An Effective Techniques Using Apriori and Logistic Methods in Cloud Computing

This paper presents a creativity data prefetching scheme on the loading servers in distributed file systems for cloud computing. The server will get and piggybacked the frequent data from the client system, after analyzing the fetched data is forward to the client machine from the server. To place this technique to work, the data about client nodes is piggybacked onto the real client I/O requests, and then forwarded to the relevant storage server. Next, dual prediction algorithms have been proposed to calculation future block access operations for directing what data should be fetched on storage servers in advance. Finally, the prefetching data can be pressed to the relevant client device from the storage server. Over a series of evaluation experiments with a group of application benchmarks, we have demonstrated that our presented initiative prefetching technique can benefit distributed file systems for cloud environments to achieve better I/O performance. In particular, configuration-limited client machines in the cloud are not answerable for predicting I/O access operations, which can certainly contribute to preferable system performance on them

Performance analysis of cloud-based cve communication architecture in comparison with the traditional client server, p2p and hybrid models

Gital et al. (2014) proposed a cloud based communication architecture for improving efficiency of collaborative virtual environment (CVE) systems in terms of Scalability and Consistency requirements. This paper evaluates the performance of the proposed CVE architecture. The metrics use for the evaluation is response time. We compare the cloud-based architecture to the traditional client server and peer-2–peer (P2P) architecture. The comparison was implemented in the CVE systems. The comparative simulation analysis of the results suggested that the CVE architecture based on cloud computing can significantly improve the performance of the CVE system

Array Requirements for Scientific Applications and an Implementation for Microsoft SQL Server

This paper outlines certain scenarios from the fields of astrophysics and fluid dynamics simulations which require high performance data warehouses that support array data type. A common feature of all these use cases is that subsetting and preprocessing the data on the server side (as far as possible inside the database server process) is necessary to avoid the client-server overhead and to minimize IO utilization. Analyzing and summarizing the requirements of the various fields help software engineers to come up with a comprehensive design of an array extension to relational database systems that covers a wide range of scientific applications. We also present a working implementation of an array data type for Microsoft SQL Server 2008 to support large-scale scientific applications. We introduce the design of the array type, results from a performance evaluation, and discuss the lessons learned from this implementation. The library can be downloaded from our website at http://voservices.net/sqlarray

An incremental database access method for autonomous interoperable databases

We investigated a number of design and performance issues of interoperable database management systems (DBMS's). The major results of our investigation were obtained in the areas of client-server database architectures for heterogeneous DBMS's, incremental computation models, buffer management techniques, and query optimization. We finished a prototype of an advanced client-server workstation-based DBMS which allows access to multiple heterogeneous commercial DBMS's. Experiments and simulations were then run to compare its performance with the standard client-server architectures. The focus of this research was on adaptive optimization methods of heterogeneous database systems. Adaptive buffer management accounts for the random and object-oriented access methods for which no known characterization of the access patterns exists. Adaptive query optimization means that value distributions and selectives, which play the most significant role in query plan evaluation, are continuously refined to reflect the actual values as opposed to static ones that are computed off-line. Query feedback is a concept that was first introduced to the literature by our group. We employed query feedback for both adaptive buffer management and for computing value distributions and selectivities. For adaptive buffer management, we use the page faults of prior executions to achieve more 'informed' management decisions. For the estimation of the distributions of the selectivities, we use curve-fitting techniques, such as least squares and splines, for regressing on these values

New directions for remote data integrity checking of cloud storage

Cloud storage services allow data owners to outsource their data, and thus reduce their workload and cost in data storage and management. However, most data owners today are still reluctant to outsource their data to the cloud storage providers (CSP), simply because they do not trust the CSPs, and have no confidence that the CSPs will secure their valuable data. This dissertation focuses on Remote Data Checking (RDC), a collection of protocols which can allow a client (data owner) to check the integrity of data outsourced at an untrusted server, and thus to audit whether the server fulfills its contractual obligations. Robustness has not been considered for the dynamic RDCs in the literature. The R-DPDP scheme being designed is the first RDC scheme that provides robustness and, at the same time, supports dynamic data updates, while requiring small, constant, client storage. The main challenge that has to be overcome is to reduce the client-server communication during updates under an adversarial setting. A security analysis for R-DPDP is provided. Single-server RDCs are useful to detect server misbehavior, but do not have provisions to recover damaged data. Thus in practice, they should be extended to a distributed setting, in which the data is stored redundantly at multiple servers. The client can use RDC to check each server and, upon having detected a corrupted server, it can repair this server by retrieving data from healthy servers, so that the reliability level can be maintained. Previously, RDC has been investigated for replication-based and erasure coding-based distributed storage systems. However, RDC has not been investigated for network coding-based distributed storage systems that rely on untrusted servers. RDC-NC is the first RDC scheme for network coding-based distributed storage systems to ensure data remain intact when faced with data corruption, replay, and pollution attacks. Experimental evaluation shows that RDC-NC is inexpensive for both the clients and the servers. The setting considered so far outsources the storage of the data, but the data owner is still heavily involved in the data management process (especially during the repair of damaged data). A new paradigm is proposed, in which the data owner fully outsources both the data storage and the management of the data. In traditional distributed RDC schemes, the repair phase imposes a significant burden on the client, who needs to expend a significant amount of computation and communication, thus, it is very difficult to keep the client lightweight. A new self-repairing concept is developed, in which the servers are responsible to repair the corruption, while the client acts as a lightweight coordinator during repair. To realize this new concept, two novel RDC schemes, RDC-SR and ERDC-SR, are designed for replication-based distributed storage systems, which enable Server-side Repair and minimize the load on the client side. Version control systems (VCS) provide the ability to track and control changes made to the data over time. The changes are usually stored in a VCS repository which, due to its massive size, is often hosted at an untrusted CSP. RDC can be used to address concerns about the untrusted nature of the VCS server by allowing a data owner to periodically check that the server continues to store the data. The RDC-AVCS scheme being designed relies on RDC to ensure all the data versions are retrievable from the untrusted server over time. The RDC-AVCS prototype built on top of Apache SVN only incurs a modest decrease in performance compared to a regular (non-secure) SVN system

Динамическая система балансировки нагрузки веб-серверов

В статті розглянута задача розподілення навантаження у кластері серверів. Запропонована система динамічного балансування, що базується на роботі диспетчера і забезпечує балансування вхідного навантаження на веб-сервер.Usage of Internet and frequent accesses of large amount of multimedia data increase the network traffic. Performance evaluation and high availability of server are important factors for resolving this problem using cluster based systems. There are several low-cost servers using the load sharing cluster system which are connected to high speed network, and apply load balancing technique between servers. It offers high computing power and high availability. The overall increase in traffic on the World Wide Web is augmenting user perceived response times from popular Web sites, especially in conjunction with special events. A distributed website server can provide scalability and flexibility to manage with growing client demands. To improve the response time of the web server, the evident approach is to have multiple servers. Efficiency of a replicated web server system will depend on the way of distributed incoming requests among these replicas. A distributed Web-server architectures schedule client requests among the multiple server nodes in a user transparent way that affects the scalability and availability. The aim of this paper is the development of a load balancing techniques on distributed Web-server systems.Рассмотрена задача распределения нагрузки в кластере серверов. Предложена система динамической балансировки, что базируется на работе диспетчера и обеспечивает балансировку входной нагрузки на веб-сервер

Secure set-based policy checking and its application to password registration

Policies are the corner stones of today's computer systems. They define secure states and safe operations. A common problem with policies is that their enforcement is often in con ict with user privacy. In order to check the satisfiability of a policy, a server usually needs to collect from a client some information which may be private. In this work we introduce the notion of secure set-based policy checking (SPC) that allows the server to verify policies while preserving the client's privacy. SPC is a generic protocol that can be applied in many policy-based systems. As an example, we show how to use SPC to build a password registration protocol so that a server can check whether a client's password is compliant with its password policy without seeing the password. We also analyse SPC and the password registration protocol and provide security proofs. To demonstrate the practicality of the proposed primitives, we report performance evaluation results based on a prototype implementation of the password registration protoco

Methodology for modeling high performance distributed and parallel systems

Performance modeling of distributed and parallel systems is of considerable importance to the high performance computing community. To achieve high performance, proper task or process assignment and data or file allocation among processing sites is essential. This dissertation describes an elegant approach to model distributed and parallel systems, which combines the optimal static solutions for data allocation with dynamic policies for task assignment. A performance-efficient system model is developed using analytical tools and techniques. The system model is accomplished in three steps. First, the basic client-server model which allows only data transfer is evaluated. A prediction and evaluation method is developed to examine the system behavior and estimate performance measures. The method is based on known product form queueing networks. The next step extends the model so that each site of the system behaves as both client and server. A data-allocation strategy is designed at this stage which optimally assigns the data to the processing sites. The strategy is based on flow deviation technique in queueing models. The third stage considers process-migration policies. A novel on-line adaptive load-balancing algorithm is proposed which dynamically migrates processes and transfers data among different sites to minimize the job execution cost. The gradient-descent rule is used to optimize the cost function, which expresses the cost of process execution at different processing sites. The accuracy of the prediction method and the effectiveness of the analytical techniques is established by the simulations. The modeling procedure described here is general and applicable to any message-passing distributed and parallel system. The proposed techniques and tools can be easily utilized in other related areas such as networking and operating systems. This work contributes significantly towards the design of distributed and parallel systems where performance is critical