A B M Moniruzzaman

One of the key advances in resolving the “big-data” problem has been the emergence of an
alternative database technology. Today, classic RDBMS are complemented by a rich set of
alternative Data Management Systems (DMS) specially designed to handle the volume,
variety, velocity and variability ofBig Data collections; these DMS include NoSQL, NewSQL
and Search-based systems. NewSQL is a class of modern relational database management
systems (RDBMS) that provide the same scalable performance of NoSQL systems for online
transaction processing (OLTP) read-write workloads while still maintaining the ACID
guarantees of a traditional database system. This paper discusses about NewSQL data
management system; and compares with NoSQL and with traditional database system. This
paper covers architecture, characteristics, classification of NewSQL databases for online
transaction processing (OLTP) for Big data management. It also provides the list ofpopular
NoSQL as well as NewSQL databases in separate categorized tables. This paper compares
SQL based RDBMS, NoSQL and NewSQL databases with set of metrics; as well as,
addressed some research issues ofNoSQL and NewSQL.

This article proposes a design and implementation
of a low cost two-tier architecture model for high availability
cluster combined with load-balancing and shared storage
technology to achieve desired scale of three-tier architecture for application load balancing e.g. web servers. The research work proposes a design that physically omits Network File System (NFS) server nodes and implements NFS server functionalities within the cluster nodes, through Red Hat Cluster Suite (RHCS) with High Availability (HA) proxy load balancing technologies. In order to achieve a low-cost implementation in terms of investment in hardware and computing solutions, the proposed architecture will be beneficial. This system intends to provide steady service despite any system components fails due to uncertainly such as network system, storage and applications.

Cloud Computing is becoming a viable computing solution for services oriented computing. Several open-source cloud solutions are available to these supports. Open-source software stacks offer a huge amount of customizability  without huge licensing fees. As a result, open source software are widely used for designing cloud, and private clouds are being built increasingly in the open source way. Numerous contributions have been made by the open-source community related to private-IaaS-cloud. OpenNebula - a cloud platform is one of the popular private cloud management software. However, little has been done to systematically investigate the performance evaluation of this open-source cloud solution in the existing literature. The performance evaluation aids new and existing research, industry and international projects when selecting OpenNebula software to their work. The objective of this paper is to evaluate the load-balancing performance of the OpenNebula cloud management software. For the performance evaluation, the OpenNebula cloud management software is installed and configured as a prototype implementation and tested on the DIU Cloud Lab. In this paper, two set of experiments are conducted to identify the load balancing performance of the OpenNebula
cloud management platform- (1) Delete and Add Virtual
Machine (VM) from OpenNebula cloud platform; (2) Mapping
Physical Hosts to Virtual Machines (VMs) in the OpenNebula
cloud platform.

This paper addresses the impact of Virtual Memory Streaming (VMS) technique in provisioning virtual machines
(VMs) in cloud environment. VMS is a scaling virtualization
technology that allows different virtual machines rapid scale,
high performance, and increase hardware utilization. Traditional hypervisors do not support true no-downtime live migration, and its lack of memory oversubscription can hurt the economics of a private cloud deployment by limiting the number of VMs on each host. VMS brings together several advanced hypervisor memory management techniques including granular page sharing, dynamic memory footprint management, live migration, read caching, and a unique virtual machine cloning capability. An architecture model is described, together with a proof-of-concept implementation, that VMS dynamically scaling of virtualized infrastructure with true live migration and cloning of VMs. This paper argues that VMS for Cloud allows requiring significantly reduced server memory and reducing the time for virtualized resource scaling by instantly adding more virtual machines.