Mobile Edge Computing (MEC) has received much attention from the research community in recent years. A significant part of the published work has studied the telecom-centric MEC architecture, which assumes that the computing resource is located at the edge of the mobile access network (e.g., the Evolved Packet Core or ePC), typically at the first aggregation level. Many of these studies make a silent assumption that the latency at this stage of the network is negligible. We show not only that this assumption is false, but that in some common cases the latency of the first aggregation stage dominates the end-to-end latency. We further show that common probing metrics can easily fail when used to select the service site for an application and discuss possible improvements to server-selection reliability.

Virtual machine (VM) migration demands distinct properties under resource oversubscription and workload surges. We present enlightened post-copy, a new mechanism for VMs under contention that evicts the target VM with fast execution transfer and short total duration. This design contrasts with common live migration, which uses the down time of the migrated VM as its primary metric; it instead focuses on recovering the aggregate performance of the VMs being affected. In enlightened post-copy, the guest OS identifies memory state that is expected to encompass the VM's working set. The hypervisor accordingly transfers its state, mitigating the performance impact on the migrated VM resulting from post-copy transfer. We show that our implementation, with modest instrumentation in guest Linux, resolves VM contention up to several times faster than live migration.

Cloud-sourced virtual appliances (VAs) have been touted as powerful solutions for many software maintenance, mobility, backward compatibility, and security challenges. In this paper, we ask whether it is possible to create a VA cloud service that supports fluid, interactive user experience even over mobile networks. More specifically, we wish to support a YouTube-like streaming service for executable content, such as games, interactive books, research artifacts, etc. Users should be able to post, browse through, and interact with executable content swiftly and without long interruptions. Intuitively, this seems impossible; the bandwidths, latencies, and costs of last-mile networks would be prohibitive given the sheer sizes of virtual machines! Yet, we show that a set of carefully crafted, novel prefetching and streaming techniques can bring this goal surprisingly close to reality. We show that vTube, a VA streaming system that incorporates our techniques, supports fluid interaction even in challenging network conditions, such as 4G LTE.

Cloud offload is an important technique in mobile computing. VM-based cloudlets have been proposed as offload sites for the resource-intensive and latency-sensitive computations typically associated with mobile multimedia applications. Since cloud offload relies on precisely-configured back-end software, it is difficult to support at global scale across cloudlets in multiple domains. To address this problem, we describe just-in-time (JIT) provisioning of cloudlets under the control of an associated mobile device. Using a suite of five representative mobile applications, we demonstrate a prototype system that is capable of provisioning a cloudlet with a non-trivial VM image in 10 seconds. This speed is achieved through dynamic VM synthesis and a series of optimizations to aggressively reduce transfer costs and startup latency.

Metrics like disk activity and network traffic are widespread sources of diagnosis and monitoring information in datacenters and networks. However, as the scale of these systems increases, examining the raw data yields diminishing insight. We present RainMon, a novel end-to-end approach for mining timeseries monitoring data designed to handle its size and unique characteristics. Our system is able to (a) mine large, bursty, real-world monitoring data, (b) find significant trends and anomalies in the data, (c) compress the raw data effectively, and (d) estimate trends to make forecasts. Furthermore, RainMon integrates the full analysis process from data storage to the user interface to provide accessible long-term diagnosis. We apply RainMon to three real-world datasets from production systems and show its utility in discovering anomalous machines and time periods.

Idle resources can be exploited not only to run important local tasks such as data replication and virus checking, but also to make contributions to society by participating in open computing projects like SETI@home [2]. When executing background processes to utilize such valuable idle resources, we need to explicitly control them so that the user will not be discouraged from exploiting idle resources by foreground performance degradation. Unfortunately, common priority-based schedulers lack such explicit execution control. In addition, to encourage active use of idle resources, a mechanism for controlling background processes should not require modifications to the underlying operating system or user applications. If such modifications are required, the user may be reluctant to employ the mechanism. In this paper, we argue that we can reasonably detect resource contention between foreground and background processes and properly control background process execution at the user level. We infer the existence of resource contention from the approximated resource shares of background processes. Our approach takes advantage of dynamically instrumented probes, which are becoming increasingly popular, in estimating the resource shares. Also, it considers different resource types in combination and can handle varied workloads, including multiple background processes. We show that our system effectively avoids the performance degradation of foreground activities by suspending background processes in an appropriate fashion. Our system keeps the increase in foreground execution time due to background processes below 16.9%, or much lower in most of our experiments. Also, we extend our approach to address undesirable resource allocations to CPU-intensive processes that can occur in multiprocessor environments.