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Time-critical applications, such as virtual reality and cyber-physical systems, require not only low end-to-end latency, but also the timely delivery of information. While high-speed Ethernet adoption has reduced interconnect fabric latency, bottlenecks persist in data storage, retrieval, and processing. This work examines status updating systems where sources generate time-stamped updates that are stored in memory, and readers fulfill client requests by accessing these stored updates. Clients then utilize the retrieved updates for further computations. The asynchronous interaction between writers and readers presents challenges, including: (i) the potential for readers to encounter stale updates due to temporal disparities between the writing and reading processes, (ii) the necessity to synchronize writers and readers to prevent race conditions, and (iii) the imperative for clients to process and deliver updates within strict temporal constraints. In the first part, we study optimal reading policies in both discrete and continuous time domains to minimize the Age of Information (AoI) of source updates at the client. One of the main contributions of this part includes showing that lazy reading is timely. In the second part, we analyze the impact of synchronization primitives on update timeliness in a packet forwarding scenario, where location updates are written to a shared routing table, and application updates read from it to ensure correct delivery. Our theoretical and experimental results show that using a lock-based primitive is suitable for timely application update delivery at higher location update rates, while a lock-free mechanism is more effective at lower rates. The final part focuses on optimizing update processing when updates require multiple sequential computational steps. We compare the age performance across a multitude of pipelined and parallel server models and characterize the age-power trade-off in these models. Additionally, our analysis reveals that synchronous sequential processing is more conducive to timely update processing than asynchronous methods, and that parallel processing outperforms pipeline services in terms of AoI.
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DISPERSE: A Decentralized Architecture for Content Replication Resilient to Node Failures
This paper introduces DISPERSE, a distributed scalable architecture for delivery of content and services that provides resilience against node failure through location-independent storage and replication of content. Current content delivery networks (CDNs) have, at least to some degree, a centralized structure thus susceptible to a single point of failure. DISPERSE addresses this limitation by implementing a fully de-centralized structure. DISPERSE is a two-layer architecture: the first layer (front-end layer) exposes services (e.g., Web, SFTP) to clients; the second layer (back-end layer) provides reliable distributed storage of content and application state. Content in DISPERSE's back-end layer is stored and exchanged as Named Data Network (NDN) content objects. This allows DISPERSE to implement fine-grained, location-independent, fully decentralized content replication mechanisms. We validate the performance of DISPERSE under two node failure scenarios. In the first scenario, content can be stored in any DISPERSE node, and all nodes are equally likely to fail. In this scenario, we use non-linear optimization techniques to determine the optimal number of content copies under availability and latency constraints. In the second scenario, different nodes fail with different probabilities, and content is stored in nodes according to its value, node failure probability, and resource availability. This scenario is addressed as an instance of the minimum cost flow problem. Our results show that DISPERSE reduces the failure of content retrieval by five orders of magnitude compared to common CDN implementations, without significantly increasing content retrieval delay. Further, numerical results show that DISPERSE improves content availability by a factor of 1.3x-2.3x when deploying the minimum cost flow algorithm.
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- Award ID(s):
- 1814846
- PAR ID:
- 10112524
- Date Published:
- Journal Name:
- IEEE eTransactions on network and service management
- ISSN:
- 1932-4537
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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