Nonpreemptive Priority (NPRP) based Job Scheduling model for virtualized grid environment

Cloud Computing provides a Computing environment where different resources, infrastructures, development platforms and software are delivered as a service to customers virtually on pay per time basis. Low cost, scalability, reliability, utility-based computing are important aspects of cloud computing. Job scheduling is an essential and most important part in any cloud environment. With increasing number of users, Job scheduling becomes a strenuous task. Ordering the jobs by scheduler while maintaining the balance between quality of services (QoS), efficiency and fairness of jobs is quite challenging. Scheduling algorithms are implemented considering parameters such as throughput, resource utilization, latency, cost, priority, computational time, physical distances, performance, bandwidth, resource availability. Though there are different scheduling algorithms available in cloud computing, a very less comparative study has been done on performance of various scheduling algorithms with respect to above mentioned parameters. This paper aims at a comparative study of various types of job scheduling algorithms that provide efficient cloud services.

As more and more users outsource their job executions to service clouds, effective job scheduling and pricing models are needed to solve resource and service competitions between users. Considering the particularity of scheduling and pricing problems in a service cloud whose goal is generally social welfare maximization, current commonly used models, such as fixed-pricing schemes, have obvious shortcomings and thus are unfeasible. Therefore, in this paper, we propose a randomized mechanism to schedule and charge job executions in service clouds. Our proposed mechanism can schedule jobs in a flexible way to achieve approximate social welfare maximization while guaranteeing non-preemption. Flexibility means the number of instances which are allocated to a job can be changed over time. The mechanism is truthful in expectation, computationally efficient and individually rational. The theoretical analysis shows that our mechanism can achieve an expected social welfare approximation ratio α, which can be 2 in some situations. Extensive simulations show that our proposed mechanism can efficiently solve the job scheduling problem in service clouds.

Job scheduling is a fundamental issue in achieving high performance on the Grids. In this paper, a hierarchical job scheduling architecture has been proposed for the Grids. The stochastic high-level Petri net (SHLPN) model of the proposed architecture and the refined model are presented. The job scheduling algorithms for an incoming job to be distributed to a waiting queue of any processor in any node of the Grid architecture is integrated by the node-selection algorithm at the global scheduler, the job-dispatching algorithm at the local scheduler, and job-scheduling algorithm at the processor scheduler. The integrated job scheduling policy is investigated using SHLPN performance analysis methods. The performance metrics concerned include the delay time of jobs in the Grid node and the response time perceived by the users. The numerical example shows the efficiency of the performance evaluation technique.

Job scheduling under the cloud circumstance is the key techniques to impact on frequency of cloud calculation. Trust relationship is an important factor of cloud calculation and job scheduling. In order to satisfy the requirement of quality of cloud calculation service for job scheduling, this article introduced the term of trust mechanism and apply it to cloud calculation and job scheduling. It uses altered SFLA as scheduling strategy, and brings forward a model of job scheduling with cloud circumstance which is based on trust mechanism and SFLA. The simulation results showed that this algorithm with faster convergence speed can increase the trust benefit and accomplish job scheduling task well.

Grid Computing provides sharing of geographically distributed resources among large scale complex applications. Due to dynamic nature of resources in grid, there is a need of highly efficient job scheduling and resource management policies in grid. In this paper, a novel Grid Resource Scheduler (GRS) is proposed to effectively utilize the available resources in Grid. Proposed GRS contributes, an optimal job scheduling algorithm on Job Rank-Backfilling policy and a resource matching algorithm based on ranking of resources with best fit allocation model. Performance of GRS is measured by considering a web based BLAST algorithm -- a bioinformatics application -- . GRS aims in reducing; Make span of the job workflow and execution time of varied size jobs. The experimental results prove that proposed job scheduling and resource matching algorithms performs better when evaluated against widely used FCFS job scheduling and biggest free resource matching strategies.

Cloud computing is widely used in academia and industry. Users can provide jobs to cloud service providers that provide pay-as-you-go methods by cloud for calculation and obtaining results. Cloud service providers make reasonable scheduling for different jobs of users and in the case of successful operation, the cost of energy consumption becomes a key factor. Prior works proposed various algorithms based on reinforcement learning on job scheduling. However, due to the lack of online training mode and refined container decision-making, this paper makes some improvements and extensions. This paper presents the algorithm ACT4JS, an actor-critic with transformer for three stage job scheduling algorithm, that finely manages data centers, server nodes, and application containers. The job scheduling algorithm is designed to select the best long-term decision by learning from cloud computing resource model that accepts single-task multi-instance jobs and directed acyclic graph jobs with dependencies. To learn key features and loss optimization, use the transformer network structure and the proximal policy optimization method. To minimize energy consumption costs for large scale job scheduling and running, designs a reward function to reflect optimized energy consumption targets and independent simulation experiments. To achieve low energy consumption cost and low task reject rate, use training methods such as experience replay and advantage estimate. Compared with the mainstream baseline algorithm, the results show that the ACT4JS algorithm is better than the baseline in the online training model. In addition, the effect of ACT4JS is more effective in the long-term experimental.

Job scheduling in grid computing is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. The main scope of this article is to propose a new Ant Colony Optimization (ACO) algorithm for balanced job scheduling in the Grid environment. To achieve the above goal, we will indicate a way to balance the entire system load while minimizing the makespan of a given set of jobs. Based on the experimental results, the proposed algorithm confidently demonstrates its practicability and competitiveness compared with other job scheduling algorithms.

Aiming at realizing a high performance computing (HPC) cloud with vector supercomputers, this paper presents the world’s first prototype of wide-area vector meta computing infrastructure named a vector computing cloud by virtualizing remote vector computing resources as an HPC service over the Internet. The prototype system consists of two remote NEC SX-9 nodes connected through a long fat-pipe network (LFN), and each node is located at Tohoku University and Osaka University with a distance of 800 km. The vector computing cloud also provides a single sign on environment and jobs are automatically assigned to appropriate sites. Wide-area co-operation of distributed vector supercomputers is realized by adopting the NAREGI Grid Middleware, and a virtual machine for NEC SX vector supercomputer series, job scheduling algorithms, and an MPI operating environment are newly developed to enhance the job and resource management capabilities of the NAREGI Grid Middleware. In addition, to achieve fairness and efficient job scheduling on the vector computing cloud, this paper presents a history-based job scheduling mechanism for a queue system. Based on the estimation, the job scheduling mechanism automatically allocates the job to an appropriate site, which can execute the job earlier. The operation tests and experiment results indicate that the prototype system realizes single sign on for multiple vector resources and has enough potential for transparently operating jobs between the two SX-9 systems. This paper also evaluates and discusses the performance of the proposed job scheduling mechanism and MPI operation between both SX-9 systems using the High Performance Linpack (HPL) benchmark.

Decentralized distributed systems, such as grids, clouds or networks of sensors, have been widely investigated recently. An important nature of such systems is the heterogeneity of their resources; in order to archive the availability, scalability and flexibility. As a consequence, managing the systems to meet requirements is obviously a nontrivial work. The issue is even more challenging in term of job scheduling when the task dependency within each job exists. In this paper, we address such problem of job scheduling, so called workflow-based job scheduling, in the decentralized distributed systems with heterogeneous resources. As such problem is proven to be an NP-complete problem, an efficient heuristic algorithm to address this problem is proposed. The algorithm is based on an observation that the heterogeneity of the resources can affect the execution time of the scheduling. We compare the effectiveness and efficiency of the proposed algorithm with a baseline algorithm. The result shows that our algorithm is highly effective and efficient for the scheduling problem in the decentralized distributed system with heterogeneous resources environment both in terms of the solution quality and the execution time respectively.KeywordsDecentralized algorithmHeterogeneous resourcesWorkflow-based job scheduling

With the rapid development of digital technology, from the application of traditional databases and scientific computing to the emerging cloud computing services, the analysis and processing of massive data has become the focus of society. Providing low-cost, scalable, and configurable shared cloud services to users on cloud service platforms is a new hotspot for the development of major cloud service providers. Job scheduling plays an important role in improving the overall system performance of cloud service capabilities. Simple job scheduling strategies (such as Fair and FIFO scheduling) do not consider job size and may degrade performance when jobs of different sizes arrive. This paper proposes the MQWAG (Multi-queue Load-Sensitive Greedy Scheduling Algorithm) job scheduling algorithm to reorder multi-queue jobs so that short jobs are executed preferentially in multiple queues. In our experiments, our algorithm shortened the average job completion time by about 26% compared with other algorithms.

With growing data volumes generated and stored across geo-distributed datacenters, it is becoming increasingly inefficient to aggregate all data required for computation at a single datacenter. Instead, a recent trend is to distribute computation to take advantage of data locality, thus reducing the resource (e.g., bandwidth) costs while improving performance. In this trend, new challenges are emerging in job scheduling, which requires coordination among the datacenters as each job runs across geo-distributed sites. In this paper, we propose novel job scheduling algorithms that coordinate job scheduling across datacenters with low overhead, while achieving near-optimal performance. Our extensive simulation study with realistic job traces shows that the proposed scheduling algorithms result in up to 50% improvement in average job completion time over the Shortest Remaining Processing Time (SRPT) based approaches.

Advanced reinforcement learning (RL) technologies have recently increased the opportunity for automating several tasks in cluster management at scale by exploiting repetitive logs of cluster operation and building a learning model for resource allocation and job scheduling. Yet, this trend of adopting RL in the domain of cluster management has not fully addressed the diversity and heterogeneity of jobs and machines in modern cluster environments. In this paper, we present an RL-based scheduler for a multi-resource cluster, namely SCARL (SCheduler with Attentive Reinforcement Learning), concentrating on intricate cluster operating conditions with different resource requirements and capabilities. Specifically, we employ attentive embedding and factored-action scheduling that together efficiently incorporate time-varying interdependency of jobs and machines in RL processing; they enable an end-to-end scalable policy for scheduling diverse jobs on heterogeneous machines. To the best of our knowledge, we are the first to employ attention mechanism in RL-based cluster resource management. Through experiments, we demonstrate that our approach is competitive with existing heuristic methods under various cluster simulation configurations, e.g., an average 9.2 % enhancement in slowdown over the shortest job first algorithm. Additionally, the approach yields stable performance with our test cluster for running synthetic workloads based on real traces.

The paper concerns the problem of run-time job scheduling in a multiprocessor control system. A fuzzy model of dynamic job scheduling is used for formalization and analysis of this problem. The algorithms for efficient load distribution among the processors are discussed.

Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy.

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.