Earticle

Significant correlations have been observed between the scheduling of language tasks in L2 classrooms and the learners’ L2 attitude and motivation. As such, different task-scheduling methods are used in L2 classrooms to create and sustain learners’ motivation towards learning the L2 to maximize language practice. Blocking and interleaving are two such methods of task scheduling that have been widely discussed in recent times. The present study aimed to examine and trace the differences between the effects of these two task-scheduling methods on L2 motivation during a threemonth- long English-speaking training program. Two groups of Indian undergraduate ESL learners (N=44) were kept in blocked and interleaved conditions, and their attitude and motivation towards English were recorded using the attitude and motivation test battery at several intervals during the study. Although no statistically significant difference in L2 attitude and motivation was observed in the first month, a significant difference between blocking and interleaving in their effect on the stimulus appraisal scales of coping potential and self/social image was recorded in the third month. The findings highlight the importance of task scheduling in L2 classrooms and may provide valuable insights into the specific nature of language practice that can be used to create a highly motivated language classroom.

ি􀐵তীয় ভাষাৰ ে􀏜ণীেকাঠাত ভাষাৰ কামৰ সময়সূচী িনধ􀎺া ৰণ আ􁃋 িশ􀏠াথ􀎻সকলৰ ি􀐵তীয় ভাষাৰ মেনাভাৱ আ􁃋 অনুে􀏕ৰণাৰ মাজত 􁃕􁃋􀐯পূণ 􀎺স􀑕ক 􀎺 েদখা ৈগেছ। েসেয়েহ, ভাষাঅনুশীলন সবা􀎺 িধক কৰাৰ বােব ি􀐵তীয় ভাষা িশকাৰ 􀏕িত িশ􀏠াথ􀎻সকলৰ অনুে􀏕ৰণা সৃ􀎜􀑭 আ􁃋 বাহাল ৰখাৰ বােব ি􀐵তীয় ভাষাৰ ে􀏜ণীেকাঠাত িবিভ􀑂 কায􀎺􀒝 -অনুসূচীপ􀐴িত ব􀒝ৱহাৰ কৰা হয়। অৱেৰাধ আ􁃋 আ􀐾ঃসংেযাগ ৈহেছ কামৰ সময়সূচীৰ এেন দুটা প􀐴িত যাক সা􀏸িতক সময়ত ব􀒝াপকভােৱ চিচ􀎺ত কৰা ৈহেছ। বতম􀎺 ানৰ অধ􀒝য়নেটাৰ উে􀐳শ􀒝 ৈহেছ িতিন মাহ দীঘলীয়া ইংৰাজী ভাষী 􀏕িশ􀏠ণ কাযস􀎺 ূচীৰ সময়ত ি􀐵তীয় ভাষাৰ অনুে􀏕ৰণাৰ ওপৰত এই দুটা কায􀒝􀎺-অনুসূচী প􀐴িতৰ 􀏕ভাৱেবাৰ পৰী􀏠া আ􁃋 অনুসৰণ কৰা। ি􀐵তীয় ভাষা িশ􀏠াথ􀎻 িহচােপ ভাৰতীয় 􀑹াতক ইংৰাজীৰ দুটা েগাট (এন=44) অৱ􁃋􀐴 আ􁃋 আ􀐾ঃসংেযাগযু􀐅 পিৰি􀑸িতত ৰখা ৈহিছল, আ􁃋 অধ􀒝য়নৰ সময়ত েকইবাটাও ব􀒝ৱধানত মেনাভাৱ আ􁃋 অনুে􀏕ৰণা 􀏕􀑨াৱলী ব􀒝ৱহাৰ কিৰ ইংৰাজীৰ 􀏕িত েতওঁেলাকৰ মেনাভাৱ আ􁃋 অনুে􀏕ৰণা নিথভ􀎦 􀐅 কৰা ৈহিছল। যিদও 􀏕থম মাহত ি􀐵তীয় ভাষাৰ মেনাভাৱ আ􁃋 অনুে􀏕ৰণাত েকােনা পিৰসাংিখ􀒝ক ভােৱ 􁃕􁃋􀐯পূণ 􀎺পাথক􀎺 􀒝 েদখা েপাৱা নািছল, তৃতীয় মাহত েমাকািবলা কৰাৰ স􀑘াৱনা আ􁃋 আ􀐮/সামা􀎝জক 􀏕িত􀐘িবৰ উ􀐳ীপনা মূল􀒝া􀐒ন ে􀑴লৰ ওপৰত েসইেবাৰৰ 􀏕ভাৱত অৱেৰাধ আ􁃋 আ􀐾ঃসংেযাগৰ মাজত এক 􁃕􁃋􀐯পূণ 􀎺পাথক􀎺 􀒝 নিথভ􀎦􀐅 কৰা ৈহিছল। ফলাফলেবােৰ ি􀐵তীয় ভাষাৰ ে􀏜ণীেকাঠাত কামৰ সময়সূচীিনধাৰ􀎺 ণৰ 􁃕􁃋􀐯 ৰ􀏠া কেৰ আ􁃋 ভাষা অনুশীলনৰ িনিদ􀎺􀑭 􀏕কৃ িতৰ িবষেয় মূল􀒝ৱান অ􀐾দ􀎺ৃ􀎜􀑭 􀏕দান কিৰব পােৰ যাক এক উ􀐗 অনুে􀏕িৰত ভাষাে􀏜ণীেকাঠা সৃ􀎜􀑭 কিৰবৈল ব􀒝ৱহাৰ কিৰব পািৰ।

The primary objective of task scheduling involves scheduling the task on resources and minimizing the objective of the schedule. In this study, we proposed mean grey wolf optimization algorithm to enhance the system performance there by depleting the scheduling issues. The main objective of this method is minimizing the makespan and energy consumption. The objective of the proposed algorithms has been evaluated using CloudSim toolkit for standard workload (left-skewed & right-skewed). The outcome of the simulation result shows that the proposed Mean GWO algorithm renders comparatively ample result than the other existing algorithms.

The objective of cloud task scheduling is to partition tasks on shared resources to minimize energy consumption and makespan. Recently, several meta-heuristics for task scheduling were proposed and achieved encouraging results. However, their performance is far from the ideal state and needs more improvement. This paper introduces an enhanced sunflower optimization (ESFO) algorithm for improving the performance of existing task scheduling. It finds optimal scheduling in a polynomial time. The experiments show that ESFO outperformed its counterparts. The amount of improvement in comparison with the best counterpart is 0.73% and 2.24% respectively in terms of makespan and energy consumption.

Aiming to provide low-cost MEC services for mobile devices in areas without/lacking infrastructure, we propose a renewable-energy-driven energy-harvesting-based task scheduling and energy management framework. First, for mobile devices, the constructed energy consumption minimization problem is solved by an alternating-optimization-based algorithm. Then, we design an energy management algorithm based on sampling average approximation to derive the optimal charging/discharging strategies, number of energy storage units, and renewable energy utilization. The simulation results show that the proposed framework can significantly reduce the cost of MEC servers and prolong the working hours of mobile devices.

Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

As autonomous driving and connected car technology advance, various deep learning applications for autonomous vehicles and complex traffic situations are increasing. Autonomous vehicles must collect and process vast amounts of sensor data to support various deep learning applications, but vehicles have limited computing resources to perform complex deep learning operations. Therefore, edge computing is a promising solution to complement the limitations of autonomous vehicles. In this paper, we design edge computing for efficient task processing in an autonomous driving environment using a driving simulator. Also, we propose a task scheduling and offloading method which determines the target server to offload a task according to the characteristics of the task and the computing resources. The effectiveness of the proposed method is verified through experimental evaluation in an autonomous driving environment, supporting multiple deep learning services that we established by using a driving simulator.

The Cloud computing has become the fast spread in the field of computing, research and industry in the last few years. As part of the service offered, there are new possibilities to build applications and provide various services to the end user by virtualization through the internet. Task scheduling is the most significant matter in the cloud computing because the user has to pay for resource using on the basis of time, which acts to distribute the load evenly among the system resources by maximizing utilization and reducing task execution Time. Many heuristic algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Cuckoo search (CS) algorithms, etc. In this paper, a Dynamic Adaptive Particle Swarm Optimization algorithm (DAPSO) has been implemented to enhance the performance of the basic PSO algorithm to optimize the task runtime by minimizing the makespan of a particular task set, and in the same time, maximizing resource utilization. Also, .a task scheduling algorithm has been proposed to schedule the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the Dynamic PSO (DAPSO) algorithm and the Cuckoo search (CS) algorithm; called MDAPSO. According to the experimental results, it is found that MDAPSO and DAPSO algorithms outperform the original PSO algorithm. Also, a comparative study has been done to evaluate the performance of the proposed MDAPSO with respect to the original PSO.

The latest mobile devices usually use multi-core technologies such as dual-core, quad-core or octa-core for high performance. Further, as the capacity of main memory in the mobile devices is growing, a lot of tasks are capable of running concurrently. With these trends, however, mobile devices run out of battery power faster than before. In recent years, next generation non-volatile memory technology (NVRAM) have developed significantly and considered as low-power main memory architecture. Traditionally, the bandwidth-aware multi-core task scheduling schemes have been studied in order to address the bandwidth saturation problem of shared main memory. In this paper, we propose a multi-core scheduling scheme considering DRAM/NVRAM hybrid main memory. The goal of the proposed scheme is to reduce the execution time of tasks by avoiding the memory bandwidth saturation as well as to improve the response time of interactive tasks. We have showed through trace-driven simulation that the proposed scheme outperforms the legacy scheduling schemes.

There’re huge numbers of users and various tasks need to be handled in the cloud computing environment, the high effective task scheduling algorithm is one of the crucial problems that the cloud computing need to solve. Aiming to the model structure of cloud computing, in this article it introduces the Particle Swarm Optimization algorithm (PSO) and Ant Colony Optimization algorithm (ACO) to combine with optimized task scheduling algorithm. First it takes the particle swarm optimization algorithm to generate the initial scheduling results, and introduces the random inertia weight to improve the scheduling ability of the algorithm, then to take the generated results of improved particle swarm optimization algorithm as the initial pheromones of the ant colony algorithm to find out the optimal scheduling scheme, and use the elitist strategy and crossover operator in the genetic algorithm to improve the ant colony algorithm, among the algorithms to use multistage optimization algorithm to improve the operating efficiency. The experimental results show that under the same conditions, the total task completion time of improved algorithm has been reduced and its performance advantage are getting more obvious with the increased task measures.

Cloud computing has been emerged as a new service model in computing world and giving lot of interest to the researchers to find its benefits. Task scheduling is a major conflict in cloud environment and genetic algorithms are one of the optimization techniques to solve that problem. Virtual machine’s processing elements are important criteria to solve a scheduling problem. In proposed algorithm called upgraded Genetic algorithm, initial population is sorted according to the number of processing elements of each virtual machines. Proposed algorithm is compared with MGA in terms of cost and with MACO in terms of make span. Experiment results shows upgrade genetic algorithm gives better efficiency in term of cost and make span.

With the rapid development of P2P technology, P2P IPTV applications have received more and more attention. And program-list distribution is very important to P2P IPTV applications. In order to collect IPTV program information, a distributed multi-protocol crawler was proposed based on principle of program-list distribution. The IPTV programs information will be used for characteristic analyses of program and for automatic sorting of program and establishment of IPTV repository in next work. In addition, a task scheduling model based on fuzzy control is introduced to improve performance of the crawler. In the experiment, three task scheduling algorithms are compared, and the results show that the fuzzy algorithm can balance service nodes’ load effectively with less task execution time.

Static task scheduling in multiprocessor frameworks is one of the well-defined NP Hard Problem. Due to optimal utilization of processors and in addition investing less time, the Scheduling of tasks in multiprocessor frameworks is of extraordinary significance. To Solve NP Hard Problem using traditional strategies takes reasonable measures of time. Over the time, various heuristic procedures were presented for comprehending it. Therefore, heuristic methods such as Genetic Algorithms are appropriate methods for task scheduling in multiprocessor system. In this paper, a new GA for static task scheduling in multiprocessor systems has been presented whose priority of tasks’ execution is based on the height of task in graph and other mentioned parameters and then scheduling is performed. This proposed method is simulated and then compared with Basic Genetic algorithm.

Based on the study of traditional min-min scheduling algorithm, the paper proposed a min-min task scheduling algorithm based on QOS constraints in cloud computing. According to the vector which is generated by QOS parameters, the algorithm processes the matching of resources and tasks, and then provides users with resources which meet their requirements. Experimental results show that the min-min task scheduling algorithm for cloud computing has better performance in such aspects as task execution time, rate of discarding task and QOS satisfaction compared with traditional min-min scheduling algorithm.

We proposed a task scheduling in cloud computing based on intelligence firefly algorithm aimed at the disadvantages of cloud computing task scheduling. Firstly, on the basis of cloud model, used intelligence firefly algorithm with strong ability of global searching to find the better solution of cloud computing task scheduling then turned the better solution into the initial pheromone of improved firefly algorithm, and found out the cloud computing task scheduling and the algorithm’s global optimal solution through improved firefly information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence firefly algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution’s time of objective function value is shorten which meet the user’s needs more.

Mobile cloud computing, which comes up in recent years, is a new computing paradigm. It enables people to access remote clouds by mobile device, even to build mobile micro-cloud(MuCloud) with mobile device to provide lightweight service. Despite extensive studies of task scheduling in wired cloud, effective scheduling in mobile cloud still remains challenges:1) Unreliable wireless connection and dynamic join and quit of MuCloud often result in decreased reliability of scheduling; 2) As the process capacities of wired clouds and MuClouds vary greatly, it is hard to achieve load balancing; 3) During moving, tasks, such as traffic navigation, may be scheduled consecutively by mobile users as space-time changes. Such application scenarios often incur makespan accumulation which impairs user experience, even causes system crash. Our work aims at such problems. We firstly illustrate the reason for selection of makespan and load balancing as two key performance indicators for task scheduling in the proposed architecture of mobile cloud which integrates MuClouds. Then after introduction to Monte Carlo method, degenerated Monte Carlo estimate is defined and a scheduling algorithm based on degenerated Monte Carlo estimate (DMCE) is presented. With extensive simulation experiments, the two above-mentioned indicators of task scheduling using different algorithms including DMCE, Max-Min, Min-Min and IGA are compared and evaluated. Accumulative effect and relative load are introduced to measure scheduling performance. The experimental results show that: 1)Compared with other algorithms, DMCE achieves smallest makespan on average when scheduled respectively; 2) DMCE has least accumulative effect when task sets scheduled consecutively, which makes makespan of a task set hardly relevant to the order of scheduling; 3)Among these algorithms, DMCE outperforms others in keeping relative load balancing by assigning tasks to clouds in proportion to each cloud’s process capacity.

Because the task scheduling problem is np-complete problem, it is hard to find a deterministic algorithm to solve the problem of task scheduling in the cloud computing platform. Therefore this paper presents a task scheduling mechanism based on simulated annealing algorithm. The algorithm is a modern heuristic algorithm and overcome the shortcoming of the local optimum search method. The algorithm uses a greedy algorithm to generate the initial value, and heat to a sufficiently high temperature and according to certain rules to generate a new value. If the new value is better than the original value or at a certain probability can be accepted, then replace the original value with the new value until cool. Experiments proved the feasibility and effectiveness of the algorithm. Compared with traditional algorithms, this task scheduling mechanism not only meets the needs of users and improves the performance of the system.

Efficient task scheduling mechanism in cloud computing can improve the resource utilization and enhance the overall performance of the cloud computing environment. However, the existing strategies based on static task requirements are difficult to guarantee the stability of the system, while dynamic mechanisms have higher complexity. In this paper, a new task scheduling algorithm based on potential game is proposed. We prove that the potential game will reach Nash equilibrium quickly. Also, the system load balancing level is adaptive with the number of users’ task changing. The experimental results show the priority of the proposed algorithm.

A new hybrid algorithm is proposed in this paper. An independent task scheduling algorithm is designed based on the hybrid algorithm. The hybrid algorithm adopt list scheduling method to code harmony solution and convert harmony vector to priority-based independent task scheduling model, and perform variable neighborhood search on harmony solutions to improve Harmony Search efficiency and solution quality. The simulation results demonstrate that the proposed algorithm can improve the global search abilities and convergence speed and can escape local minimizer to look for better solutions.

Cloud computing provides powerful and on-demand business environment. It is built on top of virtualized data centers. Virtualization provides the flexible infrastructure for cloud. But, managing the resources and scheduling the tasks in virtualized data center is a challenging task. This paper proposes a minimum makespan task scheduling framework named MMSF and minimum makespan task scheduling algorithm named as MMA. This algorithm is developed with two objectives. First, minimizing the total makespan and maximizing the virtual machine utilization. The task scheduling problem is formulated as multi-objective optimization problem. It is solved by using optimization techniques. Experiments shows that MMA outperforms the traditional task scheduling algorithms based on total makespan and virtual machine utilization.