Providing Transaction Class-Based QoS in In-Memory Data Grids via Machine Learning

Elastic architectures and the ”pay-as-you-go” resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing platforms, mainly thanks to their ability to be dynamically (re-)sized and tuned. Anyway, when specific QoS requirements have to be met, this kind of architectures have revealed to be complex to be managed by humans. Particularly, their management is a very complex task without the stand of mechanisms supporting run-time automatic sizing/tuning of the data platform and the underlying (virtual) hardware resources provided by the cloud. In this paper, we present a neural network-based architecture where the system is constantly and automatically re-configured, particularly in terms of computing resources

Introducing Virtual Law offices in the Existing Judiciary

With the rise of omnipresent foundation of computing resources over the past years, every IT Setup is expanding their horizons in the Cloud services and related technologies. Cloud provides dynamically scalable virtualized computing resources as a service over the Internet and this key characteristic differentiates it from traditional computing paradigm. It is the application of Cloud and mobile computing technologies for improving communication between lawyers, their clients, and any other person involved. This framework basically digitalizes the existing judicial file system to form an e-library using Clouds infrastructure and using the internet services like GPRS or GSM / CDMA or 3G/4G etc for information retrieval from this e-library after the request has received proper authorization and authentication of the regulatory body. Data is received from both Web and mobile based applications so that each person can access the judicial data anytime and anywhere. For the realization of this system, a web interface is created with e-library serving as the main database and with user-friendly interface to do the above data acquisition and analysis which ultimately gives pace to the slow process of case management

Openstack Cloud application catalog

Cloud computing has transformed IT operations throughout many industries and organizations, enabling agile self-service models that allow users to get configurable resources in minutes or seconds. CERN has implemented an Openstack cloud that is used by physicists to analyze data from experiments and by other CERN staff to implement various services. The Openstack Murano project provides a cloud application catalog, which is an opportunity to give users of the CERN cloud the option to quickly deploy standardized application environments in a self-service model. Openstack Murano was successfully deployed in CERN’s cloud, creating the necessary configuration management modules and adapting its code to work in CERN’s Openstack production environment. The application catalog is available internally, providing several databases and application servers that can be deployed in minutes with just a few clicks. More applications can be added in the future to the application catalog, with the potential to serve different use cases for the cloud at CERN

Relational Cloud: The Case for a Database Service

In this paper, we make the case for â databases as a serviceâ (DaaS), with two target scenarios in mind: (i) consolidation of data management functionality for large organizations and (ii) outsourcing data management to a cloud-based service provider for small/medium organizations. We analyze the many challenges to be faced, and discuss the design of a database service we are building, called Relational Cloud. The system has been designed from scratch and combines many recent advances and novel solutions. The prototype we present exploits multiple dedicated storage engines, provides high-availability via transparent replication, supports automatic workload partitioning and live data migration, and provides serializable distributed transactions. While the system is still under active development, we are able to present promising initial results that showcase the key features of our system. The tests are based on TPC benchmarks and real-world data from epinions.com, and show our partitioning, scalability and balancing capabilities

DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey

Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts