93 research outputs found
The Impact of Data Replicatino on Job Scheduling Performance in Hierarchical data Grid
In data-intensive applications data transfer is a primary cause of job
execution delay. Data access time depends on bandwidth. The major bottleneck to
supporting fast data access in Grids is the high latencies of Wide Area
Networks and Internet. Effective scheduling can reduce the amount of data
transferred across the internet by dispatching a job to where the needed data
are present. Another solution is to use a data replication mechanism. Objective
of dynamic replica strategies is reducing file access time which leads to
reducing job runtime. In this paper we develop a job scheduling policy and a
dynamic data replication strategy, called HRS (Hierarchical Replication
Strategy), to improve the data access efficiencies. We study our approach and
evaluate it through simulation. The results show that our algorithm has
improved 12% over the current strategies.Comment: 11 pages, 7 figure
Formal Aspects of Grid Brokering
Coordination in distributed environments, like Grids, involves selecting the
most appropriate services, resources or compositions to carry out the planned
activities. Such functionalities appear at various levels of the infrastructure
and in various means forming a blurry domain, where it is hard to see how the
participating components are related and what their relevant properties are. In
this paper we focus on a subset of these problems: resource brokering in Grid
middleware. This paper aims at establishing a semantical model for brokering
and related activities by defining brokering agents at three levels of the Grid
middleware for resource, host and broker selection. The main contribution of
this paper is the definition and decomposition of different brokering
components in Grids by providing a formal model using Abstract State Machines
Towards a Framework for Managing Inconsistencies in Systems of Systems
The growth in the complexity of software systems has led to a proliferation of systems that have been created independently to provide specific functions, such as activity tracking, household energy management or personal nutrition assistance. The runtime composition of these individual systems into Systems of Systems (SoSs) enables support for more sophisticated functionality that cannot be provided by individual constituent systems on their own. However, in order to realize the benefits of these functionalities it is necessary to address a number of challenges associated with SoSs, including, but not limited to, operational and managerial independence, geographic distribution of participating systems, evolutionary development, and emergent conflicting behavior that can occur due interactions between the requirements of the participating systems. In this paper, we present a framework for conflict management in SoSs. The management of conflicting requirements involves four steps, namely (a) overlap detection, (b) conflict identification, (c) conflict diagnosis, and (d) conflict resolution based on the use of a utility function. The framework uses a Monitor-Analyze-Plan- Execute- Knowledge (MAPE-K) architectural pattern. In order to illustrate the work, we use an example SoS ecosystem designed to support food security at different levels of granularity
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Business Grid Services
Grid services have come to represent the synthesis of web services and grid computing paradigms. Web services provide the means to modularize software, enabling loosely coupled and novel synthesis. Grid computing removes the binding between functional software components and specific hosting hardware, enabling software to be deployed dynamically over a network (e.g. intra-, extra- or inter-net). Applying the constructs of grid computing to the service orientation of enterprise software will allow business service networks to utilize more specialized services. An upper service ontology that enables business grid services to be described and then related to the grid hosting platform is presented. Explicit knowledge is required for enterprise software, hosting servers and the domain that can then be utilized by both SLA and reservation systems. The ontology presented is derived from and validated using a collection of web services taken from leading investment banks
A Multi-Objective Fuzzy Evolutionary Algorithm for Job Scheduling on Computational Grids
Scheduling jobs in grid computing is a challenging task. The job scheduling is a process of optimization of resource allocation for job completion in a optimum amount of time. There are various solutions like using dynamic programming, evolutionary algorithms etc., in literature. However, till date, no algorithm is found to be the best. This paper attempts a new job shop scheduling problem using a recent JAYA optimization algorithm. This work proposes a fuzzy based JAYA algorithm to minimize the makespan of the selected job scheduling problem. The main feature proposed is its simplicity due to the simple JAYA algorithm compared to other existing evolutionary algorithms. Experiments are conducted on four different data sets and the results are compared with other evolutionary and fuzzy based evolutionary algorithms. The proposed fuzzy based JAYA produced compatible results in terms of average makespan, flowtime and fitness
A Secured Model for Resource Access in Grid Environment
Grid computing provides a way to execute applications over autonomous, distributed and heterogeneous nodes. The main goal of grid technology is to allow sharing of resources and services under a set of rules and policies, which govern the conditions for access to the resources. This paper reviews the state of security and access control for resources in grid environment and presents a secured model for resource access in grid environment
Grid Global Behavior Prediction
Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach
Credibility-Based Binary Feedback Model for Grid Resource Planning
In commercial grids, Grid Service Providers (GSPs) can improve their profitability by maintaining the lowest possible amount of resources to meet client demand. Their goal is to maximize profits by optimizing resource planning. In order to achieve this goal, they require an estimate of the demand for their service, but collecting demand data is costly and difficult. In this paper we develop an approach to building a proxy for demand, which we call a value profile. To construct a value profile, we use binary feedback from a collection of heterogeneous clients. We show that this can be used as a proxy for a demand function that represents a client’s willingness-to-pay for grid resources. As with all binary feedback systems, clients may require incentives to provide feedback and deterrents to selfish behavior, such as misrepresenting their true preferences to obtain superior services at lower costs. We use credibility mechanisms to detect untruthful feedback and penalize insincere or biased clients. Finally, we use game theory to study how cooperation can emerge in this community of clients and GSPs
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