Answer retrieval is to find the most aligned answer from a large set of candidates given a question.Learning vector representations of questions/answers is the key factor.Questionanswer alignment and question/answer semantics are two important signals for learning the representations.Existing methods learned semantic representations with dual encoders or dual variational auto-encoders.The semantic information was learned from language models or question-to-question (answer-to-answer) generative processes.However, the alignment and semantics were too separate to capture the aligned semantics between question and answer.In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions.Experiments show that our method outperforms the state-of-theart answer retrieval method on SQuAD.Question Answer Decoder 𝑝(𝑞|𝒛 𝒂 ) 𝑝(𝑎|𝒛 𝒒 ) 𝑝(𝑦|𝑧 !, 𝑧 " ) 𝑝(𝑦|𝑧 !, 𝑧 " ) Question Answer Question Answer Decoder Encoder Encoder Decoder Decoder Encoder 𝑝(𝑧 !|𝑞) 𝑝(𝑧 " |𝑎) Encoder Encoder (a) Dual-Encoders (Yang et al., 2019)Question Answer Decoder 𝑝(𝑞|𝒛 𝒂 ) 𝑝(𝑎|𝒛 𝒒 ) 𝑝(𝑦|𝑧 !, 𝑧 " ) 𝑝(𝑦|𝑧 !, 𝑧 " ) Question Answer Question Answer Decoder Encoder Encoder Decoder Decoder Encoder 𝑝(𝑧 !|𝑞) 𝑝(𝑧 " |𝑎) Encoder Encoder (b) Dual-VAEs (Shen et al., 2018) 𝑧 !~𝑝(𝑧 ! ) 𝑧 " ~𝑝(𝑧 " ) Question Answer 𝑧 !~𝑝(𝑧 ! ) 𝑝(𝑧 !|𝑞) 𝑧 " ~𝑝(𝑧 " ) 𝑝(𝑧 " |𝑎) Question Answer 𝑝(𝑦|𝑧 !, 𝑧 " ) 𝑝(𝑞|𝑧 ! ) 𝑝(𝑎|𝑧 " )

On a Blockchain network, transaction data are exposed to all participants. To preserve privacy and confidentiality in transactions, while still maintaining data immutability, we design and implement FabZK. FabZK conceals transaction details on a shared ledger by storing only encrypted data from each transaction (e.g., payment amount), and by anonymizing the transactional relationship (e.g., payer and payee) between members in a Blockchain network. It achieves both privacy and auditability by supporting verifiable Pedersen commitments and constructing zero-knowledge proofs. FabZK is implemented as an extension to the open source Hyperledger Fabric. It provides APIs to easily enable data privacy in both client code and chaincode. It also supports on-demand, automated auditing based on encrypted data. Our evaluation shows that FabZK offers strong privacy-preserving capabilities, while delivering reasonable performance for the applications developed based on its framework.

While existing visual recognition approaches, which rely on 2D images to train their underlying models, work well for object classification, recognizing the changing state of a 3D object requires addressing several additional challenges. This paper proposes an active visual recognition approach to this problem, leveraging camera pose data available on mobile devices. With this approach, the state of a 3D object, which captures its appearance changes, can be recognized in real time. Our novel approach selects informative video frames filtered by 6-DOF camera poses to train a deep learning model to recognize object state. We validate our approach through a prototype for Augmented Reality-assisted hardware maintenance.

Cloud computing is emerging as a viable platform for scientific exploration. Elastic and on-demand access to resources (and other services), the abstraction of “unlimited” resources, and attractive pricing models provide incentives for scientists to move their workflows into clouds. Generalizing these concepts beyond a single virtualized datacenter, it is possible to create federated marketplaces where different types of resources (e.g., clouds, HPC grids, supercomputers) that may be geographically distributed, are collectively exposed as a single elastic infrastructure. This presents opportunities for optimizing the execution of application workflows with heterogeneous and dynamic requirements, and tackling larger scale problems. In this paper, we introduce a framework to manage the end-to-end execution of data-intensive application workflows in dynamic software-defined resource federation. This framework enables the autonomic execution of workflows by elastically provisioning an appropriate set of resources that meet application requirements, and by adapting this set of resources at runtime as the requirements change. It also allows users to customize scheduling policies that drive the way resources federated and used. To demonstrate the benefits of our approach, we study the execution of two different data-intensive scientific workflows in a multi-cloud federation using different policies and objective functions.

Sensitive data such as health records and financial transactions are increasingly being stored and processed in the cloud. Correspondingly, laws and regulations have been established to protect such data. For a cloud-based analytics service provider, it is of paramount importance to protect the sensitive information contained in customer data, while running analytics on it. While there exist a plethora of technologies to safeguard data, regulatory rules are not always defined in clear technical terms, and different regulations may impose different (or sometimes conflicting) rules on the analytics platform. Therefore, it remains a challenge in developing a platform that can support various security and compliance-enabling mechanisms, in a agile fashion, to reduce maintenance effort as well as improving scalability and performance. To address this challenge, we introduce the design and implementation of a cloud-based middleware platform that supports on-demand composition and configuration of security mechanisms to ease regulatory compliance enablement. We discuss at length our experiences and lessons learned from using our platform to deploy secure analytics systems at IBM and highlight the benefits of our approach by discussing the performance impact and trade-offs of different security mechanisms with respect to regulatory compliance.

Emerging container technologies, such as Docker, offer unprecedented agility in developing and running applications in cloud environment especially when combined with a microservice-style architecture. However, it is often difficult to use containers to manage the cloud infrastructure, without sacrificing many benefits container offers. This paper identifies the key challenges that impede realizing the full promise of containerizing infrastructure services. Using OpenStack as a case study, we explore solutions to these challenges. Specifically, we redesign OpenStack deployment architecture to enable dynamic service registration and discovery, explore different ways to manage service state in containers, and enable containers to access the host kernel and devices. We quantify the efficiency of the container-based microservice-style DevOps compared to the VM-based approach, and study the scalability of the stateless and stateful containerized components. We also discuss limitations in our current design, and highlight open research problems that, if solved, can lead to wider adoption of containers in cloud infrastructure management.

Problem diagnosis is one crucial aspect in the cloud operation that is becoming increasingly challenging. On the one hand, the volume of logs generated in today's cloud is overwhelmingly large. On the other hand, cloud architecture becomes more distributed and complex, which makes it more difficult to troubleshoot failures. In order to address these challenges, we have developed a tool, called LOGAN, that enables operators to quickly identify the log entries that potentially lead to the root cause of a problem. It constructs behavioral reference models from logs that represent the normal patterns. When problem occurs, our tool enables operators to inspect the divergence of current logs from the reference model and highlight logs likely to contain the hints to the root cause. To support these capabilities we have designed and developed several mechanisms. First, we developed log correlation algorithms using various IDs embedded in logs to help identify and isolate log entries that belong to the failed request. Second, we provide efficient log comparison to help understand the differences between different executions. Finally we designed mechanisms to highlight critical log entries that are likely to contain information pertaining to the root cause of the problem. We have implemented the proposed approach in a popular cloud management system, OpenStack, and through case studies, we demonstrate this tool can help operators perform problem diagnosis quickly and effectively.

Cloud computing is emerging as a viable platform for scientific exploration. The ideas of on-demand access to resources, "unlimited" resources as well as interesting pricing models are making scientist to move their workflows into cloud computing. However, the amount of services and different pricing models offered by the providers often overwhelm users when deciding which option is best for them. Moreover, interoperability across providers remains an open topic that forces users to develop specific solutions for each provider. In this paper, we present a service framework that enables the autonomic execution of dynamic workflows in multi-cloud environments. It also allows users to customize scheduling policies to use those resources that best fit their needs. To demonstrate the benefits of this framework, we study the execution of a real scientific workflow, with data dependencies across stages, in a multi-cloud federation using different policies and objective functions.

Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns.

Adoption of hybrid clouds by enterprises has been hampered by the inability of current hybrid cloud infrastructures to provide scalable and efficient mechanisms (1) to ensure the trustworthiness and integrity of the software stack executing a hybrid application workload, or (2) to enforce governmental privacy, data jurisdiction and audit regulations by ensuring that remote data and computation do not cross specified geographic boundaries.

This paper presents Latent Association Analysis (LAA), a generative model that analyzes the topics within two document sets simultaneously, as well as the correlations between the two topic structures, by considering the semantic associations among document pairs. LAA defines a correlation factor that represents the connection between two documents, and considers the topic proportion of paired documents based on this factor. Words in the documents are assumed to be randomly generated by particular topic assignments and topic-to-word probability distributions. The paper also presents a new ranking algorithm, based on LAA, that can be used to retrieve target documents that are potentially associated with a given source document. The ranking algorithm uses the latent factor in LAA to rank target documents by the strength of their semantic associations with the source document. We evaluate the LAA algorithm with real datasets, specifically, the IT-Change and the IT-Solution document sets from the IBM IT service environment and the Symptom-Treatment document sets from Google Health. Experimental results demonstrate that the LAA algorithm significantly outperforms existing algorithms.

Cloud computing promises high scalability, flexibility and cost-effectiveness to satisfy emerging computing requirements. To efficiently provision computing resources in the cloud, system administrators need the capabilities of characterizing and predicting workload on the Virtual Machines (VMs). In this paper, we use data traces obtained from a real data center to develop such capabilities. First, we search for repeatable workload patterns by exploring cross-VM workload correlations resulted from the dependencies among applications running on different VMs. Treating workload data samples as time series, we develop a co-clustering technique to identify groups of VMs that frequently exhibit correlated workload patterns, and also the time periods in which these VM groups are active. Then, we introduce a method based on Hidden Markov Modeling (HMM) to characterize the temporal correlations in the discovered VM clusters and to predict variations of workload patterns. The experimental results show that our method can not only help better understand group-level workload characteristics, but also make more accurate predictions on workload changes in a cloud.

Collaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers' problems. In such networks, information flow among members is driven by the tasks assigned to the network, and by the expertise of its members to complete those tasks. In this work, we analyze real-life collaborative networks to understand their common characteristics and how information is routed in these networks. Our study shows that collaborative networks exhibit significantly different properties compared with other complex networks. Collaborative networks have truncated power-law node degree distributions and other organizational constraints. Furthermore, the number of steps along which information is routed follows a truncated power-law distribution. Based on these observations, we developed a network model that can generate synthetic collaborative networks subject to certain structure constraints. Moreover, we developed a routing model that emulates task-driven information routing conducted by human beings in a collaborative network. Together, these two models can be used to study the efficiency of information routing for different types of collaborative networks -- a problem that is important in practice yet difficult to solve without the method proposed in this paper.

Complex social and information network search becomes important with a variety of applications. In the core of these applications, lies a common and critical problem: Given a labeled network and a query graph, how to efficiently search the query graph in the target network. The presence of noise and the incomplete knowledge about the structure and content of the target network make it unrealistic to find an exact match. Rather, it is more appealing to find the top-k approximate matches.

Problem resolution is a key issue in the IT service industry. A large service provider handles, on daily basis, thousands of tickets that report various types of problems from its customers. The efficiency of this process highly depends on the effective interactions among various expert groups, in search of the resolver to the reported problem. In fact, ticket transfer decisions reflect the expertise awareness between groups, thus encoding a sophisticated resolution social network. In this paper, we propose a computational framework to quantitatively assess expertise awareness, i.e., how well a group knows the expertise of others. An accurate assessment of expertise awareness could identify the weakest components in a resolution system. The framework, built on our previously developed resolution engine, is able to calculate the performance difference caused by excluding a node from the network. The difference exposes the awareness of this node to other nodes in the network. To our best knowledge, this is the first study on this problem from a computational perspective. We tested the proposed framework on a large set of real-world problem tickets and validated our discovery by carefully analyzing the tickets that are incorrectly transferred. Experimental results show that our framework can successfully capture groups that do not know others' expertise very well.

The Ms 8.0 Wenchuan earthquake, occurred on 12 May 2008 in Sichuan Province, caused enormous property damages and human casualties. In order to remove threat from the secondary landslides effectively, this study used techniques of remote sensing and GIS to generate susceptibility map, taking the case of Wenchuan County. The factors controlling landslides are obtained from geological maps and DEM, including relief amplitude, lithology, seismic intensity, distance to faults and rivers. Furthermore, this paper presents quantitative analysis of relationship between number and area of the landslides and factors in the study area. Based on this analysis, the probability predicting the possibility of landslide occurrence has been calculated applying information value method and logistic regression separately. The study zone was ultimately categorized into five classes, specifically, “extremely low”, “low”, “moderate”, “high” and “very high”. These results have been proved to reflect closely the spatial distributions of landslides in the study area.

Ticket resolution is a critical, yet challenging, aspect of the delivery of IT services. A large service provider needs to handle, on a daily basis, thousands of tickets that report various types of problems. Many of those tickets bounce among multiple expert groups before being transferred to the group with the right expertise to solve the problem. Finding a methodology that reduces such bouncing and hence shortens ticket resolution time is a long-standing challenge. In this paper, we present a unified generative model, the Optimized Network Model (ONM), that characterizes the lifecycle of a ticket, using both the content and the routing sequence of the ticket. ONM uses maximum likelihood estimation, to represent how the information contained in a ticket is used by human experts to make ticket routing decisions. Based on ONM, we develop a probabilistic algorithm to generate ticket routing recommendations for new tickets in a network of expert groups. Our algorithm calculates all possible routes to potential resolvers and makes globally optimal recommendations, in contrast to existing classification methods that make static and locally optimal recommendations. Experiments show that our method significantly outperforms existing solutions.

Strategic Outsourcing (SO) services allow clients to offload the operation of IT systems and processes to a specialized service provider, so they can focus on their core business functions. SO projects are multi-year engagements and are governed by a myriad of factors throughout their lifetime. The goal of service management is to ensure uninterrupted delivery of service from the provider to the client, while meeting a number of quality and performance goals. For the service provider, such objectives include continuous profitability of the contract, high execution quality and ultimately, high client satisfaction. The latter requires quality in delivery (as evidenced by a number of key performance indicators), controlled cost, and service responsiveness.

As IP telephony becomes a critical part of the enterprise network infrastructure, there is an increasing need for tools to help manage VoIP systems. In this paper, we introduce DA VINCI, a tool to manage VoIP call routing - the core of VoIP configurations that determines how calls are routed inside the enterprise or between the enterprise and external networks. We first establish the complexity involved in call routing design and configuration by studying characteristics of nine large enterprise VoIP networks. We then develop methodologies to improve and simplify call routing configurations by (i) identifying common design patterns, i.e., shared resource routing and local resource routing, so that administrators can better understand and reuse existing call routing designs, and (ii) removing unused and superfluous configurations from dial plan designs while retaining the correctness of operations. All of our techniques are incorporated in the DA VINCI tool that has been deployed in more than ten enterprise networks to help administrators streamline VoIP maintenance through extraction, analysis, simulation, and configuration of call routing.

Modern networks face the challenging task of handling increasingly diverse traffic that is displaying a growing intolerance to disruptions. This has given rise to many initiatives, and in this paper we focus on multiple topology routing as the primary vehicle for meeting those demands. Specifically, we seek routing solutions capable of not just accommodating different performance goals, but also preserving them in the presence of disruptions. The main challenge is computational, i.e., to identify among the enormous number of possible routing solutions the one that yields the best compromise between performance and robustness. This is where our principal contribution lies, as we expand the definition of critical links - a key concept in improving the efficiency of routing computation - and develop a precise methodology to efficiently converge on those solutions. Using this new methodology, we demonstrate that one can compute routing solutions that are both flexible in accommodating different performance requirements and robust in maintaining them in the presence of failures and traffic fluctuations.

Modern networks face the daunting task of handling increasingly diverse traffic that is displaying a growing intolerance to disruptions. This has given rise to many initiatives, and in this paper we focus on multiple topology routing as the primary vehicle for meeting those demands. Specifically, we seek routing solutions capable of not just accommodating different performance goals, but also preserving them in the presence of disruptions. The main challenge is computational, i.e., to identify among the enormous number of possible routing solutions the one that yields the best compromise between performance and robustness. This is where our principal contribution lies, as we expand the definition of critical links -- a key concept in improving the efficiency of routing computation -- and develop a precise methodology to efficiently converge on those solutions. Using this new methodology, we demonstrate that one can compute routing solutions that are both flexible in accommodating different performance requirements and robust in maintaining them in the presence of failures and traffic fluctuations.

IT problem management calls for quick identification of resolvers to reported problems. The efficiency of this process highly depends on ticket routing---transferring problem ticket among various expert groups in search of the right resolver to the ticket. To achieve efficient ticket routing, wise decision needs to be made at each step of ticket transfer to determine which expert group is likely to be, or to lead to the resolver.

Managing problem tickets is a key issue in IT service industry. A large service provider may handle thousands of problem tickets from its customers on a daily basis. The efficiency of processing these tickets highly depends on ticket routing---transferring problem tickets among expert groups in search of the right resolver to the ticket. Despite that many ticket management systems are available, ticket routing in these systems is still manually operated by support personnel. In this demo, we introduce EasyTicket, a ticket routing recommendation engine that helps automate this process. By mining ticket history data, we model an enterprise social network that represents the functional relationships among various expert groups in ticket routing. Based on this network, our system then provides routing recommendations to new tickets. Our experimental studies on 1.4 million real-world problem tickets show that on average, EasyTicket can improve the efficiency of ticket routing by 35%.

The convergence on IP of a wide variety of traffic types has strengthened the need for service differentiation. Service differentiation relies on two equally important components: (i) resource allocation, i.e., what resources does a given service class have access to; and (ii) contention resolution, i.e., how is access to shared resources arbitrated between services classes. The latter has been well studied with numerous mechanisms, e.g., scheduling and buffer management, supporting it in modern routers. In contrast, relatively few studies exist on the former, and in particular on the impact of routing that determines the resources a given service class is assigned to. This is the focus of the paper, which seeks to investigate how routing influences a network's ability to efficiently support different service classes. Of particular interest is the extent to which the ability to route service classes separately is beneficial. This question is explored for a base configuration involving two classes with either similar or entirely different service objectives (cost functions). The paper's contributions are in demonstrating and quantifying the benefits that the added flexibility of different (dual) routing affords, and in developing an efficient heuristic for computing jointly optimal routing solutions. The former can motivate the deployment of newly standardized multi-topology routing (MTR) functionality. The latter is a key enabler for the effective use of such capability.

Health monitoring, automated failure localization and diagnosis have all become critical to service providers of large distribution networks (e.g., digital cable and fiber-to-the-home), due to the increases in scale and complexity of their offered services. Existing automated failure diagnosis solutions typically assume complete knowledge of network topology, which in practice is rarely available. The solution presented in this paper - Network Management and Diagnosis (NetworkMD) - is an automated failure diagnosis system that can infer failure groups based on historical failure data, and optionally geographical information. The inferred failure groups mirror missing topologies, and can be used to localize failures, diagnose root causes of problems, and detect misconfiguration in known topologies. NetworkMD uses an unsupervised learning algorithm based on non-negative matrix factorization (NMF) to infer failure groups. Using cable network as the primary example, we demonstrate the effectiveness of NetworkMD in both simulated settings and real environment using data collected from a commercial network serving hundreds of thousands of customers via thousands of intermediate network devices.

Cable network has become one of the most popular ways of high-speed Internet access for homes and small businesses. For broadband cable providers, managing their large-scale network infrastructure is highly challenging because these networks are geographically dispersed and contain a large number of devices. A single administrative area typically serves hundreds of thousands of end-customers (or cable modems) with thousands of intermediate distribution devices that operate on different protocol layers. Such devices include routers, Cable Modem Termination Systems (CMTS's), fiber nodes, repeaters, etc. It is, thus, critical for the providers to monitor the health of their infrastructure and perform quick failure diagnosis. However, there are many challenges in failure diagnosis. In this paper, we focus on two challenges that are motivated by input from a large U.S. cable provider: missing device status information and incomplete topology.

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Large-scale peer-to-peer systems span a wide range of Internet locations. Such diversity can be leveraged to build overlay "detours" to circumvent periods of poor performance on the default path. However, identifying which peers are "good" relay choices in support of such detours is challenging, if one is to avoid incurring an overhead that grows with the size of the peer- to-peer system. This paper proposes and investigates the Earliest Branching Rule (EBR) to perform such a selection. EBR builds on the Earliest Diverging Rule (EDR) that selects relay nodes whose AS path diverges from the default path at the earliest possible point, but calls for monitoring a much smaller number of paths. As a result, it has a much lower overhead. The paper explores the performance and overhead of EBR, and compares them to that of EDR. The results demonstrate that EBR succeeds in selecting good relay nodes with minimum control overhead. Hence, providing a practical solution for dynamically building good overlays in large peer-to-peer systems.

The current best-effort Internet cannot readily provide the service guarantees that VoIP applications often require. Path switching can potentially address this problem without requiring new network mechanisms, simply by leveraging the robustness to performance variations available from connectivity options such as multi-homing and overlays. In this paper, we evaluate the effectiveness and benefits of path switching in improving the quality of VoIP applications, and demonstrate its feasibility through the design and implementation of a prototype gateway. We argue for an application-driven path switching system that accounts for both network path characteristics and application-specific factors (e.g., codec algorithms, playout buffering schemes). We also develop an application path quality estimator based on the ITU-T E-model for voice quality assessment, and an application-driven path switching algorithm that dynamically adapts the time scales over which path switching decisions are made to maximize voice quality. Through network emulation and experiments over a wide-area multi-homed test bed, we show that, with sufficient path diversity, path switching can yield meaningful improvements in voice quality. Hence by exploiting the inherent path diversity of the Internet, application-driven path switching is a viable option in providing quality-of-service to applications.

This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as the video codec and loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on many parameters that are difficult to estimate in real-time. As a result, we introduce a "relative quality" metric that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments.

This work explores the feasibility of improving the performance of end-to-end data transfers between different sites through path switching. Our study is focused on both the logic that controls path switching decisions and the configurations required to achieve sufficient path diversity. Specifically, we investigate two common approaches offering path diversity multi-homing and overlay networks - and investigate their characteristics in the context of a representative wide-area testbed. We explore the end-to-end delay and loss characteristics of different paths and find that substantial improvements can potentially be achieved by path switching, especially in lowering end-to-end losses. Based on this assessment, we develop a simple path-switching mechanism capable of realizing those performance improvements. Our experimental study demonstrates that substantial performance improvements are indeed achievable using this approach.

Estimating end-to-end packet loss on Internet paths is important not only to monitor network performance, but also to assist adaptive applications make the best possible use of available network resources. There has been significant prior work on measuring and modeling packet loss in the Internet, but most of those techniques do not focus on providing real-time information and on assessing path performance from an application standpoint. In this paper, we present an online probing-based approach to estimate the loss performance of a network path, and extend this estimate to infer the performance that an application using the path would see. The approach relies on a hidden Markov model constructed from performance estimates generated from probes, which is then used to predict path performance as an application would experience. The accuracy of the model is evaluated using a number of different metrics, including loss rate and loss burstiness. The sensitivity of the results to measurement and computational overhead is also investigated, and an extension of the base approach using a layered model is explored as a possible solution to capturing time-varying channel behavior while keeping computational complexity reasonably low. The results we present show that the approach is capable of generating accurate, real-time estimates of path performance, and of predicting the performance that applications would experience if routed on the path

The focus of this paper is on improving the quality of streaming video transmitted over the Internet. The approach we investigate assumes the availability of multiple paths between the source and the destination, and dynamically selects the best one. Although this is not a new concept, our contribution is in estimating the "goodness" of a path from the perspective of the video stream, instead of relying only on raw network performance measures. The paper starts by showing that the use of raw network performance data to control path switching decisions can often result in poor choices from an application perspective, and then proceeds to develop a practical approach for evaluating, in real-time, the performance of different paths in terms of video quality. Those estimates are used to continuously select the path that yields the best possible transmission conditions for video streaming applications. We demonstrate the feasibility and performance of the scheme through experiments involving different types of videos.

No abstract available.