Resource Allocation Mechanism with New Models for Grid Environment

<p class="MsoNormal" style="margin: 0cm 0cm 0pt; mso-layout-grid-align: none;"><span style="color: black; font-size: 9pt; mso-bidi-font-weight: bold;"><span style="font-family: Times New Roman;">Selecting the appropriate resources for the particular task is one of major challenging work in the computational grid. The major objective of resource allocation in grid is effective scheduling of tasks and in turn the reduction in execution time. Hence the resource allocation must consider some specific characteristics of the resources, tasks and then decide the metrics to be used accordingly. Ant algorithm, which is one of the heuristic algorithm suits well for the allocation and scheduling in grid environment. In this paper, a Grid Resource Allocation based on Ant Algorithm (GRAAA) is proposed. The simulation result shows that the proposed algorithm is capable of producing high quality allocation of grid resources to tasks. </span></span>

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

Workflow technology has been adopted in scientific domains to orchestrate and automate scientific processes in order to facilitate experimentation. Such scientific workflows often involve large data sets and intensive computation that necessitate the use of the Grid, which offers supercomputing power through shared distributed resources. To execute a scientific workflow in the Grid, tasks within the workflow that represent steps in the scientific process are assigned to Grid resources for execution. To ensure efficient execution of the workflow, Grid workflow scheduling is required to manage the allocation of Grid resources. Although many Grid workflow scheduling techniques exist, they are mainly designed for the execution of a single workflow. This is not the case with parameter sweep workflows which focus on parametric study and parameter optimisation. A parameter sweep workflow is executed numerous times with different input parameters in order to determine the effect of each parameter combination on the experiment. While executing multiple instances of a parameter sweep workflow in parallel can reduce the time required for the overall execution, this execution introduces new challenges to Grid workflow scheduling. Not only is a scheduling algorithm that is able to manage multiple workflow instances required, but this algorithm also needs the ability to schedule tasks across multiple workflow instances judiciously, as tasks may require the same set of Grid resources. Without appropriate resource allocation, resource competition problem could arise. In the thesis, we propose a new Grid workflow scheduling technique for parameter sweep workflow called the Besom scheduling algorithm. The scheduling decision of our algorithm is based on the resource dependencies of tasks in the workflow, as well as conventional Grid resource-performance metrics. In addition, the proposed technique is extended to handle loop structures in scientific workflows without using existing loop-unrolling techniques. We evaluate the Besom algorithm under a variety of conditions. A comparison between the simulation results of the Besom algorithm and of the three existing Grid workflow scheduling algorithms shows that the Besom algorithm is able to perform better than the existing algorithms for workflows that have complex structures and that involve overlapping resource dependencies of tasks. The Besom scheduling algorithm advances the ability to schedule parallel execution of parameter sweep workflows in the Grid. The outcomes of this thesis justify continuing research in this area to increase our understanding of scheduling multiple Grid workflow instances and to provide support to those involved in parametric study and scientific workflow management.

Parallel programs that require sizeable computational electricity increasingly depend on grid computing structures. Efficient, helpful resource discovery algorithms are critical for optimizing resource allocation and minimizing execution time in these environments. This look presents a unique hierarchical and weighted resource discovery algorithm designed to decorate resource distribution while decreasing communique costs among grid nodes. A behavioural modelling technique systematically establishes the weighted resource discovery algorithm's accuracy and effectiveness. The behavioural model is carried out using StarUML. At the same time, the NuSMV version checker is hired to verify essential residences along with reachability, equity, and impasse-free operation of the resource discovery procedure. Critical overall performance metrics, including the quantity of inspected nodes consistent with request and the frequency of re-discovery operations, are used to evaluate the rules' efficiency and flexibility. The weighted resource discovery algorithm also evaluates the efficiency of finding loose resources with high-bandwidth connections, optimizing overall grid resource allocation. In addition to enhancing resource localization, the observation introduces resource facts tables, which store information crucial for powerful, proper resource allocation. This study aims to develop grid computing competencies by addressing resource discovery challenges. The hierarchical shape and weighted valid resource selection decorate valid resource inspection, adaptability, and high-bandwidth utilization. Behavioural modelling and formal verification verify the algorithm's accuracy and applicability in grid environments. By using weighted resource discovery and resource information tables, this study drastically improves resource discovery's performance and effectiveness in grid computing, optimizing overall performance and proper resource allocation.

Grid enables resource sharing and dynamic allocation of computational resources. It is a great challenge to make numerous resources available on-demand to guarantee the Quality-of-Service for jobs. This paper presents a two-stage optimization model for resource allocation in grid. Job constraints are classified into mandatory constraints and negotiated constraints. In the first stage, a preprocessing procedure such as resource discovery deals with the mandatory constraints. This has been fulfilled as our previous work. In the second stage, negotiated constraints are treated as Knapsack Problem-based optimization problem. Centralized scheduling and decentralized scheduling have been considered in this paper. This work is fulfilled as part of the Constellation Model for grid resource management. We formulate centralized scheduling as Multi-Constraint Multiple Knapsack Problem (MCMKP). In centralized scheduling, jobs are submitted to a global job queue. A global scheduler assigns each job to a proper grid site according to the scheduling strategy. The scheduling is done periodically (e.g. daily or weekly). We formulate decentralized scheduling as Multi-Dimensional Knapsack Problem (MDKP). In decentralized scheduling, jobs are submitted to local job queues. Allocation decisions are made by local schedulers individually. Jobs that can not be executed immediately are sent to a global waiting queue. When local scheduling is initialized, a local scheduler can select jobs from both the local job queue and the global waiting queue. Objectives of both centralized scheduling and decentralized scheduling are to optimize the utility defined by a grid economy approach. The defined utility makes the trade-offs between user-concerned metrics and system-concerned metrics. Heuristic algorithms such as Very Large-Scale Neighborhood Search proposed by R. K. Ahuja and C. B. Cunha [2005] can be used to solve the combinatorial optimization problem. We implemented a prototype of the Constellation Model. Genetic algorithms for constrained optimization which is proposed by S. Venkatraman and G. G. Yen [2005] have been implemented to solve the above optimization problems. Experimental results show that, performance metrics such as gained utility, response rate, and resource utilization are improved and resources are allocated in an optimal way.

The aim of this paper has been to introduce a novel market based algorithm for grid resource allocation and resolve some incompetence with current mechanisms. Grid resource allocation could be considered as a double auction in which grid resource manager acts as an auctioneer and jobs and resource owners act as buyers (resource consumers) and sellers. In our approach resource allocation is based on activity history of each participant in the auction. Bids (Asks) in the auctions are calculated in each round, based on proportion of former bids (asks) fluctuation and prior activity of each buyer(seller). Practical evaluation results demonstrate that market equilibrium in our mechanism is improved in comparison with other mechanisms. Also essence of learning features in proposed mechanism increases competition incentive in participants which do selfishly in every round of an auction to maximize their utility.

The computational grid helps in faster execution of compute intensive jobs. Many characteristic parameters are intended to be optimized while making resource allocation for job execution in computational grid. Most often, the green energy aspect, in which one tries for better energy utilization, is ignored while allocating the grid resources to the jobs. The conventional systems, which propose energy efficient scheduling strategies, ignore other Quality of Service parameters while scheduling the jobs. The proposed work tries to optimize the energy in resource allocation to make it a green energy model. It explores how effectively the jobs submitted to the grid can be executed for optimal energy uses making no compromise on other desired related characteristic parameters. A graph theoretic model has been developed for this purpose. The performance study of the proposed green energy model has been experimentally evaluated by simulation. The result reveals the benefits and gives an insight for an energy efficient resource allocation.

The ability to harness heterogeneous, dynamically available grid resources is attractive to typically resource-starved computational scientists and engineers, as in principle it can increase, by significant factors, the number of cycles that can be delivered to applications. However, new adaptive application structures and dynamic runtime system mechanisms are required if we are to operate effectively in grid environments. To explore some of these issues in a practical setting, the authors are developing an experimental framework, called Cactus, that incorporates both adaptive application structures for dealing with changing resource characteristics and adaptive resource selection mechanisms that allow applications to change their resource allocations (e.g., via migration) when performance falls outside specified limits. The authors describe the adaptive resource selection mechanisms and describe how they are used to achieve automatic application migration to “better” resources following performance degradation. The results provide insights into the architectural structures required to support adaptive resource selection. In addition, the authors suggest that the Cactus Worm affords many opportunities for grid computing.

To solve the problem of low resource utilization in power wireless access networks and guarantee the QoS of power services effectively, a resource-aware and cross-layer resource allocation mechanism for power wireless access network is proposed. First of all, according to the characteristics of power wireless access network, a virtualized system model based on SDN is proposed. The underlying resources are virtualized and abstracted. The resources state of physical layer and the different QoS of service layer are perceived by SDN controller. Then, considering the factors of cost, network load and QoS constraints, the resource allocation model of power wireless access network is proposed. Finally, the resource-aware dynamic resource allocation algorithm is designed to solve the optimal resource allocation of wireless access network under the condition of satisfying QoS requirements. The simulation results show that the resource-aware and cross-layer resource allocation mechanism proposed in this paper improves network resource utilization and significantly improves network operation efficiency under the premise of meeting QoS requirements of power services.

Sun, G.; Chen, H., and Mao, Y., 2020. The efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 691–695. Coconut Creek (Florida), ISSN 0749-0208.With the continuous development of marine technology, the tasks of marine survey, exploration, navigation, transportation and development are becoming more and more serious. It is necessary to pay attention to the problem of planning resource allocation in coastal areas, so as to design a more reasonable resource allocation scheme. The traditional analysis model can only converge locally, resulting in the deviation of the analysis results of resource allocation efficiency. Therefore, the construction method of the efficiency analysis model of technological innovation resource allocation of coastal enterprises based on genetic algorithm is proposed. According to the relationship between fuzzy operator and fuzzy scale, this method calculates the carrying capacity of coastal environmental resources. In light of the prediction results of the dynamic demand forecast function of emergency resources, it sets the evaluation weight of the efficiency of technological innovation resource allocation of coastal enterprises. Based on the global convergence characteristics of genetic algorithm, it realizes the construction of the efficiency analysis model of technological innovation resource allocation of coastal enterprises. The experimental results show that compared with the traditional method, the model based on genetic algorithm has stronger convergence and can achieve global data convergence. It can be seen that the analysis results of this model accord with the reality and can be applied to the analysis of resource allocation efficiency at this stage.

In this paper, an approach based on a fuzzy hybrid Petri net model is proposed to solve the resource allocation problem of Workflow Management Systems. Hybrid resource allocation mechanisms are modeled by hybrid Petri net with discrete transitions where discrete resources represent equipment and continuous resources represent employees availability. To express in a more realistic way the resource allocation mechanisms when human behavior is considered, fuzzy sets delimited by possibility distributions of the triangular form are associated with the marking of the places which represent human availability. New firing rules and the fuzzy marking evolution of the new model are defined. Such a fuzzy resource allocation model is applied to an example of resource allocation mechanism of a Handle Complaint Process.

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Next generation wireless networks are expected to handle an exponential increase in demand for capacity generated by a collection of tenants and/or services with heterogeneous requirements. Multi-tenant network sharing, enabled through virtualization and network slicing, offers the opportunity to reduce operational and deployment costs, and the challenge of managing resource allocations among multiple tenants serving possibly mobile diverse customers. When designing shared radio resource allocation mechanisms, it is as important to provide tenants with customization and isolation guarantees, as it is to achieve high resource utilization and to do so via low complexity and easy to implement algorithms. This thesis is devoted to the design and analysis of resource allocation mechanisms that meet these objectives. We propose a sharing model in which tenants are assigned a share/budget of a pool of network resources. This share is then redistributed in the form of weights amongst users, which in turn drive dynamic resource allocations which are partially able to adapt to the traffic demands on, and requirements of, different slices customer populations. We propose and analyze two approaches for redistributing slices’ share among customers which we classify into their associated (i) cooperative, and (ii) competitive resource allocations. In the cooperative resource allocation setting, a pre-established policy is proposed, in which resources are eventually assigned in proportion to the slice’s share and the relative number of active users in currently has at a resource. This is shown to be socially optimal in a particular setting and simple to implement, with statistical multiplexing gains that increase with the number of tenants and the size of the resource pool. These gains stem from the ability of the scheme to adapt to dynamic loads leading to an up to 50% network capacity savings with respect to static allocations. We further improve these gains by presenting a framework that combines resource allocation and wireless user association which uses limited computational, information, and handoff overheads. However, using our cooperative scheme over a large pool of resources restricts the degree to which a slice can differentiate its customers’ performance at a per resource level. Thus, we study how this trade-off affects the network utility and propose a mechanism to determine an optimal partition the resources into a collection of self-managed pools under cooperative resource allocations. Our competitive resource allocation approach enables tenants to reap the performance benefits of sharing while retaining the ability to customize their own users’ allocations. This setting results in a network slicing game in which each tenant reacts to the user allocations of the others so as to maximize its own customers’ utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same, or better, utility than it could achieve under a static partitioning of resources, hence providing the same level of inter-slice protection as static resource partitioning. The network utility of the equilibrium allocations is shown to be, under mild conditions, close to the socially optimal ones. The competitive resource allocation framework is complemented with a study on admission control policies that enable tenants to ensure minimum rate guarantees to their users. Our analysis and extensive simulation results confirm that our framework provides a comprehensive practical solution towards multi-tenant network slicing. We also discuss how our theoretical results fill a gap in the general resource allocation literature for strategic players.

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.