Frank Zhigang Wang

Ant colony optimization inspired resource discovery in P2P Grid systems

It is a challenge for the traditional centralized or hierarchical Grid architecture to manage the large-scale and dynamic resources, while providing scalability. The Peer-to-Peer (P2P) model offers a prospect of dynamicity, scalability, and availability of a large pool of resources. By integrating the P2P philosophy and techniques into a Grid architecture, P2P Grid system is emerging as a promising platform for executing large-scale, resource intensive applications. There are two typical resource discovery approaches for a large-scale P2P system. The first one is an unstructured approach which propagates the query messages to all nodes to locate the required resources. The method does not scale well because each individual query generates a large amount of traffic and the network quickly becomes overwhelmed by the messages. The second one is a structured approach which places resources at specified locations to make subsequent queries easier to satisfy. However, the method does not support multi-attribute range queries and may not work well in the network which has an extremely transient population. This paper proposes and designs a large-scale P2P Grid system which employs an Ant Colony Optimization (ACO) algorithm to locate the required resources. The ACO method avoids a large-scale flat flooding and supports multi-attribute range query. Multiple ants can be employed to improve the parallelism of the method. A simulator is developed to evaluate the proposed resource discovery mechanism. Comprehensive simulation results validate the effectiveness of the proposed method compared with the traditional unstructured and structured approaches.

Delayed switching applied to memristor neural networks

Magnetic flux and electric charge are linked in a memristor. We reported recently that a memristor has a peculiar effect in which the switching takes place with a time delay because a memristor possesses a certain inertia. This effect was named the “delayed switching effect.” In this work, we elaborate on the importance of delayed switching in a brain-like computer using memristor neural networks. The effect is used to control the switching of a memristor synapse between two neurons that fire together (the Hebbian rule). A theoretical formula is found, and the design is verified by a simulation. We have also built an experimental setup consisting of electronic memristive synapses and electronic neurons.

Delayed Switching in Memristors and Memristive Systems

It was found that the switching in a memristor takes place with a time delay (this peculiar feature is named “the delayed switching”). This feature has been verified by a circuit-based experiment. The physical interpretation of this phenomenon is that an electron element possesses certain inertia, i.e., charge q or flux is inertial with the tendency to remain unchanged (settle to some equilibrium state). It cannot respond as rapidly as the fast variation in the excitation waveform and always takes a finite but small time interval to change its resistance value, as it must take place in a memristor or memristive system. In addition, a potential application of using this feature in ultradense computer memory has been discussed.

Network modelling and simulation tools

Abstract Computer network technologies have been growing explosively and the study in computer networks is being a challenging task. To make this task easy, different users, researchers and companies have developed different network modelling and simulation (MS) tools. These network MS tools can be used in education and research as well as practical purposes. They vary with their characteristics. This paper reviews some of the most important network MS tools developed recently. This paper also shows a classification of the tools used in communications networks.

A heterogeneous storage grid enabled by grid service

Due to the explosive increase of data, storage Grid is a new model for deploying and managing storage resources distributed across multiple systems and networks, making efficient use of available storage capacity. Building a storage Grid demands corresponding protocols and standards to provide interoperability among the large number of heterogeneous storage systems. Service is becoming a basic application pattern of Grid because the service offers a standard means of interoperating between different applications running on a variety of platforms. This paper proposes a storage Grid architecture that wraps all distributed and heterogeneous storage resources into Grid services to provide transparent, remote, and on demand data access. The storage oriented Grid service can be considered as a basic building block of an infinite storage pool which provides good scalability through its inherent parallelism, and facilitates simple incremental resource expansion (to add storage resources, one just adds storage services). Grid users can stack simple modular storage service piece by piece as demand grows instead of buying monolithic storage systems. An implemented proof-of-concept prototype validates that the storage Grid architecture trade 5% (at most) performance degradation for an infinite and heterogeneous storage pool.

EED: Energy Efficient Disk drive architecture

Energy efficiency has become one of the most important challenges in designing future computing systems, and the storage system is one of the largest energy consumers within them. This paper proposes an Energy Efficient Disk (EED) drive architecture which integrates a relatively small-sized NAND flash memory into a traditional disk drive to explore the impact of the flash memory on the performance and energy consumption of the disk. The EED monitors data access patterns and moves the frequently accessed data from the magnetic disk to the flash memory. Due to the data migration, most of the data accesses can be satisfied with the flash memory, which extends the idle period of the disk drive and enables the disk drive to stay in a low power state for an extended period of time. Because flash memory consumes considerably less energy and the read access is much faster than a magnetic disk, the EED can save significant amounts of energy while reducing the average response time. Real trace driven simulations are employed to validate the proposed disk drive architecture. An energy coefficient, which is the product of the average response time and the average energy consumption, is proposed as a performance metric to measure the EED. The simulation results, along with the energy coefficient, show that the EED can achieve an 89.11% energy consumption reduction and a 2.04% average response time reduction with cello99 trace, a 7.5% energy consumption reduction and a 45.15% average response time reduction with cello96 trace, and a 20.06% energy consumption reduction and a 6.02% average response time reduction with TPC-D trace, respectively. Traditionally, energy conservation and performance improvement are contradictory. The EED strikes a good balance between conserving energy and improving performance.

Grid-Oriented Storage: A Single-Image, Cross-Domain, High-Bandwidth Architecture

This paper describes the grid-oriented storage (GOS) architecture and its implementations. A GOS-specific file system (GOS-FS), the single-purpose intent of a GOS OS, and secure interfaces via grid security infrastructure (GSI) motivate and enable this new architecture. As an FTP server, GOS with a slimmed OS, with a total volume of around 150 MB, outperforms the standard GridFTP by 20-40 percent. As a file server, GOS-FS acts as a network/grid interface, enabling a user to perform searches and access resources without downloading them locally. In the real-world tests between Cambridge and Beijing, where the transfer distance is 10,000 km, the multistreamed GOS-FS file opening/saving resulted in a remarkable performance increase of about 2-25 times, compared to the single-streamed network file system (NFSv4). GOS is expected to be a variant of or successor to the well-used network-attached storage (NAS) and/or storage area network (SAN) products in the grid era

Dynamic and scalable storage management architecture for Grid Oriented Storage devices

Most of currently deployed Grid systems employ hierarchical or centralized approaches to simplify system management. However, the approaches cannot satisfy the requirements of complex Grid applications which involve hundreds or thousands of geographically distributed nodes. This paper proposes a Dynamic and Scalable Storage Management (DSSM) architecture for Grid Oriented Storage (GOS) devices. Since large-scale data intensive applications frequently involve a high degree of data access locality, the DSSM divides GOS nodes into multiple geographically distributed domains to facilitate the locality and simplify the intra-domain storage management. Dynamic GOS agents selected from the domains are organized as a virtual agent domain in a Peer-to-Peer (P2P) manner to coordinate multiple domains. As only the domain agents participate in the inter-domain communication, system wide information dissemination can be done far more efficiently than flat flooding. Grid service based storage resources are adopted to stack simple modular service piece by piece as demand grows. The decentralized architecture of DSSM avoids the hierarchical or centralized approaches of traditional Grid architectures, eliminates large-scale flat flooding of unstructured P2P systems, and provides an interoperable, seamless, and infinite storage pool in a Grid environment. The DSSM architecture is validated by a proof-of-concept prototype system.

Implementing and Evaluating Security Controls for an Object-Based Storage System

This paper presents the implementation and performance evaluation of a real, secure object-based storage system compliant to the T10 OSD standard. In contrast to previous work, our system implements the entire three security methods of the OSD security protocol defined in the standard, namely CAPKEY, CMDRSP and ALLDATA, and an Oakley-based authentication protocol by which the Metadata Server (MDS) and client can be sure of each others identities. Moreover, our system supports concurrent operations from multiple clients to multiple OSDs. The MDS, a combination of security manager and storage/policy manager, performs access control, global namespace management, and concurrency control. We also evaluate the performance and scalability of our implementation and compare it with iSCSI, NFS and Lustre storage configurations. The overhead of access control is small: compared with the same system without any security mechanism, bandwidth for reads and writes with the CAPKEY and CMDRSP methods decreases by less than 5%, while latency for metadata operations with any of the security methods increases by less than 0.3 ms (5%). The system with the ALLDATA method suffers a higher performance penalty: large sequential accesses run at 46% and 52% of the maximum bandwidth of unsecured storage for reads and writes respectively. The aggregate throughput scales with the number of OSDs (up to 8 in our experiments). The overhead of the SET KEY commands for partition and working keys refreshed frequently is less than 2 ms.

A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation

Spatial On-Line Analytical Processing (SOLAP) is a powerful decision support systems tool for exploring the multidimensional perspective of spatial data. In recent years, remotely sensed data have been integrated into SOLAP cubes, and this improvement has advantages in spatio-temporal analysis for environment monitoring. However, the performance of aggregations in SOLAP still faces a considerable challenge from the large-scale dataset generated by Earth observation. From the perspective of data parallelism, a tile-based SOLAP cube model, the so-called Tile Cube, is presented in this paper. The novel model implements Roll-Up/Drill-Across operations in the SOLAP environment based on Map-Reduce, a popular data-intensive computing paradigm, and improves the throughput and scalability of raster aggregation. Therefore, the long time-series, wide-range and multi-view analysis of remotely sensed data can be processed in a short time. The Tile Cube prototype was built on Hadoop/Hbase, and drought monitoring is used as an example to illustrate the aggregations in the model. The performance testing indicated the model can be scaled along with both the data growth and node growth. It is applicable and natural to integrate the SOLAP cube with Map-Reduce. Factors that influence the performance are also discussed, and the balance of them will be considered in future works to make full use of data locality for model optimisation. Remote-sensing data aggregation in Spatial OLAP faces big data challenge.A tile-based SOLAP cube model for raster is presented based on parallel map algebra.The model integrates SOLAP with Map-Reduce for accelerating spatio-temporal aggregations.The model shows good scalability along with both increased data and node number.Complexity of the distribution is encapsulated via transparent SOLAP interface.