Co-allocation Scheme Research Articles

Co-scheduling Deadline-Sensitive Applications in Large-scale Grid Systems

In large-scale grid systems, plenty of applications are constrained by soft or hard deadline requirement. However, it is difficult to guarantee the deadline requirements of these applications because of the dynamical nature of distributed systems. In this paper, a novel approach is proposed to evaluate the deadline-guarantee of co-allocation schemes that obtained from conventional co-allocation policies. Based on this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model integrates multiple co-allocation policies to generate different co-allocation schemes, and selects the one with optimal deadline-guarantee for grid applications. By this way, the hybrid-policy model combines the merits of different policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide coallocation scheme with enhanced deadline-guarantee as well as lower deadline-miss rate.

Hybrid-Policy Co-allocation Model in Computational Grid

To address the issue of resource co-allocation with deadline constraint in grids, a novel approach is proposed to evaluate the deadline-guarantee of co-allocation schemes that obtained from conventional co-allocation policies. Based on this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model combines multiple co-allocation policies and selects the one with optimal deadline-guarantee for scheduling. In this way, the hybrid-policy model combines the merits of different policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide co-allocation scheme with improved deadline-guarantee and lower down the deadline-miss rate for real-time applications.

RARS: a Resource-Aware Replica Selection and co-allocation scheme for mobile grid

This paper presents a design for a mobile data grid architecture using agent technology, on which is based our proposed Resource-Aware Replica-Scheduling co-allocation algorithm for the mobile data grid environment. Mobile clients can access replicas of desired data from parallel, mobile grid nodes. These offer effective, efficient and reliable ways of using a variety of superior resources, such as the remaining energy, the bandwidth and the stability of connection in the nodes. The simulations presented here show that the RARS performs excellently and in the transmission time and the survival rate of the mobile nodes it exceeds all other approaches.

Accessing data from many servers simultaneously and adaptively in data grids

In data grid environments, data replication services increase the reliability and the availability of the data sets. Using replicas, a data set can be downloaded from many servers simultaneously in order to improve the performance of the data transfer. However, the servers selected may become slow or congested. Therefore, it should be replaced dynamically. In this paper, we propose a method to improve the efficiency of downloading by reevaluating the conditions of all servers that can supply the dataset during the download progress. Current servers used for downloading, if perform unsatisfactorily, will be replaced by others that meet the performance criteria. In all previous schemes in parallel downloading, the load of an ill performing server will be just transferred to a more powerful server. However, intuitively if there are idle servers around that also have this data, why not use them? Therefore, our method will monitor all servers that can supply the data even if a server is not involved in the download process initially. Loads may be transferred from a working server to a non-working server, not just transferred to another working server. Furthermore, to decide the suitability of a server, we consider not only the connection bandwidth but also the status within a server such as CPU and memory usage. We implement our method in a real grid environment. Our method decreases the completion time by 1.63%–13.45% in the real grid environment and by 6.28%–30.56% in the grid environment with other injected load when compared to the recursive co-allocation scheme. It shows that the proposed scheme adapts to the dynamic environment nicely and decreases the total download time effectively.

A Framework for an Integrated Co-allocator for Data Grid in Multi-Sender Environment

Data grid consists of scattered computing and storage resources located dispersedly in the grid network. These large sized data sets are replicated in more than one site for the better availability to the other nodes in a grid. Downloading the dataset from these replicated locations have practical difficulties and we find interest in a co-allocated download framework, which enables parallel download of replicated data from multiple servers. In this paper, we proposed a dynamic co-allocation scheme for parallel data transfer in grid environment, which copes up with highly inconsistent network and server performance. The model comprises of co-allocator, monitor and control mechanisms. The scheme initially obtains the bandwidth parameter from the monitor module to fix the partition size and the data transfer tasks are allocated onto the servers in duplication. In this way, the process of data transfer can neither be interrupted nor paralyzed, even when the network link is broken or server crash. We used Globus toolkit for our framework by making use of grid information and GridFTP services. We compared our scheme with the existing schemes and the results show notable improvement in overall completion time of data transfer.

Improvements on dynamic adjustment mechanism in co-allocation data grid environments

Several co-allocation strategies have been coupled and used to exploit rate differences among various client-server links and to address dynamic rate fluctuations by dividing files into multiple blocks of equal sizes. However, a major obstacle, the idle time of faster servers having to wait for the slowest server to deliver the final block, makes it important to reduce differences in finishing time among replica servers. In this paper, we propose a dynamic co-allocation scheme, namely Recursive-Adjustment Co-Allocation scheme, to improve the performance of data transfer in Data Grids. Our approach reduces the idle time spent waiting for the slowest server and decreases data transfer completion time.

A Recursively-Adjusting Co-allocation scheme with a Cyber-Transformer in Data Grids

A co-allocation architecture was developed in order to enable parallel downloads of datasets from multiple servers. Several co-allocation strategies have been coupled and used to exploit rate differences among various client–server links and to address dynamic rate fluctuations by dividing files into multiple blocks of equal sizes. However, a major obstacle, the idle time of faster servers having to wait for the slowest server to deliver the final block, makes it important to reduce differences in finish times among replica servers. In this paper, we propose Recursively-Adjusting Co-Allocation, a dynamic co-allocation scheme for improving data transfer performance in Data Grids. The experimental results show that our approach can reduce the idle time spent waiting for the slowest server and decrease data transfer completion times. We developed Cyber-Transformer, a new toolkit with a friendly GUI interface that makes it easy for inexperienced users to manage replicas and download files in Data Grid environments. We also provide an effective scheme for reducing the cost of reassembling data blocks.