Data Replication with 2D Mesh Protocol for Data Grid

Data replication is one of the widely approach to achieve high data availability and fault tolerant of a system. Data replication in a large scale distributed and dynamic network such as grid has effects the efficiency of data accessing and data consistency. Therefore a mechanism that can maintain the consistency of the data and provide high data availability is needed. This thesis discusses protocols and strategies of replicating data in distributed database and grid environment where network and users are dynamic. There are few protocols that have been implemented in distributed database and grid computing which is discussed such as Read One-Write All (ROWA), Voting (VT), Tree Quorum (TQ), Grid Configuration (GC), Three Dimensional Grid Structure (TDGS), Diagonal Replication in Grid (DRG) and Neighbor Replication in Grid (NRG). In this thesis, we introduce an enhanced replica control protocol, named Enhance Diagonal Replication 2D Mesh (EDR2M) protocol for grid environment and compares its result of availability, and communication cost with the latest protocol TDGS (2001) and NRG (2007). EDR2M proves data consistency by fulfilling the Quorum Intersection Properties. Evaluations that is suitable and applicability for EDR2M protocol solutions via analytical models and simulations. A simulation of EDR2M protocol is developed and the performance metrics evaluated are data availability, and communication cost. By getting the sufficient number of quorum, number of nodes in each quorum, and selecting the middle node of the diagonal sites to have the copy of the data file have improved the availability and communication cost for read and write operation compared to the latest protocol, TDGS (2001) and NRG (2007). Thus, the experiment has showed scientifically that EDR2M is the adequate protocol to achieve high data availability in a low communication cost by providing replica control protocol for a dynamic network such as grid environmen

A survey : particle swarm optimization-based algorithms for grid computing scheduling systems.

Bio-inspired heuristics have been promising in solving complex scheduling optimization problems. Several researches have been conducted to tackle the problems of task scheduling for the heterogeneous and dynamic grid systems using different bio-inspired mechanisms such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO). PSO has been proven to have a relatively more promissing performance in dealing with most of the task scheduling challenges. However, to achieve optimum performance, new models and techniques for PSO need to be developed. This study surveys PSObased scheduling algorithms for Grid systems and presents a classification for the various approaches adopted. Meta task-based and workflow-based are the main categories explored. Each scheduling algorithm is described and discussed under the suitable category

OTS: an optimal tasks scheduling algorithm based on QoS in cloud computing network

Cloud Computing has emerged as a service model that offers online accessible resources to the clients. These resources contain storage, servers, and other applications and it provides security, flexibility, and scalability. In Max-Min algorithm where the large tasks have their priority to be scheduled first this leads small tasks to stay longer in the queue until all huge tasks finished their execution. This study presents an optimal tasks scheduling algorithm by enhancing Max-Min algorithm. The simulation results have proven that the Proposed Optimal Tasks Scheduling OTS completes tasks execution with less execution time and higher performance compared with Max-Min and TS algorithms. The overall results show that the performance of the proposed algorithm achieved 6% better in terms of time execution compared of both of Max-Min and TS algorithms

Agent-based pricing determination for cloud services in multi-tenant environment

Cloud computing acts as a resource sharing pool that provides services to multiple customers, which are called tenants through the Internet. One of the big challenges in cloud is providing a price for leasing the services while adapting with budget limit of the tenants. In order to meet the rapidly growing and dynamic demands of tenants, this paper proposes a pricing determination scheme for cloud services using mathematical analysis. It aims to balance satisfaction between tenants and service provider in terms of budget and profit. Specifically, our pricing determination procedure aggregated the budget constraint of tenants and service cost to calculate the potential price of service. Service level agreement (SLA) is handled by an agent for determining minimum and maximum prices that represent in a range. Hence, the service cost that charged by the provider is identified within the price range in order to meet tenants’ requests. The results from our simulation demonstrate that the proposed pricing determination scheme provides better tenant satisfaction while sustaining provider profitability

Framework of fast medical data transmission

Nowadays, PACS systems in hospitals have been upgraded to 3D and 4D images. PACS (Picture Archiving and Communication system (PACS) is a medical imaging technology which provides storage and access to images from multiple modalities. Images from echocardiography and magnetic resonance imaging (MRI) are among the big data to be transferred through the network. Thus, the large size of data has used a lot of time to transmit through the network. To solve the problem, in our research, we proposed a new framework named “Exponential-and-Uniform-based (ExpoNUni) to improve the transmission time and maintain the quality of the data. Our "ExpoNUni" Framework has performed a better result compared to the framework embedded with techniques such as Fibonacci-based Splitting with V1mi n Technique and Uniform-based Splitting Technique

Highest response ratio next (HRRN) vs first come first served (FCFS) scheduling algorithm in grid environment

Research on Grid scheduling nowadays, focuses in solving three problems such as finding a good algorithm, automating the process, and building a flexible, scalable, and efficient scheduling mechanism. The complexity of scheduling predicament increases linearly with the size of the Grid. Submitted jobs in Grid environment are put in queue due to the large number of jobs submission. An adequate Grid scheduling technique used to schedule these jobs and sending them to their assigned resources. This paper simulates in C programming First Come First Served (FCFS) and Highest Response Ratio Next (HRRN) Grid scheduling algorithms. A good scheduling algorithm normally shows lower value of total waiting and schedule time. Hence, HRRN was selected because of the algorithm outperform the existing gLite Grid middleware scheduling. From the simulation result proof HRRN has better performance of total waiting time due to the priority scheme policy implementation in scheduler

The idle resource items workload and implication on different weight balance rate in grid scheduling

This paper discusses the impact of background workload for idle re-source items of computing elements in grid computing. Most of the previous re-search did not consider this factor. A resource item may be processing local system operations when the grid perceives them to be idle, thus upsetting grid processing activities. The introduction of the resource items and background load factor in this study will reveal the true computing capability of computing elements. This background load factor, represented in the form of weightage on resource item, is tested to seek overall grid performance. By allocating the right balance of workload weightage of resource item in a computing element, a significant improvement in processing performance is achieved

Checkpointing in selected most fitted resource task scheduling in grid computing

Grid applications run on environment that is prone to different kinds of failures. Fault tolerance is the ability to ensure successful delivery of services despite faults that may occur. Our research adds fault tolerance capacity with checkpointing and machine failure, to the current research, Selected Most Fitted (SMF) Task Scheduling for grid computing. This paper simulates one of fault tolerance techniques for grid computing, which is implementing checkpointing into Select Most Fitting Resource for Task Scheduling algorithm (SMF). We applied the algorithm of MeanFailure with Checkpointing in the SMF algorithm and named it MeanFailureCP-SMF. The MeanFailureCP-SMF is simulated using Gridsim with initial checkpointing interval at 20% job execution time. Results show that with MeanFailureCP-SMF has reduce the average execution time (AET) compare to the current SMF and MeanFailure Algorithm

Workload utilization dissemination on grid resources for simulation environment

This paper discusses the workload utilization dissemination for grid computing. The CPU is a well-known resource item and it is an integral part in most literatures while other RI's may include memory, network and I/O overhead. The selection of resource variables and the number of RI's involved will result in different definitions of the workload. Various combination of computer RI's have been explored for studying the style of usage, techniques embedded and their capabilities. In contemplating the exploration, this study successfully describe the pattern of workload dissemination through the usage of the RI's and elicited the enhancement factors for systems performance. Among these factors are the manipulation of computer RI's, type of workload information with method of use, the workload dissemination direction along with implementation method and using certain algorithm to come out with new integrated scheduling with load balancing capability. A combination of these factors will help in developing an optimized scheduling or load balancing algorithm