ARMY AL&T
R
unning out of toilet paper has a way of focusing your attention. In that way, the COVID-19 pandemic made many of us more familiar with logistics and the supply chain. We had to estimate the depletion
rate of our supplies and the lead time to procure replacements. We discovered the convenience of delivery—not only books and household goods but also food, and even alcohol. Te supply chain became so overwhelmed with demand that some of us were drafted into it, distributing misdelivered packages to our neighbors.
Te first two entries in this series (“Logistics for Data,” Army AL&T Fall 2023 and “Logistics for Data,” Army AL&T Winter 2024) discussed the demand signal for data and the inventory and warehousing of data. Tis third and final entry will focus on data transportation, synchronization and provenance.
By establishing clear priorities for data updates, battlefield commanders can ensure that the most critical information is available right after network restoration.
DATA TRANSPORTATION Whether toilet paper and milk or fuel and ammunition, logis- tics ensures that something gets to where it needs to be when it needs to be there. On the battlefield, transporting data comes at a price: detectability. Data sent from one place to another via line-of-sight communication or through a satellite emits a signal. Te more data transmitted, the bigger—or longer—the signal. While we want to capitalize on the promise of data, we must do so efficiently. If moving the data to the computing resources has its limitations, so does the alternative: moving the computing power to the data. A local data node that provides an on-prem- ises means for processing data can increase costs because hardware must exist for each node, while size, weight and power restric- tions limit capacity.
Te Army has settled on a hybrid of a cloud node and multiple local data nodes for the mission command domain. Te cloud node provides scalability and flexibility, offering expansive storage and computational power for machine learning and advanced data analytics. It excels in handling large volumes of data and adapts to fluctuating workloads. Te cloud node is the central reposi- tory for relatively static data, such as personnel and equipment.
Te local data nodes, in contrast, offer a more controlled envi- ronment. Tey are reliable and secure, especially when handling sensitive data or operations requiring strict data governance. Teir physical proximity to the core operations often translates into faster data processing speeds, albeit with limited scalability compared to a cloud node.
Effective load balancing and using content delivery networks also can mitigate network lag by distributing the data load across multiple local nodes, reducing the strain on any single node and ensuring a more efficient data flow. When real-time data transfer is not critical, employing asynchronous data replication meth- ods can help manage connectivity issues by allowing data to be queued and transmitted when the connection is stable. In short, managing data in a distributed architecture with network chal- lenges involves a combination of technological solutions and strategic data-handling practices to ensure efficient, secure and reliable data flow.
DATA SYNCHRONIZATION Te key difference between a computer network in a tactical environment and a commercial one is availability. If your home internet connection lacks reliability, often the problem is calling from inside your house, and a router reset will put it right. If the issue is outside your control, you switch providers or ask for a refund because, doggone it, you paid for full service.
Resilient data management systems are essential in a degraded network environment with intermittent or unstable connectiv- ity. Robust data synchronization protocols can handle connection interruptions and prioritize efficient data transmission when the network becomes available. Intelligent caching mechanisms can also store and update data locally, reducing the impact of network disruptions.
Tis reality is another reason for the Army’s hybrid data archi- tecture. Ideally, the data in the cloud and local nodes are synchronized. However, the volume of data not only may over- whelm the means of transporting data under ideal circumstances, but also slow to a stop under some conditions.
https://asc.ar my.mil 69
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124
