Lithium Battery Prismatic 3.2V 100AH Rechargeable Lifepo4 Battery Cell For EV/Golf Cart/Solar Systems
|Battery Type||rechargeable lithium battery|
|Nominal Capacity||102Ah at 0.3C|
|Minimum capacity||100Ah at 0.3C|
|Internal Impedance||≤1.0mΩ(by AC 1KHz)|
|End of charge voltage||3.65V at CC mode|
|End of discharge voltage||2.2V|
|Stanard charge current||50A at 0.5C|
|Max continuous discharge current||200A(under 2C)|
|Max pulse discharge current||500A(5C under 5S)|
|Max pulse charge current||100A (pulse current with 2C under 10S)|
A novel system developed by MIT researchers automatically “learns” how to schedule data-processing operations across thousands of servers — a task traditionally reserved for imprecise, human-designed algorithms. Doing so could help today’s power-hungry data centers run far more efficiently.
Data centers can contain tens of thousands of servers, which constantly run data-processing tasks from developers and users. Cluster scheduling algorithms allocate the incoming tasks across the servers, in real-time, to efficiently utilize all available computing resources and get jobs done fast.
Traditionally, however, humans fine-tune those scheduling algorithms, based on some basic guidelines (“policies”) and various tradeoffs. They may, for instance, code the algorithm to get certain jobs done quickly or split resource equally between jobs. But workloads — meaning groups of combined tasks — come in all sizes. Therefore, it’s virtually impossible for humans to optimize their scheduling algorithms for specific workloads and, as a result, they often fall short of their true efficiency potential.