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Pricing bilateral electricity trade between smart grids and hybrid green datacenters

Pricing bilateral electricity trade between smart grids and ...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Zhou, Zhi Liu, Fangming Li, Zongpeng Services Computing Technology and System Lab. School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Calgary Canada

ISBN: (纸本)9781450334860

Datacenter demand response is envisioned as a promising approach for mitigating operational instability faced by smart grids. It enables significant potentials in peak load shedding and facilitates the incorporation of distributed generation and intermittent energy sources. This work considers two key aspects towards realtime electricity pricing for eliciting demand response: (i) Two-way electricity flow between smart grids and large datacenters with hybrid green generation capabilities. (ii) The geo-distributed nature of large cloud systems, and hence the potential competition among smart grids that serve different datacenters of the cloud. We propose a pricing scheme tailored for geo-distributed green datacenters, from a multi-leader single-follower game point of view. At the cloud side, in quest for performance, scalability and robustness, the energy cost is minimized in a distributed manner, based on the technique of alternating direction of multipliers (ADMM). At the smart grid side, a practical equilibrium of the pricing game is desired. To this end, we employ mathematical programming with equilibrium constraints (MPEC), equilibrium problem with equilibrium constraints (EPEC) and exact linearization, to transform the multi-leader single-follower pricing game into a mixed integer linear program (MILP) that can be readily solved. The effectiveness of the proposed solutions is evaluated based on trace-driven simulations. © Copyright 2015 acm.

关键词： Distributed cloud

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Bandits with budgets: Regret lower bounds and optimal algorithms

Bandits with budgets: Regret lower bounds and optimal algori...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Combes, Richard Jiang, Chong Srikant, Rayadurgam Centrale-Supelec L2S Gif-sur-Yvette France Electrical and Computer Engineering University of Illinois Urbana-Champaign United States

ISBN: (纸本)9781450334860

We investigate multi-armed bandits with budgets, a natural model for ad-display optimization encountered in search engines. We provide asymptotic regret lower bounds satisfied by any algorithm, and propose algorithms which match those lower bounds. We consider different types of budgets: scenarios where the advertiser has a fixed budget over a time horizon, and scenarios where the amount of money that is available to spend is incremented in each time slot. Further, we consider two different pricing models, one in which an advertiser is charged for each time her ad is shown (i.e., for each impression) and one in which the advertiser is charged only if a user clicks on the ad. For all of these cases, we show that it is possible to achieve O(log(T)) regret. For both the cost-per-impression and cost-per-click models, with a fixed budget, we provide regret lower bounds that apply to any uniformly good algorithm. Further, we show that B-KL-UCB, a natural variant of KL-UCB, is asymptotically optimal for these cases. Numerical experiments (based on a real-world data set) further suggest that B-KL-UCB also has the same or better finite-time performance when compared to various previously proposed (UCB-like) algorithms, which is important when applying such algorithms to a real-world problem. © Copyright 2015 acm.

关键词： Search engines

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Localized epidemic detection in networks with overwhelming noise

Localized epidemic detection in networks with overwhelming n...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Meirom, Eli A. Milling, Chris Caramanis, Constantine Mannor, Shie Shakkottai, Sanjay Orda, Ariel Dept. Of Electrical Engineering Technion - Israel Institute of Technology Haifa Israel Dept. of Electrical and Computer Engineering University of Texas AustinTX United States

ISBN: (纸本)9781450334860

We consider the problem of detecting an epidemic in a population where individual diagnoses are extremely noisy. We show that exculsively local, approximate knowledge of the contact network suffices to accurately detect the epidemic. The motivation for this problem is the plethora of examples (inuenza strains in humans, or computer viruses in smartphones, etc.) where reliable diagnoses are scarce, but noisy data plentiful. In u or phone-viruses, exceedingly few infected people/phones are professionally diagnosed (only a small fraction go to a doctor) but less reliable secondary signatures (e.g., people staying home, or greater-than-typical upload activity) are more readily available. Our algorithm requires only local-neighbor knowledge of this graph, and in a broad array of settings that we describe, succeeds even when false negatives and false positives make up an overwhelming majority of the data available. Our results show it succeeds in the presence of partial information about the contact network, and also when are many (hundreds, in our examples) of initial patients-zero. © Copyright 2015 acm.

关键词： Diagnosis

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Transient and steady-state regime of a family of list-based cache replacement algorithms

Transient and steady-state regime of a family of list-based ...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Gast, Nicolas Van Houdt, Benny Inria Univ. Grenoble Alpes CNRS LIG GrenobleF-38000 France Department of Mathematics and Computer Science University of Antwerp Belgium

ISBN: (纸本)9781450334860

In this paper we study the performance of a family of cache replacement algorithms. The cache is decomposed into lists. Items enter the cache via the first list. An item enters the cache via the first list and jumps to the next list whenever a hit on it occurs. The classical policies FIFO, RANDOM, CLIMB and its hybrids are obtained as special cases. We present explicit expressions for the cache content distribution and miss probability under the IRM model. We develop an algorithm with a time complexity that is polynomial in the cache size and linear in the number of items to compute the exact miss probability. We introduce lower and upper bounds on the latter that can be computed in a time that is linear in the cache size times the number of items. We further introduce a mean field model to approximate the transient behavior of the miss probability and prove that this model becomes exact as the cache size and number of items tends to infinity. We show that the set of ODEs associated to the mean field model has a unique fixed point that can be used to approximate the miss probability in case the exact computation becomes too time consuming. Using this approximation, we provide guidelines on how to select a replacement algorithm within the family considered such that a good trade-off is achieved between the cache reactivity and its steady-state hit probability. We simulate these cache replacement algorithms on traces of real data and show that they can outperform LRU. Finally, we also disprove the well-known conjecture that the CLIMB algorithm is the optimal finite-memory replacement algorithm under the IRM model. © Copyright 2015 acm.

关键词： Storage management

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Challenges in integrating the analysis of multiple non-functional properties in model-driven software engineering 1

Challenges in integrating the analysis of multiple non-funct...

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2015 1st acm/SPEC Workshop on Challenges in Performance Methods for Software Development, WOSP-C 2015

作者： Petriu, Dorina C. Carleton University Department of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9781450333405

This vision paper discusses the challenges of integrating the analysis of multiple Non-Functional Properties (NFP) in the model-driven software engineering process, where formal analysis models are generated by model transformations from annotated software models. The paper proposes an integration approach based on an ecosystem of inter-related heterogeneous modeling artifacts intended to support consistent co-evolution of the software and analysis models, cross-model traceability, incremental propagation of changes across models and (semi)automated software process steps. Another goal is to investigate new metaheuristics approaches for reducing the size of the design space to be explored in the search for a design solution that will meet all the non-functional requirements. © 2015 acm.

关键词： Ecosystem of models Model-driven analysis Model-driven engineering Non-functional properties

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Online auctions in IaaS clouds: Welfare and profit maximization with server costs 15

Online auctions in IaaS clouds: Welfare and profit maximizat...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Zhang, Xiaoxi Huang, Zhiyi Wu, Chuan Li, Zongpeng Lau, Francis C. M. Dept. of Computer Science University of Hong Kong Hong Kong Dept. of Computer Science University of Calgary Canada

ISBN: (纸本)9781450334860

Auction design has recently been studied for dynamic resource bundling and VM provisioning in IaaS clouds, but is mostly restricted to the one-shot or offline setting. This work targets a more realistic case of online VM auction design, where: (i) cloud users bid for resources into the future to assemble customized VMs with desired occupation durations;(ii) the cloud provider dynamically packs multiple types of resources on heterogeneous physical machines (servers) into the requested VMs;(iii) the operational costs of servers are considered in resource allocation;(iv) both social welfare and the cloud provider's net profit are to be maximized over the system running span. We design truthful, polynomial time auctions to achieve social welfare maximization and/or the provider's profit maximization with good competitive ratios. Our mechanisms consist of two main modules: (1) an online primal-dual optimization framework for VM allocation to maximize the social welfare with server costs, and for revealing the payments through the dual variables to guarantee truthfulness;and (2) a randomized reduction algorithm to convert the social welfare maximizing auctions to ones that provide a maximal expected profit for the provider, with competitive ratios comparable to those for social welfare. We adopt a new application of Fenchel duality in our primal-dual framework, which provides richer structures for convex programs than the commonly used Lagrangian duality, and our optimization framework is general and expressive enough to handle various convex server cost functions. The efficacy of the online auctions is validated through careful theoretical analysis and trace-driven simulation studies. © Copyright 2015 acm.

关键词： Costs

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Memory row reuse distance and its role in optimizing application performance

Memory row reuse distance and its role in optimizing applica...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Kandemir, Mahmut Zhao, Hui Tang, Xulong Karakoy, Mustafa Department of Computer Science and Engineering Pennsylvania State University University ParkPA16802 United States Department of Computer Engineering TOBB ETU Ankara Turkey

ISBN: (纸本)9781450334860

Continuously increasing dataset sizes of large-scale applications overwhelm on-chip cache capacities and make the performance of last-level caches (LLC) increasingly important. That is, in addition to maximizing LLC hit rates, it is becoming equally important to reduce LLC miss latencies. One of the critical factors that influence LLC miss latencies is row-buffer locality (i.e., the fraction of LLC misses that hit in the large buffer attached to a memory bank). While there has been a plethora of recent works on optimizing rowbuffer performance, to our knowledge, there is no study that quantifies the full potential of row-buffer locality and impact of maximizing it on application performance. Focusing on multithreaded applications, the first contribution of this paper is the definition of a new metric called (memory) row reuse distance (RRD). We show that, while intra-core RRDs are relatively small (increasing the chances for row-buffer hits), inter-core RRDs are quite large (increasing the chances for row-buffer misses). Motivated by this, we propose two schemes that measure the maximum potential benefits that could be obtained from minimizing RRDs, to the extent allowed by program dependencies. Specifically, one of our schemes (Scheme-I) targets only intra-core RRDs, whereas the other one (Scheme-II) aims at reducing both intra-core RRDs and inter-core RRDs. Our experimental evaluations demonstrate that (i) Scheme-I reduces intra-core RRDs but increases inter-core RRDs;(ii) Scheme-II reduces inter-core RRDs significantly while achieving a similar behavior to Scheme-I as far as intra-core RRDs are concerned;(iii) Scheme-I and Scheme-II improve execution times of our applications by 17% and 21%, respectively, on average;and (iv) both our schemes deliver consistently good results under different memory request scheduling policies. © Copyright 2015 acm.

关键词： Scheduling

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An online learning approach to improving the quality of crowd-sourcing

An online learning approach to improving the quality of crow...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Liu, Yang Liu, Mingyan Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

ISBN: (纸本)9781450334860

We consider a crowd-sourcing problem where in the process of labeling massive datasets, multiple labelers with unknown annotation quality must be selected to perform the labeling task for each incoming data sample or task, with the results aggregated using for example simple or weighted majority voting rule. In this paper we approach this labeler selection problem in an online learning framework, whereby the quality of the labeling outcome by a specific set of labelers is estimated so that the learning algorithm over time learns to use the most effective combinations of labelers. This type of online learning in some sense falls under the family of multi-armed bandit (MAB) problems, but with a distinct feature not commonly seen: since the data is unlabeled to begin with and the labelers' quality is unknown, their labeling outcome (or reward in the MAB context) cannot be directly verified;it can only be estimated against the crowd and known probabilistically. We design an efficient online algorithm LS-OL using a simple majority voting rule that can differentiate high- and low-quality labelers over time, and is shown to have a regret (w.r.t. always using the optimal set of labelers) of O(log2 T) uniformly in time under mild assumptions on the collective quality of the crowd, thus regret free in the average sense. We discuss performance improvement by using a more sophisticated majority voting rule, and show how to detect and filter out "bad" (dishonest, malicious or very incompetent) labelers to further enhance the quality of crowd-sourcing. Extension to the case when a labeler's quality is task-type dependent is also discussed using techniques from the literature on continuous arms. We present numerical results using both simulation and a real dataset on a set of images labeled by Amazon Mechanic Turks (AMT). © Copyright 2015 acm.

关键词： Learning algorithms

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Joint cyber and physical attacks on power grids: Graph theoretical approaches for information recovery

Joint cyber and physical attacks on power grids: Graph theor...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Soltan, Saleh Yannakakis, Mihalis Zussman, Gil Electrical Engineering Columbia University New YorkNY United States Computer Science Columbia University New YorkNY United States

ISBN: (纸本)9781450334860

Recent events demonstrated the vulnerability of power grids to cyber attacks and to physical attacks. Therefore, we focus on joint cyber and physical attacks and develop methods to retrieve the grid state information following such an attack. We consider a model in which an adversary attacks a zone by physically disconnecting some of its power lines and blocking the information flow from the zone to the grid's control center. We use tools from linear algebra and graph theory and leverage the properties of the power flow DC approximation to develop methods for information recovery. Using information observed outside the attacked zone, these methods recover information about the disconnected lines and the phase angles at the buses. We identify sufficient conditions on the zone structure and constraints on the attack characteristics such that these methods can recover the information. We also show that it is NP-hard to find an approximate solution to the problem of partitioning the power grid into the minimum number of attack-resilient zones. However, since power grids can often be represented by planar graphs, we develop a constant approximation partitioning algorithm for these graphs. Finally, we numerically study the relationships between the grid's resilience and its structural properties, and demonstrate the partitioning algorithm on real power grids. The results can provide insights into the design of a secure control network for the smart grid. © Copyright 2015 acm.

关键词： Electric power transmission networks

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DiskAccel: Accelerating disk-based experiments by representative sampling 15

DiskAccel: Accelerating disk-based experiments by representa...

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acm sigmetrics International conference on measurement and modeling of computer systems, sigmetrics 2015

作者： Tarihi, Mojtaba Asadi, Hossein Sarbazi-Azad, Hamid Department of Computer Engineering Sharif University of Technology Tehran Iran AICT Innovation Center Sharif University of Technology Tehran Iran Institute for Research in Fundamental Sciences Tehran Iran

ISBN: (纸本)9781450334860

Disk traces are typically used to analyze real-life workloads and for replay-based evaluations. This approach benefits from capturing important details such as varying behavior patterns, bursty activity, and diurnal patterns of system activity, which are often missing from the behavior of workload synthesis tools. However, accurate capture of such details requires recording traces containing long durations of system activity, which are difficult to use for replay-based evaluation. One way of solving the problem of long storage trace duration is the use of disk simulators. While publicly available disk simulators can greatly accelerate experiments, they have not kept up with technological innovations in the field. The variety, complexity, and opaque nature of storage hardware make it very difficult to implement accurate simulators. The alternative, replaying the whole traces on real hardware, suffers from either long run-time or required manual reduction of experimental time, potentially at the cost of reduced accuracy. On the other hand, burstiness, autocorrelation, and complex spatio-temporal properties of storage workloads make the known methods of sampling workload traces less effective. In this paper, we present a methodology called DiskAccel to efficiently select key intervals of a trace as representatives and to replay them to estimate the response time of the whole workload. Our methodology extracts a variety of spatial and temporal features from each interval and uses efficient data mining techniques to select the representative intervals. To verify the proposed methodology, we have implemented a tool capable of running whole traces or selective intervals on real hardware, warming up hardware state in an accelerated manner, and emulating request causality while minimizing request inter-arrival time error. Based on our experiments, DiskAccel manages to speed up disk replay by more than two orders of magnitude, while keeping average estimation error at 7.6%. ©

关键词： Simulators

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