High Performance Computing Design by Code Migration for Distributed Desktop Computing Grids

Large scale loosely coupled PCs can organize clusters and form desktop computing grids on sharing each processing power; power of PCs, transaction distributions, network scales, network delays, and code migration algorithms characterize the performance of the computing grids. This article describes the design methodologies of workload management in distributed desktop computing grids. Based on the code migration experiments, transfer policy for computation was determined and several simulations for location policies were examined, and the design methodologies for distributed desktop computing grids are derived from the simulation results. The language for distributed desktop computing is designed to accomplish the design methodologies.

An Intelligent Sensor Placement Method to Reach a High Coverage in Wireless Sensor Networks

Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Optimizing the sensor deployment provides sufficient sensor coverage and saves cost of sensors for locating in grid points. This article applies the modified binary particle swarm optimization algorithm for solving the sensor placement in distributed sensor networks. PSO is an inherent continuous algorithm, and the discrete PSO is proposed to be adapted to discrete binary space. In the distributed sensor networks, the sensor placement is an NP-complete problem for arbitrary sensor fields. One of the most important issues in the research fields, the proposed algorithms will solve this problem by considering two factors: the complete coverage and the minimum costs. The proposed method on sensors surrounding is examined in different area. The results not only confirm the successes of using the new method in sensor placement, also they show that the new method is more efficiently compared to other methods like Simulated Annealing(SA), PBIL and LAEDA.

ACO Based Dynamic Scheduling Algorithm for Real-Time Multiprocessor Systems

The Ant Colony Optimization (ACO) algorithms are computational models inspired by the collective foraging behavior of ants. The ACO algorithms provide inherent parallelism, which is very useful in multiprocessor environments. They provide balance between exploration and exploitation along with robustness and simplicity of individual agent. In this paper, ACO based dynamic scheduling algorithm for homogeneous multiprocessor real-time systems is proposed. The results obtained during simulation are measured in terms of Success Ratio (SR) and Effective CPU Utilization (ECU) and compared with the results of Earliest Deadline First (EDF) algorithm in the same environment. It has been observed that the proposed algorithm is very efficient in underloaded conditions and it performs very well during overloaded conditions also. Moreover, the proposed algorithm can schedule some typical instances successfully which are not possible to schedule using EDF algorithm.

Performance Evaluation of Full Diversity QOSTBC MIMO Systems with Multiple Receive Antenna

Multipath fading is inherent in wireless communication systems. Diversity is the technique which takes advantage of multipath to mitigate the effect of fading and increase signal strength. Space Time Block codes (STBC) are used in MIMO systems to improve the performance by maximizing transmit and/or receive diversity. Among different schemes based on STBC, Quasi Orthogonal Space Time Block Code (QOSTBC) is able to achieve full rate transmission for more than two transmit antennas. Constellation Rotation QOSTBC (CR-QOSTBC) achieves full diversity and improves performance further along with full rate, to overcome the limitation of QOSTBC, which is unable to maintain orthogonality amongst the codes transmitted by different antennas. Higher diversity can be achieved by increasing uncorrelated paths between transmitter and receivers using higher number of receive antennas. This paper examines improvement in BER with reference to a number of receive antennas. Simulations were carried out under ideal as well as realistic environments, using least square technique with four antennas at transmitter side and variable receive antennas. Results of simulations presented in this paper indicate performance improvement of CR-QOSTBC over QOSTBC in flat fading channel environment. Simulation results also show performance degradation in BER when channel is estimated at the receiver.

Using Machine Learning Techniques for Performance Prediction on Multi-Cores

Sharing of resources by the cores of multi-core processors brings performance issues for the system. Majority of the shared resources belong to memory hierarchy sub-system of the processors such as last level caches, prefetchers and memory buses. Programs co-running on the cores of a multi-core processor may interfere with each other due to usage of such shared resources. Such interference causes co-running programs to suffer with performance degradation. Previous research works include efforts to characterize and classify the memory behaviors of programs to predict the performance. Such knowledge could be useful to create workloads to perform performance studies on multi-core processors. It could also be utilized to form policies at system level to mitigate the interference between co-running programs due to use of shared resources. In this work, machine learning techniques are used to predict the performance on multi-core processors. The main contribution of the study is enumeration of solo-run program attributes, which can be used to predict concurrent-run performance despite change in the number of co-running programs sharing the resources. The concurrent-run involves the interference between co-running programs due to use of shared resources.

Location Update Improvement Using Fuzzy Logic Optimization in Location Based Routing Protocols in MANET

Several position-based routing protocols have been developed for mobile ad hoc networks. Many of these protocols assume that a location service is available which provides location information on the nodes in the network. This paper introduces a new schema in management of mobile nodes location in mobile ad hoc networks. Fuzzy logic optimization is applied to a better management of location update operation in hierarchical location services. Update management overhead is decreased without significant loss of query success probability. One-hop-chain-technique is used for Auto compensation. A new composed method can update mobile nodes location when the nodes cross a grid boundary. The proposed method uses a dynamic grid area that ?solves the ping-pong problem between grids. Simulation results show that these methods are effective. The algorithms are distributed and can keep scalability in the scenario of increasing nodes density?. The described solutions are not limited to a special network grid ordering, and can be used in every hierarchical ordering like GLS if the ordering can be mappable on these methods.

A Novel System Oriented Scheduler for Avoiding Haste Problem in Computational Grids

Scheduling is an important issue that must be handled carefully to realize the “Just login to compute” principle introduced by computational grids. Current grid schedulers suffer from the haste problem, which is the inability to schedule all tasks successfully. Accordingly, some tasks fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously allocated to other tasks. This paper introduces the scheduling haste problem and presents a novel high throughput grid scheduler. The proposed scheduler selects the most suitable worker to execute an input grid task. Hence, it minimizes the turnaround time for a set of grid tasks. Moreover, the scheduler is system oriented and avoids the scheduling haste problem. Experimental results show that the proposed scheduler outperforms traditional grid schedulers as it introduces better scheduling efficiency.

A Computational Grid Scheduling Model to Maximize Reliability Using Modified GA

This paper presents a grid scheduling model to schedule a job on the grid with the objective of ensuring maximum reliability to the job under the current grid state. The model schedules a modular job to those resources that suit the job requirements in terms of resources while offering the most reliable environment. The reliability estimates depict true grid picture and considers the contribution of the computational resources, network links and the application awaiting allocation. The scheduling executes the interactive jobs while considering the looping structure. As scheduling on the grid is an NP hard problem, soft computing tools are often applied. This paper applies Modified Genetic Algorithm (MGA), which is an elitist selection method based on the two threshold values, to improve the solution. The MGA works on the basis of partitioning the current population in three categories: the fittest chromosomes, average fit chromosomes and the ones with worst fitness. The worst are dropped, while the fittest chromosomes of the current generation are mated with the average fit chromosomes of the previous generation to produce off-spring. The simulation results are compared with other similar grid scheduling models to study the performance of the proposed model under various grid conditions.

Risk Assessment for Cloud-Based IT Systems

The use of Cloud Computing services is an attractive option to improve IT systems to achieve rapidly and elastically provisioned capability, and also to offer economic benefits. However, companies see security as a major concern in migrating to the Cloud. To bring clarity in Cloud security, this paper presents a systematic approach to manage the risks and analyzes the full range of risk in Cloud Computing solutions. Furthermore, as a study case, Google App Engine Platform is assessed based on ISO/IEC 27002 and OWASP Top 10 Risk List in this paper. Knowing the risks of Cloud solutions, companies can execute well-informed decisions on going into the Cloud and build their Cloud solutions in a secure way, relying on a robust e-trust relationship.

Parallelization of Littlewood-Richardson Coefficients Computation and its Integration into the BonjourGrid Meta-Desktop Grid Middleware

This paper shows how to parallelize a compute intensive application in mathematics (Group Theory) for an institutional Desktop Grid platform coordinated by a meta-grid middleware named BonjourGrid. The paper is twofold: it shows how to parallelize a sequential program for a multicore CPU which participates in the computation; and it demonstrates the effort for launching multiple instances of the solutions for the mathematical problem with the BonjourGrid middleware. BonjourGrid is a fully decentralized Desktop Grid middleware. The main results of the paper are: a) an efficient multi-threaded version of a sequential program to compute Littlewood-Richardson coefficients, namely the Multi-LR program and b) a proof of concept, centered around the user needs, for the BonjourGrid middleware dedicated to coordinate multiple instances of programsfor Desktop Grids and with the help of Multi-LR. In this paper, the scientific work consists in starting from a model for the solution of a compute intensive problem in mathematics, to incorporate the concrete model into a middleware and running it on commodity PCs platform managed by an innovative meta Desktop Grid middleware.