Mobile sensors embedded in smart phones and smart buildings enable mobile sensing and users' behavior modeling and thus open the doors for edge-cutting applications such as personalized intelligent computing, activity prediction, health/wellbeing monitoring and behavior intervention. One critical obstacle in mobile sensing and behavior modeling is privacy. Users do not always trust the public cloud to store and process their detailed personal data. In this paper, we propose a novel approach of using hybrid cloud to distribute the computing among mobile devices, personal cloud and public cloud. Raw sensor data is stored within the personal cloud where users have full, physical control. User can authorize analytic widgets (e.g., health monitor or marketing survey) to only collect user-approved data. We demonstrate that with this approach, users' privacy anxiety is significantly reduced and the acceptance rate of the mobile sensing technology increases from 23% to 60%.

This keynote presentation will focus on the challenges and opportunities imposed on mobility and wireless communications technologies by the emerging and explosive needs of the future Internet of Things. As traditional (wireless) networking meets the "real world", with its highly mobile requirements, its noisy and lossy environments, its need to optimize communications across multiple, non-homogeneous wireless technologies, with often intermittent connectivity, its requirements for more deterministic exchanges, the urgent need of innovation and accelerated research into a number of relevant and exiting domains become apparent. We will describe the ICT Infrastructure developments needed to support these new requirements, and identify broad open mobility and wireless research topics, which may help catalyze innovation affecting our industry. In particular, among other topics, we will deal with the issue of IP mobility, and will discuss technologies responding to these requirements (LISP among them). We will then consider the need to support and manage non-homogeneous, multi-homed, highly lossy wireless connectivity, and will highlight the potential roles of Distributed Computing and of Network Coding in this domain. Finally, we will detail the deterministic networking requirements arising in a number of Internet of Things verticals, and highlight progress towards the definition and standardization of techniques enabling more predictable exchanges even in wireless networks.

Despite growing maturity in broadband mobile networks, wireless video streaming remains a challenging task, especially in highly dynamic environments. Rapidly changing wireless link qualities, highly variable round trip delays, and unpredictable traffic contention patterns often hamper the performance of conventional end-to-end rate adaptation techniques such as TCP-friendly rate control (TFRC). Furthermore, existing approaches tend to treat all flows leaving the network edge equally, without accounting for heterogeneity in the underlying wireless link qualities or the different rate utilities of the video streams. In this paper, we present a proxy-based solution for adapting the scalable video streams at the edge of a wireless network, which can respond quickly to highly dynamic wireless links. Our design adopts the recently standardized scalable video coding (SVC) technique for lightweight rate adaptation at the edge. Leveraging previously developed rate and quality models of scalable video with both temporal and amplitude scalability, we derive the rate-quality model that relates the maximum quality under a given rate by choosing the optimal frame rate and quantization stepsize. The proxy iteratively allocates rates of different video streams to maximize a weighted sum of video qualities associated with different streams, based on the periodically observed link throughputs and the sending buffer status. The temporal and amplitude layers included in each video are determined to optimize the quality while satisfying the rate assignment. Simulation studies show that our scheme consistently outperforms TFRC in terms of agility to track link qualities and overall subjective quality of all streams. In addition, the proposed scheme supports differential services for different streams, and competes fairly with TCP flows.

In this paper, we present a proxy-based solution for adapting the scalable video streams at the edge of a wireless network, which can respond quickly to highly dynamic wireless links. Our design adopts the scalable video coding (SVC) technique for lightweight rate adaptation at the edge. We derive a QoE model, i.e., rate-quality tradeoff model, that relates the maximum subjective quality under a given rate by choosing the optimal frame rate and quantization stepsize. The proxy iteratively allocates rates of different video streams to maximize a weighted sum of video qualities associated with different streams, based on the periodically observed link throughputs and the sending buffer status. Simulation studies show that our scheme consistently outperforms TFRC in terms of agility to track link qualities and overall quality of all streams. In addition, the proposed scheme supports differential services for different streams, and competes fairly with TCP flows.

As video traffic increases in the Internet and competes for limited bandwidth resources, it is important to design bandwidth-sharing and loss-protection schemes that account for video characteristics, beyond the traditional paradigm of fair-rate allocation among data flows. Ideally, such a scheme should handle both persistent and transient congestion as video streaming applications demand low-latency transmissions and low packet-loss ratios. This paper presents a novel scheme, layered Internet video adaptation (LIVA), in which network nodes feed back virtual congestion levels to video senders to assist both media-aware bandwidth sharing and transient-loss protection. The video senders respond to such feedback by adapting the rates of encoded scalable bitstreams based on their respective video rate-distortion (R-D) characteristics. The same feedback is employed to calculate the amount of forward error correction (FEC) protection for combating transient losses. Simulation studies show that LIVA can minimize the total distortion of all participating video streams and hence maximize their overall quality. At steady state, video streams experience no queueing delays or packet losses. In the face of transient congestion, the network-assisted adaptive FEC promptly protects video packets from losses. Our Linux-based demonstration showcases how LIVA can be implemented in a simple manner in real systems. We also present a solution for LIVA streams to coexist with TCP flows based on explicit congestion notification signaling. Finally, our theoretical analysis guarantees system stability for an arbitrary number of streams with round-trip delays below a prescribed limit.

This paper presents a novel scheme, Layered Internet Video Engineering (LIVE), in which network nodes feedback virtual congestion levels to video senders to assist both media-aware bandwidth sharing and transient loss protection. The video senders respond to such feedback by adapting the rates of encoded H.264/SVC streams based on their respective video rate-distortion (R-D) characteristics. The same feedback is employed to calculate the amount of forward error correction (FEC) protection for combating transient losses. Simulation studies show that LIVE can minimize the total distortion of all participating video streams and hence maximize their overall quality. At steady state, video streams experience no queuing delays or packet losses. In face of transient congestion, the network-assisted adaptive FEC effectively protect video packets from losses while keeping a minimum overhead. Our theoretical analysis further guarantees system stability for arbitrary number of streams with arbitrary round trip delays below a prescribed limit. Finally, we show that LIVE streams can coexist with TCP flows within the existing explicit congestion notification (ECN) framework.

We present a network-assisted scheme for media-aware bandwidth sharing among multiple video streaming sessions. Departing from the conventional paradigm of fair-rate allocation among data traffic flows, our scheme allocates the bottleneck bandwidth among the video streams according to their rate-distortion (R-D) characteristics, with the objective of minimizing the total video distortion of all streams. This demonstration shows that our scheme achieves the optimal rate allocation with fast convergence, efficient bottleneck utilization, and balanced video qualities among the competing streams.

Despite the importance of traffic classification in modern networks, the number of languages tailored to this task is extremely limited. These languages can be valuable, because they allow the update of an application (e.g. firewall) in terms of supported protocols by simply updating its protocol description database, instead of recompiling the application from scratch. This paper presents a set of extensions to the Network Protocol Description Language (NetPDL) allowing support of traffic classification from data-link to application-layer protocols. A set of preliminary experimental results obtained with these new extensions is presented as well.

Hash tables are a fundamental data structure in many network applications, including route lookups, packet classification and monitoring. Often a part of the data path, they need to operate at wire-speed. However, several associative memory accesses are needed to resolve collisions, making them slower than required. This motivates us to consider minimal perfect hashing schemes, which reduce the number of memory accesses to just 1 and are also space-efficient. Existing perfect hashing algorithms are not tailored for network applications because they take too long to construct and are hard to implement in hardware. This paper introduces a hardware-friendly scheme for minimal perfect hashing, with space requirement approaching 3.7 times the information theoretic lower bound. Our construction is several orders faster than existing perfect hashing schemes. Instead of using the traditional mapping-partitioning-searching methodology, our scheme employs a Bloom filter, which is known for its simplicity and speed. We extend our scheme to the dynamic setting, thus handling insertions and deletions

Many networking applications require fast state lookups in a concurrent state machine,which tracks the state of a large number of flows simultaneously.We consider the question of how to compactly represent such concurrent state machines. To achieve compactness,we consider data structures for Approximate Concurrent State Machines (ACSMs)that can return false positives,false negatives,or a "don 't know "response.We describe three techniques based on Bloom filters and hashing,and evaluate them using both theoretical analysis and simulation.Our analysis leads us to an extremely efficient hashing-based scheme with several parameters that can be chosen to trade off space,computation,and the pact of errors.Our hashing approach also yields a simple alternative structure with the same functionality as a counting Bloom filter that uses much less space.We show how ACSMs can be used for video congestion control.Using an ACSM,a router can implement sophisticated Active Queue Management (AQM)techniques for video traffic (without the need for standards changes to mark packets or change video formats),with a factor of four reduction in memory compared to full-state schemes and with very little error.We also show that ACSMs show promise for real-time detection of P2P traffic.

Ptacek and Newsham [14] showed how to evade signature detection at Intrusion Prevention Systems (IPS) using TCP and IP Fragmentation. These attacks are implemented in tools like FragRoute, and are institutionalized in IPS product tests. The classic defense is for the IPS to reassemble TCP and IP packets,and to consistently normalize the output stream. Current IPS standards require keeping state for 1 million connections. Both the state and processing requirements of reassembly and normalization are barriers to scalability for an IPS at speeds higher than 10 Gbps.In this paper, we suggest breaking with this paradigm using an approach we call Split-Detect. We focus on the simplest form of signature, an exact string match, and start by splitting the signature into pieces. By doing so the attacker is either forced to include at least one piece completely in a packet, or to display potentially abnormal behavior (e.g., several small TCP fragments or out-of-order packets) that cause the attacker's flow to be diverted to a slow path. We prove that under certain assumptions this scheme can detect all byte-string evasions. We also show using real traces that the processing and storage requirements of this scheme can be 10% of that required by a conventional IPS, allowing reasonable cost implementations at 20 Gbps. While the changes required by Split-Detect may be a barrier to adoption, this paper exposes the assumptions that must be changed to avoid normalization and reassembly in the fast path.

This paper presents a shaper architecture that scales to a large number of connections with diverse burstiness and bandwidth parameters. The architecture arbitrates fairly between connections with conforming cells by carefully integrating leaky-bucket traffic shaping with rate-based scheduling algorithms. Through a careful combination of per-connection queueing and approximate sorting, the shaper performs a small, bounded number of operations in response to each arrival and departure, independent of the number of connections and cells. To handle a wider range of rate parameters, a hierarchical arbitration scheme can reduce the implementation overheads and the interference between competing connections. Simulation experiments demonstrate that the architecture limits shaping delay and traffic distortions, even under heavy congestion.

Emerging broad-band switches must accommodate the diverse traffic parameters and quality-of-service requirements of voice, data, and video applications. End-to-end performance guarantees depend on connections complying with traffic contracts as their cells travel through the network. This paper presents a leaky-bucket shaper architecture that scales to a large number of connections with diverse burstiness and bandwidth parameters. In contrast to existing designs, the proposed architecture arbitrates fairly between connections with conforming cells by carefully integrating leaky-bucket traffic shaping with rate-based scheduling algorithms. Through a careful combination of per-connection queueing and approximate sorting, the shaper performs a small, bounded number of operations in response to each arrival and departure, independent of the number of connections and cells. When the shaper must handle a wide range of rate parameters, a hierarchical arbitration scheme can reduce the implementation overheads and further limit interference between competing connections. Through simulation experiments, we demonstrate that the architecture limits cell-shaping delay and traffic distortions, even in periods of heavy congestion. The efficient combination of traffic shaping and link scheduling results in an effective architecture for managing buffer and bandwidth resources in large, high-speed ATM switches.

In emerging communication networks (B-ISDN based on asynchronous transfer mode (ATM) technology), a single link may carry traffic for thousands of connections with different traffic parameters and quality-of-service requirements. High-speed links, coupled with small packet/cell sizes, require efficient switch architectures that can handle cell arrivals and departures every few microseconds, or faster. This paper presents a collection of self-clocked fair queueing (SCFQ) architectures amenable to efficient hardware implementation in network switches. Exact and approximate implementations of SCFQ efficiently handle a moderate range of connection bandwidth parameters, while hierarchical arbitration schemes scale to a large range of throughput requirements. Simulation experiments demonstrate that these architectures divide link bandwidth fairly on a small time scale, preserving connection bandwidth and burstiness properties.

This paper gives a class of flow control algorithms for the adaptive allocation of bandwidths to virtual connections (VC) in high-speed, wide-area ATM networks. The feedback rate to the source from the network is parsimonious, with each feedback bit indicating whether the buffer at a distant switch is above or below a threshold. The service discipline at the switch is first-come-first-served. The important goal of adaptability aims to make all of the network bandwidth available to the active VCs, even though the number of such VCs is variable over a given range. Each VC has two parameters, one giving its minimum guaranteed bandwidth and the other is the weight for determining its share of the uncommitted bandwidth. Judicious selection of these parameters defines distinctive services, such as best effort and best effort with minimum bandwidth. We derive design rules for selecting the parameters of the algorithms such that the appropriate guarantees and fairness properties are exhibited in the dynamical behavior. The systematic use of "damping" in right proportion with "gain" is shown to be a powerful device for stabilizing behavior and achieving fairness. Our analyses are based on a simple analytic fluid model composed of a system of first-order delay-differential equations, which reflect the propagation delay across the network. Extensive simulations examine the following: (1) fairness, especially to start-up VCs; (2) oscillations; (3) transient behavior, such as the rate of equalization from different initial conditions; (4) disparate bandwidth allocations; (5) multiple paths with diverse propagation delays; (6) adaptability and robustness with respect to parameters; and (7) interoperability of different algorithms.>

The authors consider an N*N asynchronous transfer mode (ATM) switching element with output queuing, under a uniformity assumption for the traffic arriving on the various input lines and for the output line address of the incoming cells. The traffic on each input line is modeled as a two-state Markov chain. The distribution of the busy and idle periods for each output line is analytically derived. It is concluded that it is not easy to identify a simple two-state Markov chain model that provides an adequate description for the output traffic. The implications of two different output line address assignment mechanisms for the incoming cells are quantitatively described. An alternative method is outlined for the computation of the steady-state distribution of the number of cells in the system. An analysis of the numerical results is presented.>

Two of the optimality criteria most commonly considered to compare job assignment policies, namely, the stochastic order and the average total completion time criterion, are discussed. It is shown that the latter, while being weaker than the former, still provides a useful criterion when policies are to be evaluated in terms of average performance measures such as the system average response time. Some of the most relevant results available in the literature on job assignment to parallel first come, first served (FCFS) servers are summarized. The assignment problem is discussed for a parallel system of processor sharing (PS) queues with exponential interarrival times and exponential and identical service requirements. The optimality of the join-the-shortcut-queue (JSQ) assignment policy for this case is proven. Feasible policies for general service time distribution are discussed. An approach allowing a meaningful comparison of servers in the perspective of job assignment is proposed. A comparison of the assignment problem for FCFS and PS parallel systems is presented.>

A model comprising several servers, each equipped with its own queue and with possibly different service speeds, is considered. Each server receives a dedicated arrival stream of jobs; there is also a stream of generic jobs that arrive to a job scheduler and can be individually allocated to any of the servers. It is shown that if the arrival streams are all Poisson and all jobs have the same exponentially distributed service requirements, the probabilistic splitting of the generic stream that minimizes the average job response time is such that it balances the server idle times in a weighted least-squares sense, where the weighting coefficients are related to the service speeds of the servers. The corresponding result holds for nonexponentially distributed service times if the service speeds are all equal. This result is used to develop adaptive quasi-static algorithms for allocating jobs in the generic arrival stream when the load parameters are unknown. The algorithms utilize server idle-time measurements which are sent periodically to the central job scheduler. A model is developed for these measurements, and the result mentioned is used to cast the problem into one of finding a projection of the root of an affine function, when only noisy values of the function can be observed.>

Simple binary symmetric one- and two-dimensional channel systems called Ising channels are investigated. The one-dimensional version is a certain binary finite-state channel with input intersymbol interference. Its zero-error capacity is determined together with lower and upper bounds to its Shannon capacity. For the two-dimensional channel system, a lower bound is obtained for the zero-error capacity, and an efficient zero-error coding scheme is developed.>

The authors formally state and discuss the response time minimization problem. They show the equivalence of this problem to a weighted least square load balancing problem. They also discuss the correct solution of these problems. Next they describe and analyze a class of server idle time measurements. Finally, a class of adaptive algorithms is presented, and their performance is studied via simulation experiments.>