With the growing costs of powering data centers, power management is gaining importance. Server consolidation in data centers, enabled by virtualization technologies, is becoming a popular option for organizations to reduce costs and improve manageability. While consolidation offers these benefits, it is important to ensure proper resource provisioning so that performance is not compromised. In addition to reducing the number of servers, there are other knobs -such as frequency/voltage scaling -that are being offered by recent hardware for finer granularity of power control. In this paper, we look at exploiting server consolidation and frequency/voltage control to reduce power consumption, while meeting certain provisioning guarantees. We formulate the problem as a variant of variable-sized bin packing. We show that the problem is NP-hard, and present an approximation algorithm for the same. The algorithm takes ( 2 log ) time for workloads, and has a provable approximation ratio. Experimental evaluation shows that in practice our algorithm obtains solutions very close (< 6.5% difference) to optimal.
There are numerous types of networks in the realworld which involve strategic actors: supply chain networks, logistics networks, company networks, and social networks. In this investigation, we explore the topologies of decentralized networks that will be formed by strategic actors who interact with one another. In particular, we analyze a network formation game in a strategic setting where payoffs of individuals depend only on their immediate neighbourhood. These localized payoffs incorporate the social capital emanating from bridging positions that nodes hold in the network. Using this novel and appealing model of network formation, our study explores the structure of networks that form, satisfying pairwise stability or efficiency or both. We derive sufficient conditions for the pairwise stability of several interesting network structures. We characterize topologies of efficient networks by applying classical results from extremal graph theory and discover that the Turán graph (or the complete equi-bipartite network) emerges as the unique efficient network under many configurations of parameters. We examine the tradeoffs between topologies of pairwise stable networks and efficient networks using the notion of price of stability. We identify several parameter configurations where the price of stability is 1 (or at least lower bounded by 0.5) in the proposed model. This leads to another key insight of this paper: under mild conditions, efficient networks will form when strategic individuals choose to add or delete links based on only localized payoffs. We study the dynamics of the proposed model by designing a simple myopic best response updating rule and implementing it on a customized network formation test-bed.
Integration of wind power with the grid has become an important problem. For integration, a producer needs to bid in a time-ahead market to deliver an amount of energy at a future point in time. Because wind speed and price are both uncertain, a producer needs to place bids on the basis of expected wind power yield and price. To this end, improving the accuracy of the prediction of wind speed has received much attention. However, the trade-off between expected profit and the prediction errors over a multi-period setting has been less studied.We fill this gap by quantifying trade-offs between profits and prediction errors. First, we obtain, under idealized conditions on the price and the yield processes, an optimal bid strategy as a closedform expression. Next, we evaluate the profit-vs-prediction tradeoff using this idealized bidding strategy on synthetic datasets which satisfy all the idealistic assumptions. We also consider two baselines -a naive strategy and an oracle strategy that has perfect knowledge over a limited horizon. Finally, we relax our assumptions and evaluate all strategies under real-world datasets. We identify and work around limitations of the idealized bidding strategy when the underlying assumptions are violated.On synthetic datasets, with no buffering and a (relative) prediction error of 25% , we find that our bidding approach performs significantly better than a naive approach and compares favourably (86%) to an oracle with a look-ahead of two time-slots and infinite buffer. On real-world datasets, with buffer equivalent to 20% of the maximum yield, our approach exceeds the naive approach by 25%, while remaining within 62% of a two-step look-ahead oracle that uses infinite buffering.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.