Massive increases in production and consumption of digital media are motivating cloud "video encoding as a service." In this paper, we explore efficient platforms for supporting such services. Specifically, we explore transcoding performance on a software encoder and two hardware-accelerated encoders representing widely different points in the hardware acceleration design space, as well as interference effects when heterogenous encoders are run concurrently. Using results from our exploratory study, we next propose a framework for providing video encoding as a service on a single server, and implement a prototype of the framework in a system called Catalyst. Catalyst accepts transcoding tasks and intelligently uses all available resources, including software and hardware-accelerated encoders, to maximize throughput while respecting task deadlines, and provides a foundational building block for a cluster-based cloud video encoding service. We evaluate Catalyst using a synthetic transcoding workload designed to emulate an IP-TV/Cloud-DVR workload. Evaluation results show that Catalyst can significantly increase throughput while meeting task deadlines compared to naive use of hardware-accelerated encoding.

Data-driven application workflows that leverage compute capabilities and hosted services near the network edge can support latency-sensitive and critical applications in emerging areas such as Internet of Things (IoT) and smart infrastructure. However, distributed instantiation and execution of these workflows using resources across service providers and datacenters can be challenging. In this paper, we present the formulation of a decentralized workflow management approach for the autonomous instantiation and execution of dynamic data-driven workflows based on the opportunistic discovery and composition of services on-demand. Given a workflow template specification, this approach allows us to decouple workflow stages, allowing the execution of different stages to be performed by individual services, which are discovered and instantiated dynamically, and can be independently scaled as needed. These services may be geographically distributed and may be offered by different service providers using various QoS levels and cost models. The design, implementation and experimental evaluation of a decentralized workflow management framework using a live media stream application in a multi-cloud infrastructure is presented. Evaluations using a sample topology shows up to 2.5 times increase in QoS-meeting throughput when using our dynamic multi-cloud approach instead of using a fixed centralized cloud of identical capacity.

Recommender systems play a key role in personalizing service experiences by recommending relevant items to users. One popular technique for producing such personalization at scale is collaborative filtering via Matrix Factorization (MF). The essence of MF is to train a model by factorizing a sparse rating matrix consisting of users' ratings of item. Unfortunately, existing MF methods require model Learning from Scratch when new data (for users, items, or user ratings) arrive. Learning large models from scratch incurs significant computation cost and typically also results in stale recommendations. With increasing amounts of data and a need for real-time recommendations, incremental learning is desirable. In this paper, we develop a novel but simple method for incremental learning of MF models, called One-sided Least Squares, and demonstrate its parallel implementation via Apache Spark. We also describe how to integrate it with batch learning via Alternating Least Squares (ALS). Unlike previous incremental learning methods, we study our method's approximation of the results of ALS, while significantly reducing compute and storage costs. Our theoretical analysis and experimental results on three real-world datasets suggest that One-sided Least Squares achieves prediction accuracy close to Learning from Scratch with ALS at substantially faster learning speeds. This fast and accurate method for incremental learning enables improved Web-scale recommender systems.

As more and more reusable web services are published on the Internet, how to help users quickly identify appropriate candidate services has become an increasingly critical challenge. Most of the current research efforts on service discovery rely on syntax and semantics-based service matchmaking. In contrast, this paper presents a novel way of applying network routing mechanism to facilitate service discovery, featuring scalability and performance. Services annotated by Web Ontology Language for Services (OWL-S) are organized into a network based on semantic clustering. Virtual routers are created representing clusters, and Bloom Filters are generated for service routing. A service search request is thus transformed into a network routing problem to quickly locate semantic service cluster and in turn to candidate services. In addition, the deterministic annealing technique is applied to facilitate service classification in the network construction. Dynamic network adjustment is operated to ensure the search performance in the network. Empirical study over common testbed annotated in OWL-S is reported.

This paper describes a compiler and algorithms for simulation and fault diagnosis of computer hardware modeled in VHSIC Hardware Description Language(VHDL).Given a VHDL description, the compiler creates an internal representation.For simulation, a discrete-event based compiled code simulation algorithm is used.For fault diagnosis, a hierarchical approach using the stuck-at fault model at the first level and the arbitrary failure model at the second level, is used.The diagnosis algorithm reasons from first principles using constraint suspension.

This paper describes algorithms for fault diagnosis of computer hardware modeled in VHDL [1,2,3,4]. Given a VHDL description, the compiler creates an internal representation. For fault diagnosis, a hierarchical approach using the stuck-at fault model at the first level and the arbitrary failure model at the second level, is used. The diagnosis algorithm reasons from first principles using constraint suspension.