H2O: An Autonomic, Resource-Aware Distributed Database System

This paper presents the design of an autonomic, resource-aware distributed database which enables data to be backed up and shared without complex manual administration. The database, H2O, is designed to make use of unused resources on workstation machines. Creating and maintaining highly-available, replicated database systems can be difficult for untrained users, and costly for IT departments. H2O reduces the need for manual administration by autonomically replicating data and load-balancing across machines in an enterprise. Provisioning hardware to run a database system can be unnecessarily costly as most organizations already possess large quantities of idle resources in workstation machines. H2O is designed to utilize this unused capacity by using resource availability information to place data and plan queries over workstation machines that are already being used for other tasks. This paper discusses the requirements for such a system and presents the design and implementation of H2O.Comment: Presented at SICSA PhD Conference 2010 (http://www.sicsaconf.org/

Energy-Aware Lease Scheduling in Virtualized Data Centers

Energy efficiency has become an important measurement of scheduling algorithms in virtualized data centers. One of the challenges of energy-efficient scheduling algorithms, however, is the trade-off between minimizing energy consumption and satisfying quality of service (e.g. performance, resource availability on time for reservation requests). We consider resource needs in the context of virtualized data centers of a private cloud system, which provides resource leases in terms of virtual machines (VMs) for user applications. In this paper, we propose heuristics for scheduling VMs that address the above challenge. On performance evaluation, simulated results have shown a significant reduction on total energy consumption of our proposed algorithms compared with an existing First-Come-First-Serve (FCFS) scheduling algorithm with the same fulfillment of performance requirements. We also discuss the improvement of energy saving when additionally using migration policies to the above mentioned algorithms.Comment: 10 pages, 2 figures, Proceedings of the Fifth International Conference on High Performance Scientific Computing, March 5-9, 2012, Hanoi, Vietna

Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

On Reliability-Aware Server Consolidation in Cloud Datacenters

In the past few years, datacenter (DC) energy consumption has become an important issue in technology world. Server consolidation using virtualization and virtual machine (VM) live migration allows cloud DCs to improve resource utilization and hence energy efficiency. In order to save energy, consolidation techniques try to turn off the idle servers, while because of workload fluctuations, these offline servers should be turned on to support the increased resource demands. These repeated on-off cycles could affect the hardware reliability and wear-and-tear of servers and as a result, increase the maintenance and replacement costs. In this paper we propose a holistic mathematical model for reliability-aware server consolidation with the objective of minimizing total DC costs including energy and reliability costs. In fact, we try to minimize the number of active PMs and racks, in a reliability-aware manner. We formulate the problem as a Mixed Integer Linear Programming (MILP) model which is in form of NP-complete. Finally, we evaluate the performance of our approach in different scenarios using extensive numerical MATLAB simulations.Comment: International Symposium on Parallel and Distributed Computing (ISPDC), Innsbruck, Austria, 201