Archive for the ‘IEEE Papers’ Category

Some IEEE papers on Grid Job scheduling.. worth to study

  1. Ant Colony Algorithm for Job Scheduling in Grid Computing.pdf
  2. A Secure Resource and Job scheduling Model with Job Grouping strategy in Grid Computing
  3. A Memory-Aware Dynamic Job Scheduling Model in Grid Computing
  4. G RID TS: A New Approach for Fault-Tolerant Scheduling in Grid Computing
  5. A New Resource Scheduling Model with Bandwidth Aware Job Grouping Strategy in Grid Computing
  6. Cost-effective Heuristics for Workflow Scheduling in Grid Computing Economy
  7. Adaptive Task Scheduling in Grid Computing Environments
  8. A Heuristic on Job Scheduling in Grid Computing Environment
  9. A Heuristic Scheduling Algorithm for Computing of Power Grid
  10. A Modified Simulated Annealing Algorithm for Static Task Scheduling in Grid Computing
  11. An adaptive grouping based Job Scheduling in Grid Computing Read more…
Categories: IEEE Papers, Uncategorized

Ant Colony Algorithm for Job Scheduling in Grid Computing

Ant Colony Algorithm for Job Scheduling in Grid Computing
Abstract – Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources. This will lead to resources having high workload and stagnation may occur if computational times of the processed jobs are high. This paper proposed an enhanced ant colony optimization algorithm for jobs and resources scheduling in grid computing. The proposed ant colony algorithm for job scheduling in the grid environment combines the techniques from Ant Colony System and Max – Min Ant System. The algorithm focuses on local pheromone trail update and the trail limit values. A matrix is used to record the status of the available resources. The agent concept is also integrated in this algorithm for the purpose of updating the grid resource table. Experimental results obtained showed that this is a promising ant colony algorithm for job scheduling in grid environment.

Essential matter : There are two types of scheduling

(1) Static scheduling: Where jobs are assigned to suitable resources before their execution begin.

(2) Dynamic Scheduling: Reevaluation of status of the resources is allowed even if jobs have been assigned to those resources, and can trigger job migration and interruption based on the status of the system and workload.

Jobs submitted to a grid computing system need to be processed by the available resources. Best resources in term of processing speed, memory and availability status are more likely to be selected for the submitted jobs during the scheduling process. Ant Colony algorithm has been used in this paper as an effective method for solving this scheduling issue.

Read more…

Categories: IEEE Papers

Toward a Synergy Between P2P and Grids

Authors:  Domenico Talia and Paolo Trunfio

P2P has emerged as a new trend in the internet and have got enormous media attention for two reasons:
• file sharing, in which peers share files with each other.
• highly parallel computing, in which an (inherently) parallel application runs on available nodes.

P2P model has emerged as a distributed paradigm because of its potential to harness the computing, storage and communication power of hosts in the network to make underutilized resources available to other peers.

Grid, which was designed to provide access to remote computing resources for high-performance applications, data-intensive applications, or both. Grid computing can be seen as an answer to drawbacks such as overloading, failure, and low QoS, in client–server systems. Such problems can occur in the context of high-performance computing, for example, when a large set of remote users accesses a supercomputer. Grid nodes typically make their own
resources available at the same time they are accessing resources on other nodes. Some recently developed P2P systems also require nodes to act as servers, at least when joining the network.

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Categories: IEEE Papers

Data-aware scheduling in grid computing

Authors:  Tevfik Kosar, Mehmet Balman
Efficient and reliable access to large-scale data sources and archiving destinations in a widely distributed computing environment brings new challenges. The insufficiency of the traditional systems and existing CPU-oriented batch schedulers in addressing these challenges has yielded a new emerging era: data-aware schedulers. In this article, we discuss the limitations of the traditional CPU-oriented batch schedulers in handling the challenging data management problem of large-scale distributed applications; give our vision for the new paradigm in data-intensive scheduling; and elaborate on our case study: the Stork data placement scheduler.

Essential matter:
Large experiments, such as high-energy physics simulations, genome mapping, and climate modeling generate data volumes reaching hundreds of terabytes. Data transferred from satellites and remote sensors too generate huge data. Processing such data requires distributed resources. Distributed resources imposes new challenges i.e. managing these resources, scheduling and allocation of storage resources, and efficient data movement.

A middleware is needed for scheduling and managing the tasks, as well as resources. The management of storage resources and data movement is main bottleneck. Overload of write data transfers on remote storage resources and concurrent read data transfers can crash server. Traditional Distributed systems closely couple data handling and computation. Data access is considered as a side effect of computation. The insufficiency of the traditional systems and existing CPU-Oriented schedulers in dealing with complex data handling has urged the necessity for Data-aware schedulers. Example of such schedulers is Stork Data placement Scheduler. Read more…

Categories: IEEE Papers

A Case For Grid Computing On Virtual Machines

A Case For Grid Computing On Virtual Machines

Authors: Renato J. Figueiredo, Peter A. Dinda, Jose A. B. Fortes

Proceedings of the 23rd International Conference on Distributed Computing Systems (ICDCS’03)


We advocate a novel approach to grid computing that is based on a combination of “classic” operating system level virtual machines (VMs) and middleware mechanisms to manage VMs in a distributed environment. The abstraction is that of dynamically instantiated and mobile VMs that are a combination of traditional OS processes (the VM moitors) and files (the VM state). We give qualitative arguments that justify our approach in terms of security, isolation, customization, legacy support and resource control, and we show quantitative results that demonstrate the feasibility of our approach from a performance perspective. Finally, we describe the middleware challenges implied by the approach and an architecture for grid computing using virtual machines.

Essential matter:

The Grid middleware solutions are implemented as an operating system users. This has some limitations of traditional user account model in conflicting with administrative domain boundaries. Implementing security mechanism for integrity of Grid resources from untrusted, and legacy code running on general purpose OS having multiplexing at this level(operating system user) is somewhat sloppy.

Figueiredo et al. proposes to fundamentally change the way grid computing is performed by raising the level of abstraction from that of the operating system user to that of the operating system virtual machine or VM. This addresses three fundamental issues:

  • support for legacy applications,
  • security against untrusted code and users
  • computation deployment independently of site administration.

Virtual machine presents a raw image to each user, and this mechanism is powerful because users become decoupled from

  • system Software and resource
  • other user sharing the resource

also it ensures that untrusted application can only access OS of virtual machine. Another advantage of this mechanism is that we can migrate running VMs to appropriate resources.

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Categories: IEEE Papers