We address the problem of cache replacement policies for Storage Resource Managers (SRMs) that are used in Data Grids. An SRM has a disk storage of bounded capacity that retains some N objects. A replacement policy is applied to determine which object in the cache needs to be evicted when space is needed. We define a utility function for ranking the candidate objects for eviction and then describe an efficient algorithm for computing the replacement policy based on this function. This computation takes time O (log N). We compare our policy with traditional replacement policies such as Least Frequently Used (LFU), Least Recently Used (LRU), LRU-K, Greedy Dual Size (GDS), etc., using simulations of both synthetic and real workloads of file accesses to tertiary storage. Our simulations of replacement policies account for delays in cache space reservation, data transfer and processing. The results obtained show that our proposed method is the most cost effective cache replacement policy for Storage Resource Managers (SRM).

Disk cache replacement algorithms for storage resource managers indata-grids

Data Replication, creating identical copies of data on different sites geographically, is one of the effective optimization techniques for reducing data access costs and improving load balance among sites in data-intensive computing. However, due to the limitation of storage capacity of each site, an efficient replica replacement can make a significant difference in the performance and efficiency of replication technologies. In this paper, for the inability of LRU to cope with access patterns with weak locality, a novel replacement strategy called Low Inter-reference Recency Set (LIRS) was applied into replica replacement during data replication. And then, OptorSim was extended, and LIRS replica replacement algorithm was implemented. Finally, LIRS replacement strategy was comprehensively compared with other algorithms including LRU, MRU LFU and MFU by use of different scheduling algorithms and file access patterns, and the results show that LIRS replacement algorithm can improve the performance of the data access, especially under access pattern with weak locality.

An LIRS-Based Replica Replacement Strategy for Data-Intensive Applications

Nowadays, it has become feasible to use mobile nodes as contributing entities in computing systems. In this paper, we consider a computational grid in which the mobile devices can share their idle resources to realize parallel processing. The overall computing task can be arbitrarily partitioned into multiple subtasks to be distributed to mobile resource providers (RPs). In this process, the computation load scheduling problem is highlighted. Based on the optimization objective, i.e., minimizing the task makespan, a buyer-seller model in which the task sponsor can inspire the SPs to share their computing resources by paying certain profits, is proposed. The Stackelberg Pricing Game (SPG) is employed to obtain the optimal price and shared resource amount of each SP. Finally, we evaluate the performance of the proposed algorithm by system simulation and the results indicate that the SPG-based load scheduling algorithm can significantly improve the time gain in mobile grid systems.

pdf/divisible-load-scheduling-in-mobile-grid-based-on-1vugr0o96c.pdf

Divisible Load Scheduling in Mobile Grid Based on Stackelberg Pricing Game

Recently, excessive installations of APs have caused WLAN interference, and many techniques have been suggested to solve this problem. The AP aggregation technique serves to reduce active APs by moving station connections to a certain AP. Since this technique forcibly moves station connections, the transmission performance of some stations may deteriorate. The AP transmit power control technique may cause station disconnection or deterioration of transmission performance when power is reduced under a certain level. The combination of these two techniques can reduce interference through AP aggregation and narrow the range of interferences further through detailed power adjustment. However, simply combining these techniques may decrease the probability of power adjustment after aggregation and increase station disconnections upon power control. As a result, improvement in performance may be insignificant. Hence, this study suggests a scheme to combine the AP aggregation and the AP transmit power control techniques in OpenFlow-based WLAN to ameliorate the disadvantages of each technique and to reduce interferences efficiently by performing aggregation for the purpose of increasing the probability of adjusting transmission power. Simulations reveal that the average transmission delay of the suggested scheme is reduced by as much as 12.8% compared to the aggregation scheme and by as much as 18.1% compared to the power control scheme. The packet loss rate due to interference is reduced by as much as 24.9% compared to the aggregation scheme and by as much as 46.7% compared to the power control scheme. In addition, the aggregation scheme and the power control scheme decrease the throughput of several stations as a side effect, but our scheme increases the total data throughput without decreasing the throughput of each station.

/pdf/an-interference-reduction-scheme-using-ap-aggregation-and-4y9jpr9n8x.pdf

An Interference Reduction Scheme Using AP Aggregation and Transmit Power Control on OpenFlow-based WLAN

Virtual molecular docking is a computational method used in computer-aided drug discovery that calculates the binding affinity of a small molecule drug candidate to a target protein and greatly reduces the time and cost of suggesting new potential pharmaceuticals. D3DOCK is a suite of automated docking tools, it was developed for sensitively investigating the effects of halogen bond in drug discovery by based on Autodock Vina. In this study, we developed IDOS, a high-throughput and scalable virtual docking system. We use the open source Hadoop framework implementing the Spark paradigm for distributed computing on a private cloud platform. IDOS can work on a single node as well as on distributed nodes compared with the stand-alone version of D3DOCK, it performed almost linear acceleration on our Spark cluster, with parallel efficiency of more than 80%, and 5x to 6x speedup can be achieved on the cluster depending on the number of work nodes. Moreover, On account of the widely used of cloud computing, IDOS can be easily installed and employed for docking on condition that the IDOS docker image was upload to the docker hub shared warehouse. We also have developed the docking workflow with an MPI version on high-performance computers (HPC). However, compared with HPC system, mounting IDOS on a cheap and easy to obtain cloud computing cluster is obviously more conducive, which allows more researchers to do docking with it expediently. IDOS is freely available at https://github.com/codedinner/IDOS.

IDOS: Improved D3DOCK on Spark

One of the major problems in Mobile Grid environments is the optimal distribution and replication of data files, in order to improve and maintain a high overall throughput of Grid jobs that access files. A data replica replacement algorithm based on value model is proposed and the arithmetic method to calculate replica value is introduced. This algorithm adapts to the high variation environment in Mobile Grid environment. The value of a data replica reflects the usage history of the cache and the variability of it. We construct a data replica value evaluation system which makes those unstable or rarely used replicas first be deleted, when there is not enough storage space during replication. Experimental results show that the proposed algorithm can obtain better performance for grid tasks to access data replicas.

A efficient data replica replacement algorithm for Mobile Grid

Protein Docking is an important task in computational chemistry and computational biology and yet is very computationally expensive. This paper presents a study on implementing molecular docking program in the Hadoop-based system. The molecular docking platform based on Hadoop provides the preprocessing of ligand datasets and the analysis function of the docking results, it also implements the parallel docking by using MapReduce. Through this platform, the docking time is reduced, and the management of the ligand dataset is convenient.

A molecular docking platform based on Hadoop

A distributed dynamic power control algorithm that can improve per-user throughput significantly in dense WLAN/WPANs is proposed in this paper. It can solve self-interference problems which are caused by the deficiency of communication channels, particularly for non-uniform traffic conditions. Computer simulations show that given any traffic load distribution and any initial channel allocation, the algorithm converges to an equilibrium state in a short time, in which the overall throughput of the network is significantly improved. The proposed algorithm can change its threshold self-adaptively and has significant practical value due to its simplicity and effectiveness.

A Distributed Dynamic Power Control Algorithm for Efficient Processing Self-Interference in WLAN/WPANs

A new Drug Discovery Platform (DDP) for distributed virtual screening by using database system is proposed. DDP can monitor the process in real time, analyze docking results promptly, and process poses by energy ranking techniques. DDP can provide controls of communication and synchronization, it allows multiple tasks to access and update the data in parallel with marginal additional effort by using a database management system DB2. DDP can be used to dock millions of molecules effectively.

http://ieeexplore.ieee.org/iel7/6657460/6657931/06658058.pdf

A New Drug Discovery Platform for Distributed Virtual Screening Based on Data Management System

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Papers

A efficient data replica replacement algorithm for Mobile Grid

A molecular docking platform based on Hadoop

A Distributed Dynamic Power Control Algorithm for Efficient Processing Self-Interference in WLAN/WPANs

A New Drug Discovery Platform for Distributed Virtual Screening Based on Data Management System