We introduce Satori, an efficient and effective system for sharing memory in virtualised systems. Satori uses enlightenments in guest operating systems to detect sharing opportunities and manage the surplus memory that results from sharing. Our approach has three key benefits over existing systems: it is better able to detect short-lived sharing opportunities, it is efficient and incurs negligible overhead, and it maintains performance isolation between virtual machines. We present Satori in terms of hypervisor-agnostic design decisions, and also discuss our implementation for the Xen virtual machine monitor. In our evaluation, we show that Satori quickly exploits up to 94% of the maximum possible sharing with insignificant performance overhead. Furthermore, we demonstrate workloads where the additional memory improves macrobenchmark performance by a factor of two. 1

The increasing demand for storage and computation has driven the growth of large data centers–the massive server farms that run many of today’s Internet and business applications. A data center can comprise many thousands of servers and can use as much energy as a small city. The massive amounts of computation power required to drive these systems results in many challenging and interesting distributed systems and resource management problems. In this thesis I investigate challenges related to data centers, with a particular emphasis on how new virtualization technologies can be used to simplify deployment, improve resource efficiency, and reduce the cost of reliability. I first study problems that relate the initial capacity planning required when deploying applications into a virtualized data center. I demonstrate how models iv of virtualization overheads can be utilized to accurately predict the resource needs of virtualized applications, allowing them to be smoothly transitioned into a data center. I next study how memory similarity can be used to guide placement when

Crash dump, or core dump is the typical way to save memory image on system crash for future offline debugging and analysis. However, for typical server machines with likely abundant memory, the time of core dump can significantly increase the mean time to repair (MTTR) by delaying the reboot-based recovery, while not dumping the failure context for analysis would risk recurring crashes on the same problems. In this paper, we propose several optimization techniques for core dump in virtualized environments, in order to shorten the MTTR of consolidated virtual machines during crashes. First, we parallelize the process of crash dump and the process of rebooting the crashed VM, by dynamically reclaiming and allocating memory between the crashed VM and the newly spawned VM. Second, we use the virtual machine management layer to introspect the critical data structures of the crashed VM to filter out the dump of unused memory. Finally, we implement disk I/O rate control between core dump and the newly spawned VM according to user-tuned rate control policy to balance the time of crash dump and quality of services in the recovery VM. We have implemented a working prototype, Vicover, that optimizes core dump on system crash of a virtual machine in Xen, to minimize the MTTR of core dump and recovery as a whole. In our experiment on a virtualized TPC-W server, Vicover shortens the downtime caused by crash dump by around 5X.

Abstract—A virtualized system incorporates multiple systems into a single physical computer as virtual domains. A lot of data centers and server systems have been organized using virtualization technology to merge several computer systems. On the shared system, resource manager is the key affecting the performance. However, the resource management in current systems does not provide accurate resource allocation, because it only utilizes information from virtual machines and disregards the state of running applications. The paper demonstrates the CPU resource controller taking the state of application as inputs to produce the minimum resource retaining application performance in acceptable level. In particular, it employs two-layered controller. The first layer controller makes resource request based on the relationship between the state and resource demand of each application, modeled by fuzzy control theory. This approach is efficient to represent resource allocation model since fuzzy control theory deals imprecise and uncertain problems. The second layer controller adjusts the requests to the system capacity and builds the layout of resource capacity based on the relative Quality of Service performances between applications. For the separation of resource, common resource controller imposes a hard limit on the amount of resource a given domain can consume. The controller allocates resource with most effective capacity configuration. Under certain specified conditions, the controller does not set the capacities and allows domains to use the free time if the resource is idle. This results in eliminating unused resources and achieves relative high resource usage. Finally, the resource controller is evaluated with a virtualized system, and its advantages over conventional resource allocation methods are shown. I.