running VMs do interfere with each other.
The co-located VMs share physical resources of the server with each other [31, 32]. The sharing creates resource contention for the VMs resulting in the performance variation. Measuring or predicting this performance variation is not a straightforward process. Various resources like CPU, memory and I/O are virtualized in different ways. Different virtualized resource behave differently during consolidation, and their effects on the consolidation are not the same [33].
Consolidation is used in all modern servers; the resource utilization depends on how many VMs can be run on a server without creating too much resource con- tention [32]. As data centers are getting bigger and consuming more energy the re- source utilization efficiency is becoming a more pressing issue. For increasing the resource utilization efficiency of the servers, it is essential to study the reaction of the consolidated VMs.
What is more, existing scheduling algorithms are designed for physical machines [34]. As more and more servers are getting virtualized; it is essential to study the perfor- mance variation of the consolidated server to determine how to make the scheduling algorithms more efficient in the virtualized environment.
1.2
Contributions
Virtualization is essential for providing Cloud services. This dissertation deals with an- alyzing and improving the performance of consolidated VMs. Many important issues like resource usages and energy efficiency depend on the consolidated VMs perfor- mance. The primary research contributions of this dissertation are summarized below. 1. All experiments are conducted on real virtualized systems. Results from three well known hypervisors are used throughout the dissertation; they are VMware
ESXi[35–37], Citrix XenServer, and Xen [38, 39]. Multiple VMs are set up on each hypervisor and experiments are done with real workloads. The experimen- tal data are collected from both the VMs and hypervisors for analysis.
2. Real system data is used throughout the dissertation, and no simulation is used in any experiment. There are fields where simulation provides excellent results; however, virtualization system research is not one of them.
1.2. CONTRIBUTIONS 6
In each chapter of the dissertation, different experiments have been designed and carried out with consolidated virtual machines to collect system data. For example in chapter 4 experiments are conducted to collected CPU, memory, and I/O usages data of the VMs from the hardware. Based on the analysis of this data further experiments have been designed for chapter 5.
In chapter 5, the task execution finish times collected from the Operating System (OS) is used analysis. Similarly, in chapter 7 experiments are designed to collect system events and interrupt data from the hardware. Thus, all the chapters are logically connected, and analysis of experimental system data of one chapter is the basis for the experiments of the subsequent chapters.
3. Experiments are conducted with the resource consumption and performance variation of VMs. It is known that the consolidated VMs of a server interfere with the performance of each other [31, 32, 40–42]. Simultaneously running VMs of a server compete with each other for three primary computing resources; CPU, memory, and I/O.
Furthermore, different VMs use the resources differently; hence, their contribu- tion to resource contention is different. Experiments have been done with vari- ous resource usages pattern and number of consolidated VMs. The experimental results demonstrate how the VM performance is affected by different types of resource contention.
4. A benchmarking technique is presented to analyze the VM consolidation perfor- mance, called the Incremental Consolidation Benchmark Method (ICBM) [43]. In this method, the numbers of various resource intensive VMs are increased systematically. In turn, it increases the resource contention in the system and the execution finish times of tasks start to rise.
As the increasing number of VMs compete for the resources, some VMs are deprived of the resources and can not continue to execute properly. It has con- sequences on the tasks running on the affected VMs. As a result, the tasks on the VMs take longer to finish execution. Therefore, in the ICBM, the number of consolidated VMs are systematically manipulated to observe their effect on the overall performance.
1.2. CONTRIBUTIONS 7
5. In this dissertation, the task execution time is used as a performance metric. The experimental results show that the task execution time variation can be a good indicator of the consolidation performance. The resource usage intensity varies from task to task.
First of all, several tasks are chosen for the experiments whose resource intensi- ties are known; then, they are combined to create combinational workloads. The tasks are combined in several patterns to create several workloads. The work- loads are run on three hypervisors to collect the data about task execution time variation. The collected data is then compared and analyzed. The experimental results show that different resource intensive tasks react differently to the VM consolidation.
6. To efficiently experiments with the virtualized system, a VM workflow schedul- ing framework is developed [44]. It is an application entirely written in Java. It can schedule parallel tasks and workflow on multiple VMs. Also, once the task execution is finished, the application can retrieve the data for later analysis. The scheduler can simultaneously connect to multiple hypervisors and run work- loads concurrently. The workloads are defined in a separate file, which defines which task to run at which VM and in what pattern. Executing the tasks in spe- cific patterns is the key to analyze the execution time variation of consolidated VMs.
The framework is built with the objective to apply the ICBM on multiple hyper- visors simultaneously. A server can run many VMs at a time, and it is hard to manage all the execute tasks manually. The new scheduler can execute work- loads with a large number of VMs automatically.
7. Experiments have been conducted with various workload and hypervisors. The results show that the proposed methodology, ICBM is a powerful tool for ana- lyzing resource contention of VMs [45]. Furthermore, experiments have been conducted with a real-world parallel workflow to test the effectiveness of ICBM. Data collected through the ICBM can also be used to predict the task execution time variation due to resource contention of VMs. First, a set of workloads is run and their execution finish times are profiled. Then, the profiled-data is used