Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

Building Distributed Systems with Non-Volatile Main Memories and RDMA Networks

High-performance, byte-addressable non-volatile main memories (NVMMs) allow application developers to combine storage and memory into a single layer. These high-performance storage systems would be especially useful in large-scale data center environments where data is distributed and replicated across multiple servers.Unfortunately, existing approaches of providing remote storage access rest on the assumption that storage is slow, so the cost of the software and protocols is acceptable. Such assumption no longer holds for the fast NVMM. As a result, taking full advantage of NVMMs’ potential will require changes in system software and networking protocol. This thesis focuses on accessing remote NVMM efficiently using remote direct memory access (RDMA) network. RDMA enables a client to directly access memory on a remote machine without involving its local CPU.This thesis first presents Mojim, a system that provides replicated, reliable, and highly-available NVMM as an operating system service. Applications can access data in Mojim using normal load and store instructions while controlling when and how updates propagate to replicas using system calls. Our evaluation shows Mojim adds little overhead to the un-replicated system and provides 0.4x to 2.7x the throughput of the un-replicated system.This thesis then presents Orion, a distributed file system designed from for NVMM and RDMA networks. Traditional distributed file systems are designed for slower hard drives. These slower media incentivizes complex optimizations (e.g., queuing, striping, and batching) around disk accesses. Orion combines file system functions and network operations into a single layer. It provides low latency metadata accesses and outperforms existing distributed file systems by a large margin.Finally, an NVMM application can map files backed by an NVMM file system into its address space, and accesses them using CPU instructions. In this case, RDMA and NVMM file systems introduce duplication of effort on permissions, naming, and address translation. We introduce two changes to the existing RDMA protocol: the file memory region (FileMR) and range based address translation. By eliminating redundant translations, FileMR minimizes the number of translations done at the NIC, reducing the load on the NIC’s translation cache and resulting in application performance improvement by 1.8x - 2.0x

GPUs as Storage System Accelerators

Massively multicore processors, such as Graphics Processing Units (GPUs), provide, at a comparable price, a one order of magnitude higher peak performance than traditional CPUs. This drop in the cost of computation, as any order-of-magnitude drop in the cost per unit of performance for a class of system components, triggers the opportunity to redesign systems and to explore new ways to engineer them to recalibrate the cost-to-performance relation. This project explores the feasibility of harnessing GPUs' computational power to improve the performance, reliability, or security of distributed storage systems. In this context, we present the design of a storage system prototype that uses GPU offloading to accelerate a number of computationally intensive primitives based on hashing, and introduce techniques to efficiently leverage the processing power of GPUs. We evaluate the performance of this prototype under two configurations: as a content addressable storage system that facilitates online similarity detection between successive versions of the same file and as a traditional system that uses hashing to preserve data integrity. Further, we evaluate the impact of offloading to the GPU on competing applications' performance. Our results show that this technique can bring tangible performance gains without negatively impacting the performance of concurrently running applications.Comment: IEEE Transactions on Parallel and Distributed Systems, 201

funcX: A Federated Function Serving Fabric for Science

Exploding data volumes and velocities, new computational methods and platforms, and ubiquitous connectivity demand new approaches to computation in the sciences. These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e.g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available. They also require new design approaches in which monolithic applications can be decomposed into smaller components, that may in turn be executed separately and on the most suitable resources. To address these needs we present funcX---a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. funcX's endpoint software can transform existing clouds, clusters, and supercomputers into function serving systems, while funcX's cloud-hosted service provides transparent, secure, and reliable function execution across a federated ecosystem of endpoints. We motivate the need for funcX with several scientific case studies, present our prototype design and implementation, show optimizations that deliver throughput in excess of 1 million functions per second, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap with arXiv:1908.0490

Resurrection: Rethinking Magnetic Tapes For Cost Efficient Data Preservation

With the advent of Big Data technologies-the capacity to store and efficiently process large sets of data, doors of opportunities for developing business intelligence that was previously unknown, has opened. Each phase in the processing of this data requires specialized infrastructures. One such phase, the preservation and archiving of data, has proven its usefulness time and again. Data archives are processed using novel data mining methods to elicit vital data gathered over long periods of time and efficiently audit the growth of a business or an organization. Data preservation is also an important aspect of business processes which helps in avoiding loss of important information due to system failures, human errors and natural calamities. This thesis investigates the need, discusses possibilities and presents a novel, highly cost-effective, unified, long- term storage solution for data. Some of the common processes followed in large-scale data warehousing systems are analyzed for overlooked, inordinate shortcomings and a profitably feasible solution is conceived for them. The gap between the general needs of 'efficient' long-term storage and common, current functionalities is analyzed. An attempt to bridge this gap is made through the use of a hybrid, hierarchical media based, performance enhancing middleware and a monolithic namespace filesystem in a new storage architecture, Tape Cloud. The scope of studies carried out by us involves interpreting the effects of using heterogeneous storage media in terms of operational behavior, average latency of data transactions and power consumption. The results show the advantages of the new storage system by demonstrating the difference in operating costs, personnel costs and total cost of ownership from varied perspectives in a business model.Computer Science, Department o

Flexible allocation and space management in storage systems

In this dissertation, we examine some of the challenges faced by the emerging networked storage systems. We focus on two main issues. Current file systems allocate storage statically at the time of their creation. This results in many suboptimal scenarios, for example: (a) space on the disk is not allocated well across multiple file systems, (b) data is not organized well for typical access patterns. We propose Virtual Allocation for flexible storage allocation. Virtual allocation separates storage allocation from the file system. It employs an allocate-on-write strategy, which lets applications fit into the actual usage of storage space without regard to the configured file system size. This improves flexibility by allowing storage space to be shared across different file systems. We present the design of virtual allocation and an evaluation of it through benchmarks based on a prototype system on Linux. Next, based on virtual allocation, we consider the problem of balancing locality and load in networked storage systems with multiple storage devices (or bricks). Data distribution affects locality and load balance across the devices in a networked storage system. We propose user-optimal data migration scheme which tries to balance locality and load balance in such networked storage systems. The presented approach automatically and transparently manages migration of data blocks among disks as data access patterns and loads change over time. We built a prototype system on Linux and present the design of user-optimal migration and an evaluation of it through realistic experiments

Configurable data center switch architectures

In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces