Datacenter networks should support high network utilization. Yet today's routing is typically load agnostic, so large flows can starve other flows if routed through overutilized links. Even recent proposals like centralized scheduling or end-host multi-pathing give suboptimal throughput, and they suffer from poor scalability and other limitations.

Recent popularity of smartphones drives rapid growth in the demand for cellular network bandwidth. Unfortunately, due to the centralized architecture of cellular networks, increasing the physical backhaul bandwidth is challenging. While content caching in the cellular network could be beneficial, relatively few characteristics of the cellular traffic is known to come up with a highly-effetive caching strategy. In this work, we provide insight into flow and content-level characteristics of modern 3G traffic at a large cellular ISP in South Korea. We first develop a scalable deep flow inspection (DFI) system that can manage hundreds of thousands of concurrent TCP flows on a commodity multicore server. Our DFI system collects various HTTP/TCP-level statistics and produces logs for analyzing the effectiveness of conventional Web caching, prefix-based Web caching, and TCP-level redundancy elimination (RE) without a single packet drop at a 10~Gbps link. Our week-long measurements of over 370 TBs of the 3G traffic reveal that standard Web caching can reduce download bandwidth consumption up to 27.1% while simple TCP-level RE can save the bandwidth consumption up to 42.0% with a cache of 512~GB of RAM. We also find that applying TCP-level RE on the largest 9.4% flows eliminates 68.4% of the total redundancy. Most of the redundancy (52.1%~58.9%) comes from serving the same HTTP objects while the contribution by aliased URLs is up to 38.9%.

Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to partition applications automatically at a fine granularity while optimizing execution time and energy use for a target computation and communication environment. At runtime, the application partitioning is effected by migrating a thread from the mobile device at a chosen point to the clone in the cloud, executing there for the remainder of the partition, and re-integrating the migrated thread back to the mobile device. Our evaluation shows that CloneCloud can adapt application partitioning to different environments, and can help some applications achieve as much as a 20x execution speed-up and a 20-fold decrease of energy spent on the mobile device.

As Web sites move from relatively static displays of simple pages to rich media applications with heavy client-side interaction, the nature of the resulting Web traffic changes as well. Understanding this change is necessary in order to improve response time, evaluate caching effectiveness, and design intermediary systems, such as firewalls, security analyzers, and reporting/management systems. Unfortunately, we have little understanding of the underlying nature of today's Web traffic.

As the nature of Web traffic evolves over time, we must update our understanding of underlying nature of today's Web, which is necessary to improve response time, understand caching effectiveness, and to design intermediary systems, such as firewalls, security analyzers, and reporting or management systems. In this paper, we analyze five years (2006-2010) of real Web traffic from a globally-distributed proxy system, which captures the browsing behavior of over 70,000 daily users from 187 countries. Using this data set, we examine major changes in Web traffic characteristics during this period, and also investigate the redundancy of this traffic, using both traditional object-level caching as well as content-based approaches.

This paper proposes MISSA, a novel middleware to facilitate the development and provision of stream-based services in emerging pervasive environments. The stream-based services utilize voluminous and continuously updated data streams as their input. The characteristics of data streams bring new requirements on the development and provision of the services. To satisfy the requirements, a unique service model and a runtime system are designed in MISSA. The key concept of our service model is to separate service logic from handling data streams. This significantly mitigates the burden on service developers by allowing them to only concentrate on the service logic. Job of handling data streams is completely delegated to the runtime. In this paper, we first present the importance of stream-based services and their requirements. Also, we describe a best route finding service as an example to motivate the need for MISSA. Then, we envision overall architecture for provisioning of stream-based services and detail our design of service model and runtime. Lastly, we demonstrate the efficiency of service model and runtime through experiments.