A modern service-oriented distributed storage solution

A new system model reflecting the clustered structure of distributed storage is suggested to investigate bandwidth requirements for repairing failed storage nodes. Large data centers with multiple racks/disks or local networks of storage devices (e.g. sensor network) are good applications of the suggested cluster-based model. In realistic scenarios involving clustered storage structures, repairing storage nodes using intact nodes residing in other clusters is more bandwidth-consuming than restoring nodes based on information from intra-cluster nodes. Therefore, it is important to differentiate between intra-cluster repair bandwidth and cross-cluster repair bandwidth in modeling distributed storage. Capacity of the suggested model is obtained as a function of fundamental resources of distributed storage systems, namely, storage capacity, intra-cluster repair bandwidth and cross-cluster repair bandwidth. Based on the capacity expression, feasible sets of required resources which enable reliable storage are analyzed. It is shown that the cross-cluster traffic can be minimized to zero (i.e., local repair within a cluster becomes possible) by allowing extra resources on storage capacity and intra-cluster repair bandwidth, according to a law specified in a closed-form. Moreover, trade-off between cross-cluster traffic and intra-cluster traffic is observed for sufficiently large storage capacity.

We present the design and implementation of both an enhanced type of OSD device, the OSD+ device, and a metadata cluster based on it. OSD+s support data objects and directory objects. A directory object stores file names and attributes, and supports metadata--related operations. OSD+s profit the directory implementation and features of the underlying file systems used by the storage nodes, achieving a great flexibility, simplicity and small overhead. By using OSD+ devices, we show how a metadata cluster can effectively be managed by all the servers in a system, improving the performance, scalability and availability of the metadata service. The performance of our new metadata cluster has been evaluated and compared with Lustre's. The results show that our proposal obtains a better throughput than Lustre when both use a single metadata server, easily getting improvements of more than 60--80\%, and that the performance scales with the number of OSD+s.

SABDTM: Security-first architecture-based dynamic trusted measurement scheme for operating system of the virtual computing node

Selfishness and altruism are two distinctive node behaviors in wireless networks. Users of network nodes, e.g. mobile phones, would like their individual devices to be operational for as long as possible. On the other hand, users and operators of wireless networks may wish for an overall long network lifetime via collaborative node operations. In contrast to the most of the work done till now, in this paper, we consider a node-centric scenario, where a node can balance its energy consumption according to its needs to prolong the node lifetime. More specifically, the energy balancing problem is defined as an inventory control problem, where the node may choose to be selfish or unselfish with various degrees having in mind a range of parameters, such as its battery state and the existence and rate of energy harvesting. Each node can use the proposed solution to allocate the available energy between internal (node itself) and external (collaborative) tasks subject to dependencies among application and communication layers and the node operating system. We also discuss a number of other possible extensions of the method to include interactions among different nodes in the network. To illustrate one such an extension, a case study with a large number of nodes among two separate but co-located wireless mesh networks is presented. By applying the inventory balancing algorithm, considerable increase in the network lifetime is shown for both networks.

The Design of Node Operating System for Cyber Physical Systems

Large-scale storage systems are crucial components in data-intensive applications such as search engine clusters, video-on-demand servers, sensor networks and grid computing. A storage server typically consists of a set of storage devices. In such systems, data layouts may need to be reconfigured over time for load balancing or in the event of system failure/upgrades. It is critical to migrate data to their target locations as quickly as possible to obtain the best performance. Most of the previous results on data migration assume that each storage node can perform only one data transfer at a time. A storage node, however, can typically handle multiple transfers simultaneously and this can reduce the total migration time significantly. Moreover, storage devices tend to have heterogeneous capabilities as devices may be added over time due to storage demand increase. In this paper, we consider the heterogeneous data migration problem, where we assume that each storage node v has different transfer constraint cv, which represents how many simultaneous transfers v can handle. We develop algorithms to minimize the data migration time. We show that it is possible to find an optimal migration schedule when all cvs are even. Furthermore, though the problem is NP-hard in general, we give an efficient algorithm that offers a rigorous (1 + o(1))-approximation guarantee.

We have developed a distributed parallel storage system that employs the aggregate bandwidth of multiple data servers connected by a high-speed wide-area network to achieve scalability and high data throughput. This paper studies different schemes to enhance the reliability and availability of such network-based distributed storage systems. The general approach of this paper employs erasure error-correcting codes that can be used to reconstruct missing information caused by hardware, software, or human faults. The paper describes the approach and develops optimized algorithms for the encoding and decoding operations. Moreover, the paper presents techniques for reducing the communication and computation overhead incurred while reconstructing missing data from the redundant information. These techniques include clustering, multidimensional coding, and the full two-dimensional parity schemes. The paper considers trade-offs between redundancy, fault tolerance, and complexity of error recovery.

This study develops a methodology for coordinated operation of distributed energy storage systems in distribution networks. The developed methodology considers that energy storage resources can contribute to their owners’ inherent activities and to a more flexible and efficient distribution network operation. The optimisation tool based on mixed‐integer linear programming is developed to maximise the technical and economic value of distributed storage taking into account their multifunctional potential and the presence of intermittent renewable sources. The methodological developments are validated in a case study of a real medium‐voltage distribution network with two storage systems coupled with a wind park and an industrial ‘prosumer’ (i.e. energy consumers who are producing their own energy). Results make evident the robustness of the methodology and enable the assessment of the technical and economic impacts of the integration of distributed storage. Moreover, opportunity costs for distributed storage to perform services to the distribution system operator are demonstrated.

Data migration schemes are critical to balance the load in storage clusters for performance improvement. However, as NAND flash based SSDs are widely deployed in storage systems, extending the lifespan of SSD storage clusters becomes a new challenge for data migration. Prior approaches designed for HDD storage clusters, however, are inefficient due to excessive write amplification during data migration, which significantly decrease the lifespan of SSD storage clusters. To overcome this problem, we propose EDM, an endurance aware data migration scheme with careful data placement and movement to minimize the data migrated, so as to limit the worn-out of SSDs while improving the performance. Based on the observation that performance degradation is dominated by the wear speed of an SSD, which is affected by both the storage utilization and the write intensity, two complementary data migration policies are designed to explore the trade-offs among throughput, response time during migration, and lifetime of SSD storage clusters. Moreover, we design an SSD wear model and quantitatively calculate the amount of data migrated as well as the sources and destinations of the migration, so as to reduce the write amplification caused by migration. Results on a real storage cluster using real-world traces show that EDM performs favorably versus existing HDD based migration techniques, reducing cluster-wide aggregate erase count by up to 40%. In the meantime, it improves the performance by 25% on average compared to the baseline system which achieves almost the same effectiveness of performance improvement as previous migration techniques.

In this paper, an extensible system prototype of cloud storage is designed. Considering the load balance of cloud storage system, an improved data storage strategy based on consistent hashing algorithm is proposed in this paper. The strategy adopts virtual nodes to storage data and real–time monitoring of load rate of each storage node to adjust load balancing of the whole storage system. In the improved strategy, the priority is introduced into the storage system to rapidly improve the utilization rate of the new storage nodes. The strategy can effectively optimize the performance of the whole storage system, and improve the overall effect of the load balancing.

Distributed storage systems provide large-scale data storage services, yet they are confronted with frequent node failures. To ensure data availability, a storage system often introduces data redundancy via replication or erasure coding. As erasure coding incurs significantly less redundancy overhead than replication under the same fault tolerance, it has been increasingly adopted in large-scale storage systems. In erasure-coded storage systems, degraded reads to temporarily unavailable data are very common, and hence boosting the performance of degraded reads becomes important. One challenge is that storage nodes tend to be heterogeneous with different storage capacities and I/O bandwidths. To this end, we propose FastDR, a system that addresses node heterogeneity and exploits I/O parallelism, so as to boost the performance of degraded reads to temporarily unavailable data. FastDR incorporates a greedy algorithm that seeks to reduce the data transfer cost of reading surviving data for degraded reads, while allowing the search of the efficient degraded read solution to be completed in a timely manner. We implement a FastDR prototype, and conduct extensive evaluation through simulation studies as well as testbed experiments on a Hadoop cluster with 10 storage nodes. We demonstrate that our FastDR achieves efficient degraded reads compared to existing approaches.

Software-defined storage (SDS) aims to minimize the complexity of data management in the Cloud. SDS decouples the control plane from the data plane and simplifies the management of the storage system via automated storage policy enforcement. In this paper, we propose a novel SDS framework for Object Storage that allows to decentralize policy enforcement through the deployment of per-object management policies in the storage nodes. As in active storage systems, we leverage the underutilized CPU time in the storage nodes. But our framework goes one step further. It provides a new management abstraction called micro-controllers which operate on objects depending on their state and content, thereby permitting the implementation of sophisticated management policies, such as the automated deletion of an object based on its access history, and even allowing the orchestration of active storage tasks. Our SDS system avoids the massive interception of data flows by moving that logic to the appropriate objects. Furthermore, our extensible model simplifies the customization of Object Storage services. We present in the validation several interesting use cases such as automated deletion, content level access control, and Web prefetching.

With the development of cloud computing and internet, e-Commerce, e-Business and corporate world revenue are increasing with high rate. These areas require scalable and consistent databases. NoSQL databases such as HBase has been proven to scalability and well performance on cloud computing platforms. However, the inevitable special data with few increment and frequent access leads to hotspot data and unbalanced accessing distribution between data storage servers. Due to their properties, these data often cannot be stored in multiple tables. Some storage nodes become the bottleneck of the distributed storage system, therefore, it becomes difficult to improve the performance by increasing the number of nodes which severely limits the scalability of the storage system. In order to make the performance of the cluster increases with the size of the cluster simultaneously, we devise a new distributed database storage framework to solve those issues mentioned above by changing the storage and read-write mode of the hotspot data. This structure guarantees that the hotspot data will not aggregate in the same storage node, as it guarantees that the data is not too hot in a single storage node. We implement the scalable database based on Apache HBase, which achieve almost double performance of throughput considering heavy read-write pressure situation only with double reading substites. Besides, heavy load node owing to hotspot data will no longer present in the new distributed database.

A new distributed storage scheme for cluster video server

We have proposed a distributed storage system which aims to utilize storage fragments distributed in network nodes without administrators' maintenance cost. Even the performance of storage devices are not uniform, autonomously data block migration helps to achieve enhancing the whole I/O performance of the storage system. However, we have considered all the storage devices to be reliable equally. On this assumption, migration of an important data block to unstable storage might cause degradation of the availability of the data block. In this paper, we propose a scheme to choose the destination of data block migration by evaluating the nodes' reliability along with the performance of storage devices. This scheme aims to improve system stability by preventing to locate a critical data onto an unstable storage device. The storage nodes which lack stability are also utilized for storing a data block which can be easily regenerated or duplicated. The results of the preliminary investigation show that a large number of warning logs from Machine Check Architecture may suggest a sign of unexpected system shutdown.