Earticle

To address the problems of large system overhead and low timeliness when dealing with task scheduling in mobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computing based on an improved genetic algorithm was proposed. First, a user task scheduling system model based on edge cloud computing was constructed using the Shannon theorem, including calculation, communication, and network models. In addition, a multi-objective optimization model, including delay and energy consumption, was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operations of the genetic algorithm were improved using the best reservation selection algorithm and normal distribution crossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objective problem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimental analysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss does not exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.

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.

Task scheduling is an integrated component of computing with the emergence of grid computing. In this paper, we address two different task scheduling models, which are static Round-Robin (RR) and dynamic Fastest Site First (FSF) task scheduling method, using extended timed marked graphs, which is a special case of Stochastic Petri Nets (SPN). Stochastic reward nets (SRN) is an extension of SPN and provides compact modeling facilities for system analysis. We build hierarchical SRN models to compare two task scheduling methods. The upper level model simulates task scheduling and the lower level model implements task serving process for different sites with multiple servers. We compare these two models and analyze their performances by giving reward measures in SRN.

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.

Power consumed by modern computer systems, particularly servers in data centers has almost reached an unacceptable level. However, their energy consumption is often not justifiable when their utilization is considered; that is, they tend to consume more energy than needed for their computing related jobs. Task scheduling in distributed computing systems (DCSs) can play a crucial role in increasing utilization; this will lead to the reduction in energy consumption. In this paper, we address the problem of scheduling precedence-constrained parallel applications in DCSs, and present two energy- conscious scheduling algorithms. Our scheduling algorithms adopt dynamic voltage and frequency scaling (DVFS) to minimize energy consumption. DVFS, as an efficient power management technology, has been increasingly integrated into many recent commodity processors. DVFS enables these processors to operate with different voltage supply levels at the expense of sacrificing clock frequencies. In the context of scheduling, this multiple voltage facility implies that there is a trade-off between the quality of schedules and energy consumption. Our algorithms effectively balance these two performance goals using a novel objective function and its variant, which take into account both goals; this claim is verified by the results obtained from our extensive comparative evaluation study.

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.

It is important that desktop grids should be able to aggressively deal with the dynamic properties that arise from the volatility and heterogeneity of resources. Therefore, it is required that task scheduling be able to positively consider the execution behavior that is characterized by an individual resource. In this paper, we implement a log analysis system with REST web services, which can analyze the execution behavior by utilizing the actual log data of desktop grid systems. To verify the log analysis system, we conducted simulations and showed that the resource group-based task scheduling, based on the analysis of the execution behavior, offers a faster turnaround time than the existing one even if few resources are used.

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.

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।

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.

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.

Most existing task scheduling algorithmsfail to aware users' QoS preference and result in low user satisfaction rate for they do not reflect users’QoS requirements. We classify QoS factors into four main QoS class which users understand well and can describe their important level, andintroduce AHP method to help user decide the class weight and avoid judgment logical error.Then, we improve existing standard PSO scheduling by use above AHP based different weights for different QoS classes to make PSO haveQoS preference awareness ability.The simulations show our method gets obviously higher user satisfaction rate and maintains the efficiency at the same time.Finally, the simulations also point out that the hierarchical scheduling is necessary to avoid the common tasks take over the special resource.

The multiprocessor computing is composed of more than one central processing units (CPU) that simultaneously execute the task of a parallel application for obtain quick results, to process a massive amount of data, and to solve a problem in expected time. If Scheduling is done properly in task allocation then they are increase the performance of the system. Task scheduling in a parallel environment is one of the NP-problems, which deals with the optimal assignment of a task. In this paper, various algorithms are surveyed that apportion a parallel program impersonate by an edge-weighted Directed Acyclic Graph (DAG). These include Bounded no. of Processors (BNP), Unbounded no. of Clusters (UNC), Task Duplication Based scheduling (TDB) and Arbitrary Processor Network scheduling algorithm (APN). The objective of this paper is to study and explore several DAG based task scheduling algorithm and the performance of all of the algorithms is evaluated and compared against each other on a unified basis by using various scheduling parameters.

The applications based on cloud computing platform usually need to use a number of computing resources and storage resources to completing computing tasks, so the fault-tolerant capability of system has become increasingly important. Aiming to solve this problem, an evaluation model of task scheduling is proposed based on cloud system (TSCS). TSCS can effectively model and simulate complex cloud systems due to its strong capabilities of quantitative evaluation and behavioral description resulting from combining the theoretical characteristics of queuing task theory and Petri net. The algorithm solves the problem that meeting customer service satisfaction and load balancing at the same time. In addition, consider single backup task status, for the failure of more than one processor at the same time, present the minimum cost of backup scheduling algorithm, the algorithm to solve the problem that require a lot of backup cost. Experimental results show that TSCS is able to effectively reflect the architecture characteristics of various cloud system at the perspectives of performance, service, etc., and highly simulated various kinds of dynamic service behaviors of cloud system, single workload and multi workloads shows that the proposed policy can finish the user’s queuing task scheduling before deadline as well as obtain approving cost efficient.

Scheduling algorithm is always a hot topic in cloud computing environment. In order to eliminate system bottleneck and balance load dynamically. A load balancing task scheduling algorithm based on weighted random and feedback mechanisms was proposed in this paper. At first the chosen cloud scheduling host chose resources by needs and made static quantification, and then sorted them; secondly the algorithm chose resources from which sorted by weight randomly; then it acquired corresponding dynamic information to make load filter and sort the left. At last it achieved the self-adaptively to system load through feedback mechanisms. The experiment shows that the algorithm has avoided the system bottleneck effectively and has achieved balanced load as well as self-adaptability to it.

In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.

In this paper, a BCC-DPSO scheduling algorithm is proposed to solve multi-objective optimization problem for task scheduling on Network-on-Chip (NoC). In our proposal, the relative advantage of the solution is evaluated by calculating its efficiency using BCC model in Data Envelopment Analysis (DEA), and the referred-time method is introduced to rank the BCC-efficient solution. Moreover, a sub-swarm strategy is adopted to reduce the high computational requirement introduced by the DEA. There are four sub-swarms, each of which optimizes one of four observed metrics, namely makespan, energy, link load and workload balance. Meanwhile, the speed vector updating formulation is modified to comply with the sub-swarm strategy. By conducting comparative simulations, the results show that our proposal produces more efficient schedule solution than other multi-objective Particle Swarm Optimization (PSO).