Panjai Tantasanawong

Server virtualization by user behaviour model using a data mining technique — A preliminary study

Server virtualization is the masking of server resources, including the number and identity of individual physical servers, processors, and operating systems, from server users. However, the problem of tuning dynamic resource allocation is a novelty. Managing heterogeneous workloads running within virtual machines is an interesting and challenging topic of server virtualization. This research applied association rule discovery, which is one of the data mining techniques to predict level of user access. The results illustrate that performance of the predictive model for a proxy server is 86.86%. The performance of the predictive model for a web server is 87.18%. Additionally, user behaviors for proxy and web servers are visualized. The results suggest that user behaviors are different in term of workload, day and time usage. This preliminary study may be an approach to improve management of data centers running heterogeneous workloads using server virtualization.

Analyzing user behavior from server logs for improved virtualization management

Many organizations are shifting to virtualization of servers to improve operational efficiency and reduce capital expenditures. Virtualization helps consolidate servers, improve hardware utilization and reduce the consumption of power and physical space in the data center. However, the problem of tuning dynamic resource allocation is a novelty. Managing heterogeneous workloads running within virtual machines is an interesting and challenging topic of server virtualization. This paper presents an analysis of trending user access and their data size requirements, both of which are meaningful for virtualization management. The results suggest that user behaviors are different in terms of workload, day and time of usage. The data size requested by users also differs across time periods. This preliminary study may offer an approach to improve management of data centers running heterogeneous workloads using server virtualization.

A Trustable Software with a Dynamic Loop Control Mechanism

Unexpected infinite loop during software deployment period is an undesirable for every organisation since it can cause critical damages in various aspects. Most of the current protection methods will perform during the software development process using verification and validation processes. However, these methods will not cover the dynamic loop control mechanism where the termination of the loop is relied on the value obtained during runtime process. Thus, many possible error values cannot be protected based on human’s limitation. Therefore, this paper proposes architecture for checking and terminating infinite loop situation during the runtime process. Additionally, the proposed mechanism can completely prevent the unlimited loop for these dynamic situations. The testing results have shown that the proposed solution provides the trustable system for every organisation.

Improving Heterogeneous Workload Performance in Server Virtualization through User Behavior Schedules

One of the most attractive topics today is servervirtualization. Virtualization is a method of running multipleindependent virtual operating systems on a single physicalcomputer. It is a way of maximizing physical resources tomaximize the investment in hardware. This concept can help toconsolidate servers, improve hardware utilization and reducethe consumption of power and physical space in the datacenter. However, in an environment where workloads sharethe underlying platforms, the problem of configurationcontrols on sharing dynamic resource allocation becomes achallenge. In this paper, we present the improved performanceon heterogeneous workload in server virtualization using userbehavior schedules. We show a new concept for managingworkloads by involving user behavior in each type of serviceworkload. The result shows that user behaviors are different ineach type of service workload and time. This preliminary studymay be an approach to improve management of data centersrunning heterogeneous workloads using server virtualization.