We ask the question: can network problems experienced by applications be identified based on symptoms contained in a network packet trace? An answer in the affirmative would open the doors to many opportunities, including non-intrusive monitoring of such problems on the network and matching a problem with past instances of the same problem.

Cellular radio communication is a significant contributor to battery energy drain on smartphones, in some cases inflating the energy cost by a factor of 5 or more compared to the energy cost of the base device. Stratus is a system to reduce this energy consumption by leveraging cloud resources to make data communication on smartphones more efficient. Using a cloud-based proxy, Stratus employs optimizations that adapt an application's incoming and outgoing traffic to better match the energy characteristics of the radio interface. The optimizations include (a) aggregation to bunch up sporadic transmissions, (b) asymmetric dictionary-based compression to reduce the number of bits transmitted over the air, and (c) opportunistic scheduling to avoid communication during periods of poor signal reception. These optimizations can be used individually, or in combination, subject to an application's delay tolerance. For example, using our Stratus prototype, the aggregation and compression optimizations together achieve up to 50% energy savings for web browsing, while the aggregation and scheduling optimizations together achieve up to 35% energy savings for a media streaming application.

We present a tool, PerfCenter, that takes as input the deployment, configuration, message flow and workload details of the hardware and software servers in an application hosting center, and predicts the performance of the applications. We allow for a hierarchical specification of the data center, where software is deployed on machines, machines consist of hardware devices and are deployed on LANs. We also explicitly model network links between LANs and model the contention at those links due to messages exchanged between servers. While tools and methodologies for such analysis have been proposed earlier, our approach allows for the most natural specification of a "data center" architecture, and is best suited for aiding in design decisions regarding deployment and configuration of software on various hardware architecture scenarios. The tool takes this high level input and generates the underlying queueing network, which is then solved analytically. Since we allow for synchronous method calls, and model contention at software as well as hardware resources, the generated queueing network is solved using approximate methods. We validate the solution against results obtained from a measurement testbed, and found that the predicted values were reasonably accurate.