A Research Perspective on Data Management Techniques for Federated Cloud Environment

Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored

Middleware architectures for the smart grid: A survey on the state-of-the-art, taxonomy and main open issues

The integration of small-scale renewable energy sources in the smart grid depends on several challenges that must be overcome. One of them is the presence of devices with very different characteristics present in the grid or how they can interact among them in terms of interoperability and data sharing. While this issue is usually solved by implementing a middleware layer among the available pieces of equipment in order to hide any hardware heterogeneity and offer the application layer a collection of homogenous resources to access lower levels, the variety and differences among them make the definition of what is needed in each particular case challenging. This paper offers a description of the most prominent middleware architectures for the smart grid and assesses the functionalities they have, considering the performance and features expected from them in the context of this application domain

Performance Evaluation of Function Composition in Middlewares supporting FaaS for Serverless computing

The concept of Serverless Computing is a new and exciting aspect of cloud computing that involves the deployment of small pieces of software applications and services as serverless functions. Serverless computing architecture enables the cloud provider to fully manage the execution of a server's code, eliminating the need for customers to develop and deploy the traditional underlying infrastructure required for running applications and programs. Even though big tech companies are extensively utilizing serverless computing in their products and investing billions on this novel but affirmed technology, it is affected by various problems still considered an open field in research. In fact, by definition, FaaS architectures are geographically dislocated and consequently subject to event propagation delays that can significantly degrade the overall system performance. What is generally done, is to reduce as much as possible cumulative delays especially if attributable to the infrastructure itself that could determine a greater or lesser competitiveness on the market. The background idea, which becomes the leit motiv throughout this work, is to develop and assess the performance, and thus the validity, of a Message-Oriented Middleware-centric serverless platform architecture promising to enable advanced analytics capabilities and better overall performance, without renouncing the essential characteristic of scalability in the context of distributed systems. Experiments in emulated conditions show that applying the MOM coordination co-locality principle improves the end-to-end delay and data processing performance

Database Usability Enhancement in Data Exploration

Database usability has become an important research topic over the last decade. In the early days, database management systems were maintained by sophisticated users like database administrators. Today, due to the availability of data and computing resources, more non-expert users are involved in database computation. From their point of view, database systems lack ease of use. So researchers believe that usability is as important as the performance and functionality of databases and therefore developed many techniques such as natural language interface to enhance the ease of use of databases. In this thesis, we find some deeper technical issues in database usability, so we look at several core database technologies to further improve the ease of use of databases in two dimensions: we help users process data and exploit computing capacities. We start by helping users find the data. In the real world, public data is everywhere on the Web, but it is scattered around. We extract a prototype relational knowledge base to solve this problem. We start from the most basic binary mapping relationships (sometimes also named bridge tables) between entities from the web. This mapping relationship facilitates many data transformation applications such as auto-correct, auto-fill, and auto-join. After finding the data, we help users explore the data. When users issue queries to explore the data, their query results may contain too many items. So the system designer has to present a small subset of representative and diverse items rather than all items. This is known as the query result diversification problem. We propose the RC-Index, which helps to solve the diversification problem by significantly reducing the number of items that must be retrieved by the database to form a diverse set of a desired size. It is nearly an order of magnitude faster than the state-of-the-art and has a good performance guarantee, which improves the ease of use of databases in terms of querying. Finally, we shift our focus from data to computing capacities. We propose a framework to help users choose configurations in the cloud. Cloud computing has revolutionized data analysis, but choosing the right configuration is challenging because the common pricing mechanism of the public cloud is too complicated. Users have to consider low-level resources to find the best plan for their computational tasks. To address this issue, we propose a new market-based framework for pricing computational tasks in the cloud. We introduce agents to help users configure their personalized databases, which improves the ease of use of databases in the cloud