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Docker containers have become a prominent solution for supporting modern enterprise applications due to the highly desirable features of isolation, low overhead, and efficient packaging of the execution environment. Containers are created from images which are shared between users via a Docker registry. The amount of data Docker registries store is massive; for example, Docker Hub, a popular public registry, stores at least half a million public images. In this paper, we analyze over 167 TB of uncompressed Docker Hub images, characterize them using multiple metrics and evaluate the potential of file-level deduplication in Docker Hub. Our analysis helps to make conscious decisions when designing storage for containers in general and Docker registries in particular. For example, only 3% of the files in images are unique, which means file-level deduplication has a great potential to save storage space for the registry. Our findings can motivate and help improve the design of data reduction, caching, and pulling optimizations for registries.

Due to their tight isolation, low overhead, and efficient packaging of the execution environment, Docker containers have become a prominent solution for deploying modern applications. Containers are created from images which are stored in a Docker registry. An image consists of a list of layers which can be shared among images. Docker registries store a large amount of images and with the increasing popularity of Docker, they continue to grow. For example, Docker Hub-a popular public registry-stores more than half a million public images. In this paper, we analyze over 167TB of uncompressed Docker images and evaluate the potential of file-level deduplication in the registry. Our analysis reveals that only 3% of the files in images are unique and Docker's existing layer sharing mechanism is not sufficient to eliminate this profound redundancy. We then present the design of Slimmer-a Docker registry with file deduplication support-and conduct a simulation-based analysis of its performance implications.

Docker container images are typically stored in a centralized registry to allow easy sharing of images. However, with the growing popularity of containerized software, the number of images that a registry needs to store and the rate of requests it needs to serve are increasing rapidly. Current registry design requires hosting registry services across multiple loosely connected servers with different roles such as load balancers, proxies, registry servers, and object storage servers. Due to the various individual components, registries are hard to scale and benefits from optimizations such as caching are limited. In this paper we propose, implement, and evaluate BOLT-a new hyperconverged design for container registries. In BOLT, all registry servers are part of a tightly connected cluster and play the same consolidated role: each registry server caches images in its memory, stores images in its local storage, and provides computational resources to process client requests. The design employs a custom consistent hashing function to take advantage of the layered structure and addressing of images and to load balance requests across different servers. Our evaluation using real production workloads shows that BOLT outperforms the conventional registry design significantly and improves latency by an order of magnitude and throughput by up to 5x. Compared to state-of-the-art, BOLT can utilize cache space more efficiently and serve up to 35% more requests from its cache. Furthermore, BOLT scales linearly and recovers from failure recovery without significant performance degradation.

The memory means an existence of output (response, endogenous variable) at the present time that depends on the history of the change of the input (impact, exogenous variable) on a finite (or infinite) time interval. The memory can be described by the function that is called the memory function, which is a kernel of the integro-differential operator. The main purpose of the paper is to answer the question of the possibility of using the fractional calculus, when the memory function does not have a power-law form. Using the generalized Taylor series in the Trujillo-Rivero-Bonilla (TRB) form for the memory function, we represent the integro-differential equations with memory functions by fractional integral and differential equations with derivatives and integrals of non-integer orders. This allows us to describe general economic dynamics with memory by the methods of fractional calculus. We prove that equation of the generalized accelerator with the TRB memory function can be represented by as a composition of actions of the accelerator with simplest power-law memory and the multi-parametric power-law multiplier. As an example of application of the suggested approach, we consider a generalization of the Harrod-Domar growth model with continuous time.

Containers are a widely successful technology today popularized by Docker. Containers improve system utilization by increasing workload density. Docker containers enable seamless deployment of workloads across development, test, and production environments. Docker's unique approach to data management, which involves frequent snapshot creation and removal, presents a new set of exciting challenges for storage systems. At the same time, storage management for Docker containers has remained largely unexplored with a dizzying array of solution choices and configuration options. In this paper we unravel the multi-faceted nature of Docker storage and demonstrate its impact on system and workload performance. As we uncover new properties of the popular Docker storage drivers, this is a sobering reminder that widespread use of new technologies can often precede their careful evaluation.

Deduplication has become essential in disk-based backup systems, but there have been few long-term studies of backup workloads. Most past studies either were of a small static snapshot or covered only a short period that was not representative of how a backup system evolves over time. For this paper, we collected 21 months of data from a shared user file system; 33 users and over 4,000 snapshots are covered. We analyzed the data set for a variety of essential characteristics. However, our primary focus was individual user data. Despite apparently similar roles and behavior in all of our users, we found significant differences in their deduplication ratios. Moreover, the data that some users share with others had a much higher deduplication ratio than average. We analyze this behavior and make recommendations for future deduplication systems design.