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Abstract
Abstract Cloud computing allows for sharing computing resources, such as CPU, application platforms, and services. Monitoring these resources would benefit from an accurate prediction model that significantly reduces the network overhead caused by unnecessary push and pull messages. However, accurate prediction of the computing resources is considered hard due to the dynamic nature of cloud computing. In this paper, two monitoring mechanisms have been developed; the first is based on a Continuous Time Markov Chain (CTMC) model and the second is based on a Discrete Time Markov Chain (DTMC) model. It is found that The CTMC-based mechanism outperformed the DTMC-based mechanism. Also, the CTMC-based mechanism outperformed the Grid Resource Information Retrieval (GRIR) mechanism, which does not employ prediction, and a prediction-based mechanism, which uses Markov Chains to predict the time interval of monitoring mobile grid resources, in monitoring cloud resources.
Abstract Cloud computing is a new generation of computing based on virtualization technology.
An important application on the cloud is the Database Management Systems (DBMSs). The work
in this paper concerns about the Virtual Design Advisor (VDA). The VDA is considered a solution
for the problem of optimizing the performance of DBMS instances running on virtual machines
that share a common physical machine pool. It needs to calibrate the tuning parameters of the
DBMS’s query optimizer in order to operate in a what-if mode to accurately and quickly estimate
the cost of database workloads running in virtual machines with varying resource allocation.
The calibration process in the VDA had been done manually. This manual calibration process is
considered a complex, time-consuming task because each time a DBMS has to run on a different
server infrastructure or to replace with another on the same server, the calibration process potentially
has to be repeated. According to the work in this paper, an Automatic Calibration Tool
(ACT) has been introduced to automate the calibration process.
Also, a Greedy Particle Swarm Optimization (GPSO) search algorithm has been proposed and
implemented in the VDA instead of the existed greedy algorithm to prevent the local optimum
states from trapping the search process from reaching global optima. The main function of this
algorithm is to minimize the estimated cost and enhance the VMs configurations.
The ACT tool and the GPSO search algorithm have been implemented and evaluated using TPC-
H benchmark queries against PostgreSQL instances hosted in Virtual Machines (VMs) on the Xen
virtualization environment.
]