8,589 research outputs found
CRAID: Online RAID upgrades using dynamic hot data reorganization
Current algorithms used to upgrade RAID arrays typically require large amounts of data to be migrated, even those that move only the minimum amount of data required to keep a balanced data load. This paper presents CRAID, a self-optimizing RAID array that performs an online block reorganization of frequently used, long-term accessed data in order to reduce this migration even further. To achieve this objective, CRAID tracks frequently used, long-term data blocks and copies them to a dedicated partition spread across all the disks in the array. When new disks are added, CRAID only needs to extend this process to the new devices to redistribute this partition, thus greatly reducing the overhead of the upgrade process. In addition, the reorganized access patterns within this partition improve the array’s performance, amortizing the copy overhead and allowing CRAID to offer a performance competitive with traditional RAIDs.
We describe CRAID’s motivation and design and we evaluate it by replaying seven real-world workloads including a file server, a web server and a user share. Our experiments show that CRAID can successfully detect hot data variations and begin using new disks as soon as they are added to the array. Also, the usage of a dedicated
partition improves the sequentiality of relevant data access, which amortizes the cost of reorganizations. Finally, we prove that a full-HDD CRAID array with a small distributed partition (<1.28% per disk) can compete in performance with an ideally restriped RAID-5 and a hybrid RAID-5 with a small SSD cache.Peer ReviewedPostprint (published version
NOMA Assisted Wireless Caching: Strategies and Performance Analysis
Conventional wireless caching assumes that content can be pushed to local
caching infrastructure during off-peak hours in an error-free manner; however,
this assumption is not applicable if local caches need to be frequently updated
via wireless transmission. This paper investigates a new approach to wireless
caching for the case when cache content has to be updated during on-peak hours.
Two non-orthogonal multiple access (NOMA) assisted caching strategies are
developed, namely the push-then-deliver strategy and the push-and-deliver
strategy. In the push-then-deliver strategy, the NOMA principle is applied to
push more content files to the content servers during a short time interval
reserved for content pushing in on-peak hours and to provide more connectivity
for content delivery, compared to the conventional orthogonal multiple access
(OMA) strategy. The push-and-deliver strategy is motivated by the fact that
some users' requests cannot be accommodated locally and the base station has to
serve them directly. These events during the content delivery phase are
exploited as opportunities for content pushing, which further facilitates the
frequent update of the files cached at the content servers. It is also shown
that this strategy can be straightforwardly extended to device-to-device
caching, and various analytical results are developed to illustrate the
superiority of the proposed caching strategies compared to OMA based schemes
Online Coded Caching
We consider a basic content distribution scenario consisting of a single
origin server connected through a shared bottleneck link to a number of users
each equipped with a cache of finite memory. The users issue a sequence of
content requests from a set of popular files, and the goal is to operate the
caches as well as the server such that these requests are satisfied with the
minimum number of bits sent over the shared link. Assuming a basic Markov model
for renewing the set of popular files, we characterize approximately the
optimal long-term average rate of the shared link. We further prove that the
optimal online scheme has approximately the same performance as the optimal
offline scheme, in which the cache contents can be updated based on the entire
set of popular files before each new request. To support these theoretical
results, we propose an online coded caching scheme termed coded least-recently
sent (LRS) and simulate it for a demand time series derived from the dataset
made available by Netflix for the Netflix Prize. For this time series, we show
that the proposed coded LRS algorithm significantly outperforms the popular
least-recently used (LRU) caching algorithm.Comment: 15 page
Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks
Cooperative video caching and transcoding in mobile edge computing (MEC)
networks is a new paradigm for future wireless networks, e.g., 5G and 5G
beyond, to reduce scarce and expensive backhaul resource usage by prefetching
video files within radio access networks (RANs). Integration of this technique
with other advent technologies, such as wireless network virtualization and
multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible
video delivery opportunities, which leads to enhancements both for the
network's revenue and for the end-users' service experience. In this regard, we
propose a two-phase RAF for a parallel cooperative joint multi-bitrate video
caching and transcoding in heterogeneous virtualized MEC networks. In the cache
placement phase, we propose novel proactive delivery-aware cache placement
strategies (DACPSs) by jointly allocating physical and radio resources based on
network stochastic information to exploit flexible delivery opportunities.
Then, for the delivery phase, we propose a delivery policy based on the user
requests and network channel conditions. The optimization problems
corresponding to both phases aim to maximize the total revenue of network
slices, i.e., virtual networks. Both problems are non-convex and suffer from
high-computational complexities. For each phase, we show how the problem can be
solved efficiently. We also propose a low-complexity RAF in which the
complexity of the delivery algorithm is significantly reduced. A Delivery-aware
cache refreshment strategy (DACRS) in the delivery phase is also proposed to
tackle the dynamically changes of network stochastic information. Extensive
numerical assessments demonstrate a performance improvement of up to 30% for
our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure
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