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Preaxial dominance in the mesopodium is limited to distal carpals/tarsals and facilitates digit reduction in early tetrapods. 

The Chameleon project developed a unique experimental testbed by adapting a mainstream cloud implementation to the needs of systems research community and thereby demonstrated that clouds can be configured to serve as a platform for this type research. More recently, the CloudBank project embarked on a mission of providing a conduit to commercial clouds for the systems research community that eliminates much of the complexity and some of the cost of using them for research. This creates an opportunity to explore running systems experiments in a combined setting, spanning both research and commercial clouds. In this paper, we present an extension to Chameleon for constructing controlled experiments across its resources and commercial clouds accessible via CloudBank, present a case study of an experiment running across such combined resources, and discuss the impact of using a combined research platform. 

Infrastructure cloud computing allows its clients to allocate on-demand resources, typically consisting of a repre- sentation of a compute node. In general however, there is a need for allocating resources other than nodes and managing them in more controlled ways than simply on demand. This paper generalizes the familiar “compute power on demand” pattern by introducing the abstraction of an allocatable resource, describing its properties, and implementation for different types of resources. We further describe architecture for a generic allocatable resource management service that can be extended to manage diverse types of resources as well as the implementation of this architecture in the OpenStack Blazar service to manage resources ranging from bare-metal compute nodes to network segments. Finally, we provide a usage analysis of this service on the Chameleon testbed and use it to illustrate the effectiveness of resource management methods as well as the need for incentives in usage arbitration. 

Although Software-Defined Wide Area Networks (SD-WANs) are now widely deployed in several production networks, they are largely restricted to traffic engineering ap- proaches based on layer 4 (L4) of the network protocol stack. Such approaches result in improved Quality-of-Service (QoS) of the network overall without necessarily focussing on the requirements of a specific application. However, the emergence of application protocols such as QUIC and HTTP/2 needs an in- vestigation of layer 5-based (L5) approaches in order to improve users’ Quality-of-Experience (QoE). In this paper, we leverage the capabilities of flexible, P4-based switches that incorporate protocol-independent packet processing in order to intelligently route traffic based on application headers. We use Adaptive Bit Rate (ABR) video streaming as an example to show how such an approach can not only provide flexible traffic management but also improve application QoE. Our evaluation consists of an actual deployment in a research testbed, Chameleon, where we leverage the benefits of fast paths in order to retransmit video segments in higher qualities. Further, we analyze real-world ABR streaming sessions from a large-scale CDN and show that our approach can successfully maximize QoE for all users in the dataset. 

Chameleon is a large-scale, deeply reconfigurable testbed built to support Computer Science experimentation. Unlike traditional systems of this kind, Chameleon has been configured using an adaptation of a mainstream open source infrastructure cloud system called OpenStack. We show that operating cloud systems requires both more skill and extra effort on the part of the operators - in particular where those systems are expected to evolve quickly - which can make systems of this kind expensive to run. We discuss three ways in which those operations costs can be managed: innovative mon- itoring and automation of systems tasks, building “operator co-ops”, and collaborating with users. 

In this paper, we study the impacts of latency variation versus latency mean on application runtime, library performance, and packet delivery. Our contributions include the design and implementation of a network latency injector that is suitable for most QLogic and Mellanox InfiniBand cards. We fit statistical distributions of latency mean and variation to varying levels of network contention for a range of parallel application workloads. We use the statistical distributions to characterize the latency variation impacts to application degradation. The level of application degradation caused by variation in network latency depends on application characteristics, and can be significant. Observed degradation varies from no degradation for applications without communicating processes to 3.5 times slower for communication-intensive parallel applications. We support our results with statistical analysis of our experimental observations. For communication-intensive high performance computing applications, we show statistically significant evidence that changes in performance are more highly correlated with changes of variation in network latency than with changes of mean network latency alone. 

The industry standard Packet CAPture (PCAP) format for storing network packet traces is normally only readable in serial due to its lack of delimiters, indexing, or blocking. This presents a challenge for parallel analysis of large networks, where packet traces can be many gigabytes in size. In this work we present RAPCAP, a novel method for random access into variable-length record collections like PCAP by identifying a record boundary within a small number of bytes of the access point. Unlike related heuristic methods that can limit scalability with a nonzero probability of error, the new method offers a correctness guarantee with a well formed file and does not rely on prior knowledge of the contents. We include a practical implementation of the algorithm with an extension to the Hadoop framework, and a performance comparison to serial ingestion. Finally, we present a number of similar storage types that could utilize a modified version of RAPCAP for random access. 

The industry standard Packet CAPture (PCAP) format for storing network packet traces is normally only readable in serial due to its lack of delimiters, indexing, or blocking. This presents a challenge for parallel analysis of large networks, where packet traces can be many gigabytes in size. In this work we present RAPCAP, a novel method for random access into variable-length record collections like PCAP by identifying a record boundary within a small number of bytes of the access point. Unlike related heuristic methods that can limit scalability with a nonzero probability of error, the new method offers a correctness guarantee with a well formed file and does not rely on prior knowledge of the contents. We include a practical implementation of the algorithm with an extension to the Hadoop framework, and a performance comparison to serial ingestion. Finally, we present a number of similar storage types that could utilize a modified version of RAPCAP for random access.