Bizur is a consensus algorithm exposing a key-value interface. It is used by a distributed file-system that scales to 100s of servers, delivering millions of IOPS, both data and metadata, with consistent low-latency. Bizur is aimed for services that require strongly consistent state, but do not require a distributed log; for example, a distributed lock manager or a distributed service locator. By avoiding a distributed log scheme, Bizur outperforms distributed log based consensus algorithms, producing more IOPS and guaranteeing lower latencies during normal operation and especially during failures. Paxos-like algorithms (e.g., Zab and Raft) which are used by existing distributed file-systems, can have artificial contention points due to their dependence on a distributed log. The distributed log is needed when replicating a general service, but when the desired service is key-value based, the contention points created by the distributed log can be avoided. Bizur does exactly that, by reaching consensus independently on independent keys. This independence allows Bizur to handle failures more efficiently and to scale much better than other consensus algorithms, allowing the file-system that utilizes Bizur to scale with it.
DO-RAMBO @cite_37 handles multiple objects. However, it still doesn't support , and it assumes a fixed namespace of the multiple objects it handles. Thus, a general key-value cannot be used directly, since each key must be known as part of the fixed namespace.
@cite_37
2112903584
We deduce key design features behind the YouTube video delivery system by building a distributed active measurement infrastructure, and collecting and analyzing a large volume of video playback logs, DNS mappings and latency data. We find that the design of YouTube video delivery system consists of three major components: a “flat” video id space, multiple DNS namespaces reflecting a multi-layered logical organization of video servers, and a 3-tier physical cache hierarchy. We also uncover that YouTube employs a set of sophisticated mechanisms to handle video delivery dynamics such as cache misses and load sharing among its distributed cache locations and data centers. No secure network file system has ever grown to span the Internet. Existing systems all lack adequate key management for security at a global scale. Given the diversity of the Internet, any particular mechanism a file system employs to manage keys will fail to support many types of use.We propose separating key management from file system security, letting the world share a single global file system no matter how individuals manage keys. We present SFS, a secure file system that avoids internal key management. While other file systems need key management to map file names to encryption keys, SFS file names effectively contain public keys, making them self-certifying pathnames. Key management in SFS occurs outside of the file system, in whatever procedure users choose to generate file names.Self-certifying pathnames free SFS clients from any notion of administrative realm, making inter-realm file sharing trivial. They let users authenticate servers through a number of different techniques. The file namespace doubles as a key certification namespace, so that people can realize many key management schemes using only standard file utilities. Finally, with self-certifying pathnames, people can bootstrap one key management mechanism using another. These properties make SFS more versatile than any file system with built-in key management. Part-based models have demonstrated their merit in object detection. However, there is a key issue to be solved on how to integrate the inaccurate scores of part detectors when there are occlusions or large deformations. To handle the imperfectness of part detectors, this paper presents a probabilistic pedestrian detection framework. In this framework, a deformable part-based model is used to obtain the scores of part detectors and the visibilities of parts are modeled as hidden variables. Unlike previous occlusion handling approaches that assume independence among visibility probabilities of parts or manually define rules for the visibility relationship, a discriminative deep model is used in this paper for learning the visibility relationship among overlapping parts at multiple layers. Experimental results on three public datasets (Caltech, ETH and Daimler) and a new CUHK occlusion dataset1 specially designed for the evaluation of occlusion handling approaches show the effectiveness of the proposed approach. PaaS vendors face challenges in efficiently providing services with the growth of their offerings. In this paper, we explore how PaaS vendors are using containers as a means of hosting Apps. The paper starts with a discussion of PaaS Use case and the current adoption of Container based PaaS architectures with the existing vendors. We explore various container implementations - Linux Containers, Docker, Warden Container, lmctfy and OpenVZ. We look at how each of this implementation handle Process, FileSystem and Namespace isolation. We look at some of the unique features of each container and how some of them reuse base Linux Container implementation or differ from it. We also explore how IaaSlayer itself has started providing support for container lifecycle management along with Virtual Machines. In the end, we look at factors affecting container implementation choices and some of the features missing from the existing implementations for the next generation PaaS