Recent desktop and mobile processors often integrate CPU and GPU onto the same die. The limited memory bandwidth of these integrated architectures can negatively affect the performance of data-parallel workloads when all computational resources are active. The combination of active CPU and GPU cores achieving the maximum performance depends on a workload's characteristics, making manual tuning a time-consuming task. Dopia is a fully automated framework that improves the performance of data-parallel workloads by adjusting the Degree Of Parallelism on Integrated Architectures. Dopia transparently analyzes and rewrites OpenCL kernels before executing them with the number of CPU and GPU cores expected to yield the best performance. Evaluated on AMD and Intel integrated processors, Dopia achieves 84% of the maximum performance attainable by an oracle.

High-performance computing (HPC) clusters suffer from an overall low memory utilization that is caused by the node-centric memory allocation combined with the variable memory requirements of HPC workloads. The recent provisioning of nodes with terabytes of memory to accommodate workloads with extreme peak memory requirements further exacerbates the problem. Memory disaggregation is viewed as a promising remedy to increase overall resource utilization and enable cost-effective up-scaling and efficient operation of HPC clusters, however, the overhead of demand paging in virtual memory management has so far hindered performant implementations. To overcome these limitations, this work presents RackMem, an efficient implementation of disaggregated memory for rack scale computing. RackMem addresses the shortcomings of Linux's demand paging algorithm and automatically adapts to the memory access patterns of individual processes to minimize the inherent overhead of remote memory accesses. Evaluated on a cluster with an InfiniBand interconnect, RackMem outperforms the state-of-the-art RDMA implementation and Linux's virtual memory paging by a significant margin. RackMem's custom demand paging implementation achieves a tail latency that is two orders of magnitude better than that of the Linux kernel. Compared to the state-of-the-art remote paging solution, RackMem achieves a 28% higher throughput and a 44% lower tail latency for a wide variety of real-world workloads.

Live migration is one of the key technologies to improve data center utilization, power efficiency, and maintenance. Various live migration algorithms have been proposed; each exhibiting distinct characteristics in terms of completion time, amount of data transferred, virtual machine (VM) downtime, and VM performance degradation. To make matters worse, not only the migration algorithm but also the applications running inside the migrated VM affect the different performance metrics. With service-level agreements and operational constraints in place, choosing the optimal live migration technique has so far been an open question. In this work, we propose an adaptive machine learning-based model that is able to predict with high accuracy the key characteristics of live migration in dependence of the migration algorithm and the workload running inside the VM. We discuss the important input parameters for accurately modeling the target metrics, and describe how to profile them with little overhead. Compared to existing work, we are not only able to model all commonly used migration algorithms but also predict important metrics that have not been considered so far such as the performance degradation of the VM. In a comparison with the state-of-the-art, we show that the proposed model outperforms existing work by a factor 2 to 5.

The ability to save the state of a running virtual machine (VM) for later restoration is an important tool for home, server, and virtual desktop cloud (VDC) environments in order to achieve optimal and balanced hardware utilization. With guest memory sizes of four to eight gigabytes being the norm the time- and space-overhead of storing VM checkpoints still prevents an effective use of the technique. This work presents a method for fast and space-efficient checkpointing of VMs. Based on the observation that operating systems cache disk blocks in memory, the proposed technique transparently intercepts I/O operations and maintains an up-to-date mapping of memory pages and disk blocks containing identical data. At a checkpoint, those memory pages are excluded from the checkpoint image leading to a significant reduction of both the time and space required to take a checkpoint of a running VM. The broad applicability and good performance of the proposed method is demonstrated by an extensive set of experiments. We have implemented the technique for para-virtualized (PV), PVHVM, and fully-virtualized (HVM) guests in the Xen hypervisor. In comparison with an unmodified Xen hypervisor, experiments with Linux and Windows guests, on average, achieve a 86, 76, 53, and 47 percent reduction in the stored data and a 73, 62, 47, and 38 percent shorter time required to take a checkpoint for PV, PVHVM, HVM Linux, and HVM Windows guests, respectively.

Live migration of virtual machines (VM) from one physical host to another is a key enabler for virtual desktop clouds (VDC). The prevalent algorithm, pre-copy, suffers from long migration times and a high data transfer volume for non-idle VMs which hinders effective use of live migration in VDC environments. In this paper, we present an optimization to the pre-copy method which is able to cut the total migration time in half. The key idea is to load memory pages duplicated on non-volatile storage directly and in parallel from the attached storage device. To keep the downtime short, outstanding data is fetched by a background process after the VM has been restarted on the target host. The proposed method has been implemented in the Xen hyper visor. A thorough performance analysis of the technique demonstrates that the proposed method significantly improves the performance of live migration: the total migration time is reduced up to 90% for certain benchmarks and by 50% on average at an equal or shorter downtime of the migrated VM with no or only minimal side-effects on co-located VMs.

Live migration of virtual machines (VM) across distinct physical hosts is an important feature of virtualization technology for maintenance, load-balancing and energy reduction, especially so for data centers operators and cluster service providers. Several techniques have been proposed to reduce the downtime of the VM being transferred, often at the expense of the total migration time. In this work, we present a technique to reduce the total time required to migrate a running VM from one host to another while keeping the downtime to a minimum. Based on the observation that modern operating systems use the better part of the physical memory to cache data from secondary storage, our technique tracks the VM's I/O operations to the network-attached storage device and maintains an updated mapping of memory pages that currently reside in identical form on the storage device. During the iterative pre-copy live migration process, instead of transferring those pages from the source to the target host, the memory-to-disk mapping is sent to the target host which then fetches the contents directly from the network-attached storage device. We have implemented our approach into the Xen hypervisor and ran a series of experiments with Linux HVM guests. On average, the presented technique shows a reduction of up over 30% on average of the total transfer time for a series of benchmarks.