantiles can be influenced by the choice of distance metric and weighting scheme. The assumption of independence between censoring mechanism and survival time may not hold in real-world scenarios. Additionally, dealing with large datasets can be computationally complex.

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

A convergence analysis of the angular position and velocity tracking errors and parametric estimation errors for a 2-link robot dynamics in the absence of noise exist in the literature (Slotine and Li in Applied nonlinear control. Prentice- Hall Inc, Englewood Cliffs, NJ, 1991). In this paper, we consider the effect of noise on the robot dynamics as well as on the parameter update equation. Noise in the robot dynamics arises due to errors in reading \(q,\dot{q}\), i.e., the angular positions and velocities of the robot link that are used for calculating the errors e, \(\dot{e}\) used to generate the feedback torque via the PD controller. Noise is chosen as Gaussian because it is produced as the cumulative effect of several small independent random effects like electrons moving in the motor wire and dust friction, and we know from the central limit theorem that the sum of a large number of small independent effects has a Gaussian distribution. Noise in the parametric update equation arises due to finite register effects in the digital computer. Both of these noises are modeled as white Gaussian, and the robot and parametric update dynamics have thus been modeled as nonlinear coupled stochastic differential equations. Using linearization of this coupled SDE system around the non-random components of the trajectory estimation error and parametric estimation errors, we first derive stochastic integral expressions for the random components in these errors. We then evaluate the average rate of change of the Lyapunov energy function using Ito calculus and obtain conditions on the noise amplitude so that the limiting value of this average rate of change is negative, guaranteeing asymptotic average stability of the system and parametric estimation algorithm. One of the novelties of this work is the new algorithm for parameter estimation which enables us to improve on the algorithm convergence rate as compared to previous work (Slotine and Li 1991). Finally, our stochastic algorithm is verified by performing hardware experiments with the actual PHANToM Omni robot.

This study demonstrated the equivalence between the Rasch testlet model and the three‐level one‐parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE) with the expectation‐maximization algorithm in ConQuest and the sixth‐order Laplace approximation estimation in HLM6. The results indicated that the estimation methods had significant effects on the bias of the testlet variance and ability variance estimation, the random error in the ability parameter estimation, and the bias in the item difficulty parameter estimation. The Laplace method best recovered the testlet variance while the MMLE best recovered the ability variance. The Laplace method resulted in the smallest random error in the ability parameter estimation while the MCMC method produced the smallest bias in item parameter estimates. Analyses of three real tests generally supported the findings from the simulation and indicated that the estimates for item difficulty and ability parameters were highly correlated across estimation methods.

The way the processes in a parallel program are scheduled on the processors of a multiprocessor system affects the performance significantly. Finding a schedule of processes to processors which results in minimum completion time is NP-hard. Therefore, one has to resort to heuristic schedules. However, it is often difficult to determine if a specific schedule is close to the optimal case or if it is worthwhile to look for other schedules. Based on information from previous executions of the parallel program, we present a formula for an upper bound on the minimum completion time of the program. The bound is a function of a set of parameters. Some of these parameters are obtained from the previous executions of the program and the others describe the target multiprocessor architecture for which we want to bound the minimum completion time. The bound is optimal when it is based on information from one previous execution. Using these results, we are able to decide if a certain schedule is close to optimal or if it is worthwhile to look for other schedules. This is demonstrated by evaluating the completion time of a specific schedule of a particular program. The proofs used for obtaining the bound are based on program transformations and combinatorial mathematics.

The spur of Grid computing is to aggregate the power of widely dispersed resources, and provide non-trivial services to users. In attempts to utilize a diverse set of resources in grids proficiently, scheduling has been made. The primary intention of scheduling is the minimization of application completion time; however, they may lead to the usage of excess and redundant resources. Our algorithm performs the scheduling b y accounting for both completion time and resource usage. Since the performance of grid resources changes dynamically and the accurate estimation of their performance is very difficult, our algorithm incorporates rescheduling to deal with unforeseen performance fluctuations effectively. Also, fault tolerance is an essential part of the grid. In Grid environments, execution failures can occur for various reasons such as network breakdown, failure or non-availability of required resources. Fault tolerance can be achieved in grids by Over provisioning and Check pointing techniques. Since, over provisioning violates the resource usage control, check pointing strategy is implemented in our proposed method.

The prediction of human motion is essential for safe human-robot collaboration (HRC). For existing prediction methods based on adaptive neural network (NN) models, estimation errors (EEs) of model parameters are directly coupled with prior EEs of trajectories. This results in poor assessment of the mean square estimation error (MSEE) of model parameters, which is a potential danger to the safety HRC. In this paper, a novel “look-backward-and-forward” method is proposed to assess the EEs of model parameters. This method firstly computes EEs of model parameters in the past few time instants based on the actual error of trajectory predicted in the past. Subsequently, the EEs of model parameters are recursively updated till the current time instant. In this way, the trajectory of a single particle and its MSEE are accurately accessed. The direct coupling between the MSEE of the trajectory and that of model parameters is cut off and the mean estimation errors (MEEs) of parameters are only affected by the EEs of the past trajectory at the recent several time instants, rather than accumulating as the time propagates. This method is further extended to estimate the pose trajectory of a rigid body based on fitting of an ellipsoid’s parameters. The experimental results of predicting 3-D trajectories of human motions show the effectiveness of the proposed method.

In this paper we present algorithms for the problem of scheduling n independent jobs on m identical machines. As a generalization of the classical multiprocessor scheduling problem each machine is available only at a machine dependent release time. Two objective functions are considered. To minimize the makespan, we develop a dual approximation algorithm with a worst case bound of 5/4. For the problem of maximizing the minimum completion time, we develop an algorithm, such that the minimum completion time in the schedule produced by this algorithm is at least 2/3 times the minimum completion time in the optimum schedule. The paper closes with some numerical results.

Eigenvector spatial filtering (ESF) is becoming a popular way to address spatial dependence. Recently, a random effects specification of ESF (RE-ESF) is receiving considerable attention because of its usefulness for spatial dependence analysis considering spatial confounding. The objective of this study was to analyze theoretical properties of RE-ESF and extend it to overcome some of its disadvantages. We first compare the properties of RE-ESF and ESF with geostatistical and spatial econometric models. There, we suggest two major disadvantages of RE-ESF: it is specific to its selected spatial connectivity structure, and while the current form of RE-ESF eliminates the spatial dependence component confounding with explanatory variables to stabilize the parameter estimation, the elimination can yield biased estimates. RE-ESF is extended to cope with these two problems. A computationally efficient residual maximum likelihood estimation is developed for the extended model. Effectiveness of the extended RE-ESF is examined by a comparative Monte Carlo simulation. The main findings of this simulation are as follows: Our extension successfully reduces errors in parameter estimates; in many cases, parameter estimates of our RE-ESF are more accurate than other ESF models; the elimination of the spatial component confounding with explanatory variables results in biased parameter estimates; efficiency of an accuracy maximization-based conventional ESF is comparable to RE-ESF in many cases.

In this article, an adaptive dynamic surface control approach is proposed for uncertain strict‐feedback systems (SFSs) to guarantee both the prescribed transient tracking performance and the asymptotic tracking while realizing the accurate parameter estimation. It is assumed that SFSs are subject to linearly parametric uncertainties in both the drift terms and the control coefficients. A new inequality on the arctangent function, which can be widely used in the robust or the adaptive control designs, is established. Owing to this inequality, nonlinear robust filters with arctangent functions are designed and embedded into the backstepping control algorithm to avoid the “differential explosion” problem. Moreover, an improved forgetting‐factor‐based parameter estimation error reconstruction mechanism is proposed. And the obtained parameter estimation errors are integrated into the adaptive laws to achieve the accurate parameter estimation. Furthermore, the projection operator is applied to avoid the singularity problem of the control law. Besides, an asymmetric error transformation is introduced to restrict the tracking error within the prescribed performance envelope. It is proved that the tracking error and the parameter estimation errors asymptotically converge to zero and the tracking error satisfies the prescribed transient performance. Finally, the effectiveness of the proposed control approach is validated in terms of the single‐link manipulator actuated by a brush DC motor.

We investigate a novel satellite avoidance strategy to mitigate the impact of large commercial satellite constellations in low-Earth orbit on the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We simulate the orbits of currently planned Starlink and OneWeb constellations (∼40,000 satellites) to test how effectively an upgraded Rubin scheduler algorithm can avoid them, and assess how the overall survey is affected. Given a reasonably accurate satellite orbit forecast, we find it is possible to adjust the scheduler algorithm to effectively avoid some satellites. Overall, sacrificing 10% of LSST observing time to avoid satellites reduces the fraction of LSST visits with streaks by a factor of 2. Whether such a mitigation will be required depends on the overall impact of streaks on science, which is not yet well quantified. This is due to a lack of adequate information about satellite brightness distributions as well as the impact of glints and low surface brightness residuals on alert purity and systematic errors in cosmological parameter estimation. A significant increase in the number of satellites or their brightness during Rubin Operations may make implementing this satellite avoidance strategy worthwhile.