A Resource Scheduling Model Supporting Differentiated Service for Grid Computing

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

The aim of this thesis is to design and implement a new Grid Resource Management methodology, where non-massive resources owners would be able to share their resources and integrate human collaboration across multiple domains regardless of network technology, operative platform or administrative domain.<br/>This thesis proposes a distributed and heuristic policy-based resource management architecture for large-scale Grids. The resource management architecture proposed herein is composed of four main building blocs: services management, resource discovery and monitoring, resource scheduling and jobs allocation and activation. The Grid Services Management (GSM) and Jobs Allocation and Activation (JAA) are supported by means of a Policy-based Grid Resource Management Architecture (PbGRMA). This architecture is able to identify service needs arising from diverse sources during the deployment and management of Grid Services, such as requirements demanded by customers, applications and network conditions. Afterwards, the PbGRMA merges these requirements into deployment policies for the corresponding Grid Services. The Grid Resource Discovery and Monitoring (GRDM) is supported by the introduction of the SNMP-based Balanced Load Monitoring Agents for Resource Scheduling (SBLOMARS), in which network and computational resources are monitored by distributed agents. This allows for a flexible, heterogeneous and scalable monitoring system. The Grid Resource Scheduling (GRS) is based on the Balanced Load Multi-Constrained Resource Scheduler (BLOMERS). This heuristic scheduler represents an alternate way of solving the inherent NP-hard problem for resource scheduling in large-scale distributed networks by means of the implementation of a Genetic Algorithm.<br/>Finally, based on the outcome of both the GRDM and GRS, the PbGRMA allocates the corresponding Grid Services by means of its interfaces with Globus ToolKit Middleware and Unix-based CLI commands along of any large-scale Grid Infrastructure. <br/>The synergy obtained by these components allows Grid administrators to exploit the available resources with predetermined levels of Quality of Service (QoS), reducing computational costs and makespan in resource scheduling while ensuring that the resource load is balanced throughout the Grid. The makespan of a schedule is the time required for all jobs to be processed when no one job could be interrupted during its execution and each node can perform at most one operation at any time.<br/>This new approach has been successfully tested in a real large-scale scenario such as Grid5000. The results presented along this Thesis show that our general solution is a reliable, flexible and scalable architecture to deploy and manage Grid Services in large-scale Grid Infrastructures. Moreover, the substitution of the heuristic algorithm approach used into the Grid Resource Scheduling (GRS) phase by other non-heuristics selection algorithms could make our solution useful in smaller Grid Infrastructures.

\n\t\t\t\t\tThis paper addresses the problem of resource scheduling in a grid computing environment. One of the main goals of grid computing is to share system resources among geographically dispersed users, and schedule resource requests in an efficient manner. Grid computing resources are distributed, heterogeneous, dynamic, and autonomous, which makes resource scheduling a complex problem. This paper proposes a new approach to resource scheduling in grid computing environments, the hierarchical stochastic Petri net (HSPN). The HSPN optimizes grid resource sharing, by categorizing resource requests in three layers, where each layer has special functions for receiving subtasks from, and delivering data to, the layer above or below. We compare the HSPN performance with the Min-min and Max-min resource scheduling algorithms. Our results show that the HSPN performs better than Max-min, but slightly underperforms Min-min.\n\t\t\t\t

During the transient period after a link failure the network cannot guarantee the agreed service levels to user data. This is due to the fact that forwarding tables in the network are inconsistent. Moreover, link states can inadvertently be advertised wrong due to protocol time outs, which may result in persistent route flaps. Reducing the probability of wrongly advertised link states, and the time during which the forwarding tables are inconsistent, is therefore of eminent importance to provide consistent and high level QoS to user data. By queuing routing traffic in a queue with strict priority over all other (data) queues, i.e. assigning the highest priority in a Differentiated Services model, we were able to reduce the probability of routing data loss to almost zero, and reduce flooding times almost to their theoretical limit. The quality of service provided to user traffic was considerable higher than without the proposed modification. The scheme is independent of the routing protocol, and can be used with most differentiated service models. It is compatible with the current OSPF standard, and can be used in conjunction with other improvements in the protocol with similar objectives.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

We consider the management of FIFO buffers for network switches providing differentiated services. In each time step, an arbitrary number of packets arrive and only one packet can be sent. The buffer can store a limited number of packets and, due to the FIFO property, the sequence of sent packets has to be a subsequence of the arriving packets. The differentiated service model is abstracted by attributing each packet with a value according to its service level. A buffer management strategy can drop packets, and the goal is to maximize the sum of the values of sent packets.For only two different packet values, we introduce the account strategy and prove that this strategy achieves an optimal competitive ratio of \(\sqrt{2}-(\sqrt{5+4\sqrt{2}}-3)/2\approx 1.282\) if the buffer size tends to infinity and an optimal competitive ratio of \((\sqrt{13}-1)/2\approx 1.303\) for arbitrary buffer sizes. For general packet values, the simple preemptive greedy strategy (PG) is studied. We show that PG achieves a competitive ratio of \(\sqrt{3}\approx 1.732\) which is the best known upper bound on the competitive ratio of this problem. In addition, we give a lower bound of \(1+1/\sqrt{2}\approx 1.707\) on the competitive ratio of PG which improves the previously known lower bound. As a consequence, the competitive ratio of PG cannot be further improved significantly.

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 has reached the core of the digital ecosystems and provides high-availability and scale-able access to the services of computational services. Nevertheless, the active resource requirements, enforcement of service-level agreement (SLA), energy usage restrictions, and a variation in the cost-performance represent significant challenges to the cloud resource management. The conventional heuristic-based schedulers in clouds orchestrators like Kube and OpenStack fail to adjust to real-time workload variability, fractional resources, violation of SLA and inflated operational costs frequently occur. The adaptive, reward-driven decision-making properties of reinforcement learning (RL) have been discovered to be an interesting alternative to a heuristic policy. However, centralized RL has scalability and state observability scale in distributed clouds. As a way of overcoming these limitations, this study hypothesizes a Multi-Agent Reinforcement Learning (MARL) model of autonomous resource scheduling and optimization in cloud infrastructures. The offered structure suggests the use of decentralized intelligent agents executing a coordinated management of computational, storage, and network allocations. The agents are optimized to adapt local behaviors under environment feedback with the aid of cooperative communication to enhance global optimization. The system uses hybrid in reinforcement system consisting of Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) to trade between exploration, stability, and on-policy improvement. The goal of an adaptive reward model is to maximize important performance indicators (KPIs): SLA adherence, task completion latency, energy efficiency and balance resource utilization. The Cloud sim Plus experimental measurements were performed in a simulated multi-cluster environment and supported by extra features of container virtualization. The proposed version of the MARL scheduler showed a better throughput (18.7%), SLA violations reduction (27.4%) and energy consumption (14.3) than the baseline strategies like Round-Robin, Min-Min, and centralized DQN models. Convergence time was reduced and decision correlation under the workload bursts was improved due to the incorporation of the inter-agent communication. The research paper has a tremendous impact in the field by proposing a foresight, smart cloud orchestration framework that has the capability to adapt itself in the distributed large scale systems. Additionally, we offer an improvement in algorithms, performance analysis, system workflow optimization, and theoretical support of a cooperative reinforcement learning in clouds. The implication of these findings in the future is that the applicability of the research to edge and fog computing settings can be applied to Industry 5.0 automation

Virtual computer-integrated manufacturing (VCIM) is a promising manufacturing strategy in today’s global market because it is capable of exploiting distributed resources effectively, both locally as well as globally. Resource scheduling is critical to the operation of VCIM systems. In this article, a novel model for resource scheduling in VCIM systems is developed. By integrating collaborative transportation scheduling into manufacturing resource scheduling, the proposed model can provide a better solution because of reduction in the fixed transportation cost. Performance of the developed model is verified by computer simulation for a case study producing computer hard disks. The proposed model serves as a fundamental step towards global optimisation of VCIM systems.

As Grid technologies evolve quickly on Internet, research related to resource scheduling faces new opportunities and challenges. These new technologies, ideas and approaches provide a new environment for researching and developing the economy-based resource scheduling system. Aiming at the hierarchical Grid model, following utility optimization thinking, while an universal flexible utility function is provided for addressing the Grid users’ QoS requirements of deadline and budget, this paper proposes a new Grid resource scheduling algorithm, which can not only increase the utilization of resources and system throughput, but also realize cost-time optimization between resource providers and users within Grid systems. On the basis of having described the different functional parts of the proposed algorithm, two kinds of simulative experiments about completion time and cost of tasks are conducted. Finally, the experiment results show that the proposed algorithm is effective.

Performance evaluation of grid resource management and scheduling system is of key importance to the success of the grid. In this paper, we introduce a generalized stochastic Petri net (GSPN) model for such purpose. The model is simple with clarity and can be applied to other research issues on grid to provide them with performance evaluation capabilities.

Grid resource management and scheduling are confronted with a great challenge. This paper presented a dynamic resource price-adjusting (RPA) strategy in computational Grid. Three new concepts and two new evaluation standards are defined. On the basis of these theories, a Grid resource scheduling algorithm based on marker-driven using dynamic RPA strategy is proposed. With the help of GridSim toolkit, the simulation of resource scheduling algorithm proposed in the paper is realized, and the new algorithm is compared with GridSim DBC (deadline and budget constrained) scheduling algorithms. The experimental results show that the new algorithm improves the performance of Grid in aspect of increasing the task accomplishment ratio and solving the problem of load imbalance.

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

In order to resolve resource management and scheduling problem in Manufacturing Grid (MG) – an application of Grid technology, we develop a resource management and scheduling system with the interaction of Manufacturing Grid Information Service (MGIS) and the Manufacturing Grid Resource Scheduler (MGRS). The former, MGIS, provides fundamental mechanisms for remote resource encapsulating, registration, and monitoring, and the latter, MGRS, performs scheduling roles as Global Process Planning (GPP) analyzing, resource discovery, resource selection, and resource mapping.

Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.

<p>Cloud manufacturing is a grand manufacturing concept aimed at reducing the waste of manufacturing resources. By borrowing the ideas of cloud computing and utilizing information technology to achieve high sharing of manufacturing resources, the core issues that both the supply and demand sides are concerned about in the cloud manufacturing process are price, product quality, and product production cycle. Therefore, this paper constructs a manufacturing process resource scheduling model that includes three elements. In order to solve the optimal solution of the model, an improved intelligent algorithm is used to solve it. The intelligent algorithm is based on the advantages of the gray wolf algorithm in solving accuracy and convergence speed. The integration of the Bat algorithm in the gray wolf algorithm improves the search ability and performance of the original gray wolf algorithm. At the same time, the gray wolf algorithm can avoid the convergence speed of the Bat algorithm from changing with the increase of iteration times. The phenomenon of slow or almost stopping. To avoid generating local optimal solutions, the accuracy of the cloud resource scheduling model and algorithm solution constructed in this paper were verified through the construction of a simulation environment. </p> <p> </p>