An Efficient Content Prefetching Method for Cloud Based Mobile Adhoc Network

Recently, Mobile Ad-hoc Networks (MANETs) has emerged as a very important research area for provisioning service for remote client through internet, cloud computing, and cellular network. This work focusses on improving image access in MANET. Various method has been present recently for reducing data access and reduce query latency. Number challenges need to addressed such as caching, content prefetching, shared access environment, dynamic high speed node mobility environment etc. As mobile adhoc network is growing rapidly for the possessions of its capability in forming provisional network without the requirement of any predefined infrastructure, improving throughput (i.e., access rate), reducing bit error rate (BER) (i.e., query latency) has been a major concern and requirement in mobile adhoc network. This work aimed at building an efficient content prefetching technique of geographically distributed data for enhancing the access rate and reduce query latency. Along with, our model can minimize processing time and cost for carrying content prefetching operation. Experiment result shows the proposed content prefetching method improves bit error rate (BER) and throughput performance.

Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.

Smart Intra-query Fault Tolerance for Massive Parallel Processing Databases

Intra-query fault tolerance has increasingly been a concern for online analytical processing, as more and more enterprises migrate data analytical systems from mainframes to commodity computers. Most massive parallel processing (MPP) databases do not support intra-query fault tolerance. They may suffer from prolonged query latency when running on unreliable commodity clusters. While SQL-on-Hadoop systems can utilize the fault tolerance support of low-level frameworks, such as MapReduce and Spark, their cost-effectiveness is not always acceptable. In this paper, we propose a smart intra-query fault tolerance (SIFT) mechanism for MPP databases. SIFT achieves fault tolerance by performing checkpointing, i.e., materializing intermediate results of selected operators. Different from existing approaches, SIFT aims at promoting query success rate within a given time. To achieve its goal, it needs to: (1) minimize query rerunning time after encountering failures and (2) introduce as less checkpointing overhead as possible. To evaluate SIFT in real-world MPP database systems, we implemented it in Greenplum. The experimental results indicate that it can improve success rate of query processing effectively, especially when working with unreliable hardware.

Performance Analysis of Query Terms during Query Phase in Virtual Databases

In early days, the queries may be executed by selecting query plans and linking throughout the execution. The main intention in such case may be to treat query execution as a process of streaming the tuples and systematize the query terms on the basis of tuples. The accuracy of routing of tuples may be obtained through semantic properties of the operators and implementation of join predicates. Considering the huge amounts of data in the dynamic environment, it may be required to locate and extract data implementing different techniques. The primary objective in this work may be to implement the virtualization along with conceptualization approaches to improve the throughput as well as turnaround time and to link to associated real databases. In such scenario, the virtual databases may be associated to enhance the computation capabilities of query plans by optimizing the performance and maintaining consistencies in the databases. Accordingly the query latency may be minimized

Exploiting Temporal and Spatial Regularities for Content Dissemination in Opportunistic Social Network

Recently, content dissemination has become more and more important for opportunistic social networks. The challenges of opportunistic content dissemination result from random movement of nodes and uncertain positions of a destination, which seriously affect the efficiency of content dissemination. In this paper, we firstly construct time-varying interest communities based on the temporal and spatial regularities of users. Next, we design a content dissemination algorithm on the basis of time-varying interest communities. Our proposed content dissemination algorithm can run in O(nlog⁡n) time. Finally, the comparisons between the proposed content dissemination algorithm and state-of-the-art content dissemination algorithms show that our proposed content dissemination algorithm can (a) keep high query success rate, (b) reduce the average query latency, (c) reduce the hop count of a query, and (d) maintain low system overhead.

Energy-Efficient Cache Invalidation in Wireless Mobile Environment

Caching at the mobile client is a potential technique that can reduce the number of uplink requests, lighten the server load, shorten the query latency and increase the data availability. A cache invalidation strategy ensures that any data item cached at a mobile client has same value as on the origin server. Traditional cache invalidation strategies make use of periodic broadcasting of invalidation reports (IRs) by the server. The IR approach suffers from long query latency, larger tuning time and poor utilization of bandwidth. Using updated invalidation report (UIR) method that replaces a small fraction of the recent updates, the query latency can be reduced. To improve upon the IR and UIR based strategies, this chapter presents a synchronous stateful cache maintenance technique called Update Report (UR). The proposed strategy outperforms the IR and UIR strategies by reducing the query latency, minimizing the disconnection overheads, optimizing the use of wireless channel and conserving the client energy.