Journal ArticleDOI
Optimal Online Algorithms for Minimax Resource Scheduling
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TLDR
It is shown that randomized algorithms can outperform deterministic algorithms, but only if the amount of work done is a nonconcave function of resource allocation.Abstract:
We consider a very general online scheduling problem with an objective to minimize the maximum level of resource allocated. We find a simple characterization of an optimal deterministic online algorithm. We develop further results for the two, more specific problems of single resource scheduling and hierarchical line balancing. We determine how to compute optimal online algorithms for both problems using linear programming and integer programming, respectively. We show that randomized algorithms can outperform deterministic algorithms, but only if the amount of work done is a nonconcave function of resource allocation.read more
Citations
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Journal ArticleDOI
Energy peak shaving with local storage
TL;DR: A new problem inspired by energy pricing schemes in which a client is billed for peak usage, where the algorithm must determine each request without knowledge of future demands or free source availability, with the goal of maximizing the amount by which the peak is reduced.
Book ChapterDOI
Peak Shaving through Resource Buffering
TL;DR: This work introduces and solves a new problem inspired by energy pricing schemes in which a client is billed for peak usage, and gives efficient optimal algorithms for the offline problem, with and without a bounded battery.
Book ChapterDOI
When to reap and when to sow - lowering peak usage with realistic batteries
TL;DR: This paper extends the problem of minimizing peak charges to the more realistic setting of lossy batteries, which lose to conversion inefficiency a constant fraction of any amount charged (e.g. 33%).
Proceedings ArticleDOI
Peak-minimizing online EV charging
TL;DR: A model where a fraction of the users reserve EV charging jobs (with possible reservation uncertainty) in advance is considered and it is indicated that reservation can indeed significantly improve the competitive ratio and reduce the peak consumption.
Proceedings ArticleDOI
Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage
TL;DR: An optimal online algorithm for the problem that achieves the best possible competitive ratio (CR) among all (deterministic and randomized) online algorithms is developed, which solves a linear number of linear-fractional problems to find the best CR in polynomial time.
References
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Book
Randomized Algorithms
TL;DR: This book introduces the basic concepts in the design and analysis of randomized algorithms and presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications.
Journal ArticleDOI
Bounds for certain multiprocessing anomalies
TL;DR: In this paper, precise bounds are derived for several anomalies of this type in a multiprocessing system composed of many identical processing units operating in parallel, and they show that an increase in the number of processing units can cause an increased total length of time needed to process a fixed set of tasks.
Journal ArticleDOI
On-line routing of virtual circuits with applications to load balancing and machine scheduling
TL;DR: An algorithm is described that achieves on-line allocation of routes to virtual circuits (both point-to-point and multicast) with a constant competitive ratio with respect to maximum congestin, where n is the number of nodes in the network.
Journal ArticleDOI
The competitiveness of on-line assignments
TL;DR: In this article, the authors consider the problem of assigning each customer to an appropriate server in a manner that will balance the load on the servers and derive tight bounds on the competitive ratio in both deterministic and randomized cases, and conclude that randomized algorithms differ from deterministic ones by precisely a constant factor.
Proceedings ArticleDOI
On-line load balancing with applications to machine scheduling and virtual circuit routing
TL;DR: An algorithm is described that achieves an O (log n) competitive ratio, where n is the number of nodes in the network, for the case where virtual circuits continue to exist forever and for the related machines case, the first algorithm that achieves constant competitive ratio is described.