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

Ad hoc grids allow a group of individuals to accomplish a mission that involves computation and communication among the grid components, often without fixed structure. In an ad hoc grid, every node in the network can spontaneously arise as a resource consumer or a resource producer at any time when it needs a resource or it possesses an idle resource. At the same time, the node in ad hoc grid is often energy constrained. The paper proposes an efficient resource allocation scheme for grid computing marketplace where ad hoc grid users can buy usage of memory and CPU from grid resource providers. The ad hoc grid user agents purpose to obtain the optimized quality of service to accomplish their tasks on time with a given budget, and the goal of grid resource providers as profit-maximization. Combining perspectives of both ad hoc grid users and resource providers, the paper present ad hoc grid resource allocation algorithm to maximize the global utility of the ad hoc grid system which are beneficial for both grid users and grid resource providers. Simulations are conducted to compare the performance of the algorithms with related work.

The paper is targeted at concerning pricing approach for grid resource scheduling with QoS guarantees. The two major parties in Grid, namely, resource consumers who submit various applications, and resources providers who share their resources, usually have different motivations when they join the Grid. These incentives are presented by objective functions in scheduling. Economic grid QoS scheduling algorithm adopt both application-centric and resource-centric scheduling objective function aim to optimize the performance of each individual grid user and the performance of the grid resources. The economic cost and profit are considered by grid users and resource providers respectively. Objective functions and QoS scheduling optimization algorithms are proposed. QoS scheduling optimization is decomposed into two sub optimizations of grid user's cost and grid resource provider's profit by utilizing dynamic programming. QoS scheduling algorithm is an interactive process in economic market coordinating grid users and grid resource providers to achieve optimal QoS scheduling. Experiments are to compare the proposed economic QoS scheduling with three partial optimisation approaches, which only consider one criterion.

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.

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.

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.

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.

The article considers the resource allocation and scheduling problem in a grid computing environment. The article proposes system optimisation scheduling (SOS) that provides a potential solution of joint optimisation of objectives for both the resource and application layer, which combine both application-oriented and resource-oriented scheduling benefits. Grid systems will strive to find an optimal relation between user satisfaction and resource utilisation. Utility functions are used to express grid user's Quality of Service requirement, resource provider's benefit function and system's objectives. In order to verify the efficiency of the proposed scheduling algorithm, we compare the performance of application optimisation scheduling, resource optimisation scheduling, SOS with a traditional Round-Robin algorithm. The simulations study the effect of the request rate and task-to-resource ratio on the different scheduling algorithm.

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.

\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

SummaryIn today's highly parametrized distributed computational environments, such as green grid clusters and clouds, the growing power and cooling rates are becoming the dominant part of the users' and system managers' budgets. Computational grids, owing to their sheer sizes, still require advanced methodologies and strategies for supporting the scheduling of the users' tasks and applications to the distributed resources. The efficient resource allocation becomes even more challenging when energy utilization, beyond the conventional scheduling criteria, such asMakespan, is treated as first‐class additional scheduling objective. In this paper, we address the independent batch scheduling in computational grid as a bi‐objective global minimization problem withMakespanandenergy consumptionas the main criteria. We apply thedynamic voltage and frequency scalingmodel for the management of the cumulative power energy utilized by the grid resources. We develop three genetic algorithms as energy‐aware grid schedulers, which were empirically evaluated in three grid size scenarios in static and dynamic modes. The simulation results confirmed the effectiveness of the proposed genetic algorithm‐based schedulers in the reduction of the energy consumed by the whole system and in dynamic load balancing of the resources in grid clusters, which is sufficient to maintain the desired quality level(s). Copyright © 2012 John Wiley & Sons, Ltd.

Grids are complex systems that aggregate large amounts of distributed computational resources to perform large scale simulations and analysis by multiple research groups. In this paper we unveil its social networks: actors that participate in a Grid and relationships among those Grid actors. Social networking information can be used as a means to increase awareness and to facilitate collaboration among Grid participants. In practice we have implemented a social networking tool so that Grid actors can discover partners to collaborate, potential providers and consumers, and their referrals path. We present and discuss the evaluation of such tool in a user study performed with Grid resource consumers and providers.

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 performance of computational grids mainly depends on the resource allocation service of a resource management system. Efficient resource allocation is essential for better resource utilisation which could be for both providers and grid users. Resource allocation includes the scheduling of gridlets to the available resources. However, the biggest challenges for grid users are to select the best resources from the available grid resources and to allocate these resources for scheduling of the gridlets. To address these issues and enhance the resource utilisation process, we propose a semantic and proximity-aware fuzzy rule-based model that improves the resource utilisation in a grid environment. The model uses fuzzy techniques with four parameters such as semantic similarity, proximity, number of total machines and number of total processors of each machine. The experimental results provide promising results. Overall, the proposed semantic and proximity-aware fuzzy rule-based decentralised resource discovery model improves the resource utilisation by 23% as compared to non-fuzzy first come first serve (FCFS) technique in a computational grid environment.

Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

Grid resource providers can use gossiping to disseminate their available resource state to remote regions of the grid to attract application load. Pairwise gossiping protocols exchange information about limited subsets of other resources between pairs of potentially remote participants. In epidemic gossiping protocols, the provider disseminates information to multiple neighbors, who in turn forward it to their neighbors, and so on. One important metric for these protocols is their coverage, which characterizes how many and which resources receive the information. Coverage characteristics of epidemic protocols are non-uniform, concentrated within the vicinity of a disseminating node; they can exhibit bi-modal behavior where information either reaches distant nodes or dies out quickly. Pairwise gossiping protocols, on the other hand, provide a more uniform coverage, but it can take longer for the dissemination to reach desired uniformity. In this paper, we study performance characteristics of three gossiping protocols: (1) epidemic gossiping, (2) pairwise gossiping, and (3) adaptive information dissemination (which is based on a form of epidemic gossiping). We report experimental results based on our simulation framework that compare the three protocols in terms of packet overhead and query satisfaction rates. We show that pairwise gossiping protocols work best when resource distribution on the grid is uniform, but that they can be configured to perform well in support of grid scheduling.