Your excellent ideas will be peppered with flaws. Your best laid plans will be consumed by obstacles. (Laughter and applause.)Īnd you will definitely have your share of setbacks.
UC MERCED SCHEDULE CREATOR HOW TO
And I'm telling you, next year's graduation speaker better watch out, because Merced students know how to get what they want. And what it demonstrates is the power of many voices coming together to make something wonderful happen. (Applause.) Every school kid in the entire county, I understand, sent a postcard to the UC Board of Regents in order to convince them to select Merced, and I just love the fact that some of the graduates sitting this audience today participating were involved in that campaign, as well, and then they used the same strategy to get me here. This community, this Merced community, employed the same strategy to help get the University of California to build the new campus here in Merced. (Applause.) I am honored by your efforts and happy to be with you to celebrate this important milestone.īut I understand that this type of community-based letter writing campaign isn't unique to me.
UC MERCED SCHEDULE CREATOR OFFLINE
To evaluate the worst-case performance of an online algorithm, we compute the worst ratio between the objective value of the online algorithm and the optimal offline scheduler's objective for any input instance.And I want to thank in particular Sam Fong and Yaasha Sabba and all of the students who launched the "Dear Michelle" campaign. Roughly speaking, an algorithm with a small approximation ratio performs well compared to the optimal scheduler, even on a worst-case input.
An offline algorithm is said to be α-approximation if its objective is within a multiplicative factor α of the optimal scheduler's objective for any input instance. Approximation ratio and competitive ratio are two popular quantities that measure the worst-case performance of offline and online algorithms, respectively. We use the standard worst-case analysis to measure the quality of the algorithms we develop for the underlying scheduling problems. Machines are unrelated in the sense that each job can have different processing times on different machines. We consider the classic scheduling problem of minimizing total weighted completion time on unrelated parallel machines. It captures the heterogeneity of jobs and machines, which is widely observed in cloud computing environments and server clusters. Unrelated machines setting: The unrelated machines setting is one of the most general and versatile scheduling models. We study the maximum flow time minimization problem in this setting, which is a capacitated version of broadcast scheduling. The server can transmit at most one page at each time to satisfy a batch of requests for that page, up to a certain capacity. In this setting, there is a server that stores different pages of information and receives requests from clients over time for specific pages. This objective seeks to measure the progress made for partially completed coflows before their deadlines.īatch scheduling setting: This setting is used to model client-server systems like multiuser operating systems, web servers, etc. We consider coflow for the objective of maximizing partial throughput. A coflow is completed when all of its flows are completed. Coflow consists of several flows that each represent data communication from one machine to another. This problem is crucial in datacenters that are estimated to use energy as much as a city.Ĭo-flow setting: Coflow has recently been introduced to abstract communication patterns that are widely observed in the cloud and massively parallel computing frameworks. We study the problem of completing all jobs before their deadlines non-preemptively with the minimum energy on parallel machines with dynamic speed-scaling capabilities. In a classical model, a processor/machine runs at speed s when consuming power s^α where α>1 is a constant. Speed scaling setting: Modern processors typically allow dynamic speed-scaling offering an effective trade-off between high throughput and energy efficiency. We address four different scheduling settings motivated by modern computing environments speed scaling setting, co-flow setting, batch scheduling setting, and unrelated machines setting. This dissertation focuses on the design and analysis of approximation and online algorithms for scheduling problems arising in large datacenters and cloud computing environments.The recent advancement of science and engineering crucially relies on computing platforms processing large data sets, and modern datacenters provide such platforms.
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