The growth in the adoption of cloud computing is driven by distinct and clear benefits for both cloud customers and cloud providers. However, the increase in the number of cloud providers as well as in the variety of offerings from each provider has made it harder for customers to choose. At the same time, the number of options to build a cloud infrastructure, from cloud management platforms to different interconnection and storage technologies, also poses a challenge for cloud providers. In this context, cloud experiments are as necessary as they are labor intensive. Cloud Bench [1] is an open-source framework that automates cloud-scale evaluation and benchmarking through the running of controlled experiments, where complex applications are automatically deployed. Experiments are described through experiment plans, containing directives with enough descriptive power to make the experiment descriptions brief while allowing for customizable multi-parameter variation. Experiments can be executed in multiple clouds using a single interface. Cloud Bench is capable of managing experiments spread across multiple regions and for long periods of time. The modular approach adopted allows it to be easily extended to accommodate new cloud infrastructure APIs and benchmark applications, directly by external users. A built-in data collection system collects, aggregates and stores metrics for cloud management activities (such as VM provisioning and VM image capture) and application runtime information. Experiments can be conducted in a highly controllable fashion, in order to assess the stability, scalability and reliability of multiple cloud configurations. We demonstrate Cloud Bench's main characteristics through the evaluation of an Open Stack installation, including experiments with approximately 1200 simultaneous VMs at an arrival rate of up to 400 VMs/hour.

Memory over commitment enables cloud providers to host more virtual machines on a single physical server, exploiting spare CPU and I/O capacity when physical memory becomes the bottleneck for virtual machine deployment. However, over commiting memory can also cause noticeable application performance degradation. We present Ginkgo, a policy framework for over omitting memory in an informed and automated fashion. By directly correlating application-level performance to memory, Ginkgo automates the redistribution of scarce memory across all virtual machines, satisfying performance and capacity constraints. Ginkgo also achieves memory gains for traditionally fixed-size Java applications by coordinating the redistribution of available memory with the activities of the Java Virtual Machine heap. When compared to a non-over commited system, Ginkgo runs the Day Trader 2.0 and SPEC Web 2009 benchmarks with the same number of virtual machines while saving up to 73% (50% omitting free space) of a physical server's memory while keeping application performance degradation within 7%.

We present MemX -- a distributed system that virtualizes cluster-wide memory to support data-intensive and large memory workloads in virtual machines (VMs). MemX provides a number of benefits in virtualized settings: (1) VM workloads that access large datasets can perform low-latency I/O over virtualized cluster-wide memory; (2) VMs can transparently execute very large memory applications that require more memory than physical DRAM present in the host machine; (3) MemX reduces the effective memory usage of the cluster by de-duplicating pages that have identical content; (4) existing applications do not require any modifications to benefit from MemX such as the use of special APIs, libraries, recompilation, or relinking; and (5) MemX supports live migration of large-footprint VMs by eliminating the need to migrate part of their memory footprint resident on other nodes. Detailed evaluations of our MemX prototype show that large dataset applications and multiple concurrent VMs achieve significant performance improvements using MemX compared against virtualized local and iSCSI disks.

We present the design, implementation, and evaluation of post-copy based live migration for virtual machines (VMs) across a Gigabit LAN. Live migration is an indispensable feature in today's virtualization technologies. Post-copy migration defers the transfer of a VM's memory contents until after its processor state has been sent to the target host. This deferral is in contrast to the traditional pre-copy approach, which first copies the memory state over multiple iterations followed by a final transfer of the processor state. The post-copy strategy can provide a "win-win" by reducing total migration time closer to its equivalent time achieved by non-live VM migration. This is done while maintaining the liveness benefits of the pre-copy approach. We compare post-copy extensively against the traditional pre-copy approach on top of the Xen Hypervisor. Using a range of VM workloads we show improvements in several migration metrics including pages transferred, total migration time and network overhead. We facilitate the use of post-copy with adaptive pre-paging in order to eliminate all duplicate page transmissions. Our implementation is able to reduce the number of network-bound page faults to within 21% of the VM's working set for large workloads. Finally, we eliminate the transfer of free memory pages in both migration schemes through a dynamic self-ballooning (DSB) mechanism. DSB periodically releases free pages in a guest VM back to the hypervisor and significantly speeds up migration with negligible performance degradation.

There is a constant battle to break-even between continuing improvements in DRAM capacities and the demands for even more memory by modern memory-intensive high-performance applications. Such applications do not take long to hit the physical memory limit and start paging to disk, which in turn considerably slows down their performance. We tackle this problem in the Adaptive Network Memory Engine (Anemone) project by pooling together the distributed memory resources of multiple machines across a gigabit network based cluster. Anemone is a distributed memory virtualization system that can dramatically improve application performance by paging over the gigabit network to the unused memory of remote clients. Anemone provides clients with completely transparent access to a potentially unlimited amount of collective memory pool. Earlier research efforts in this area advocate significant modifications to client systems, either in terms of a specific programming interface for applications, or in terms of extensive changes to the operating systems and device drivers. In contrast, Anemone requires no modifications to either the client system or the memory-intensive application.