Online Perfect Matching and Mobile Computing

The original publication is available at www.springerlink.comWe present a natural online perfect matching problem moti- vated by problems in mobile computing. A total of n customers connect and disconnect sequentially, and each customer has an associated set of stations to which it may connect. Each station has a capacity limit. We allow the network to preemptively switch a customer between allowed stations to make room for a new arrival. We wish to minimize the total number of switches required to provide service to every customer. Equiv- alently, we wish to maintain a perfect matching between customers and stations and minimize the lengths of the augmenting paths. We measure performance by the worst case ratio of the number of switches made to the minimum number required. When each customer can be connected to at most two stations: { Some intuitive algorithms have lower bounds of (n) and (n= log n). { When the station capacities are 1, there is an upper bound of O(pn). { When customers do not disconnect and the station capacity is 1, we achieve a competitive ratio of O(log n). { There is a lower bound of (pn) when the station capacities are 2. { We present optimal algorithms when the station capacity is arbitrary in special cases

A comparative analysis of adaptive middleware architectures based on computational reflection and aspect oriented programming to support mobile computing applications

Mobile computing applications are required to operate in environments in which the availability for resources and services may change significantly during system operation. As a result, mobile computing applications need to be capable of adapting to these changes to offer the best possible level of service to their users. However, traditional middleware is limited in its capability of adapting to environment changes and different users requirements. Computational Reflection and Aspect Oriented Programming paradigms have been used in the design and implementation of adaptive middleware architectures. In this paper, we propose two adaptive middleware architectures, one based on reflection and other based on aspects, which can be used to develop adaptive mobile applications. The reflection based architecture is compared to an aspect oriented based architecture from a quantitative perspective. The results suggest that middleware based on Aspect Oriented Programming can be used to build mobile adaptive applications that require less processor running time and more memory space than Computational Reflection while producing code that is easier to comprehend and modify.8th IFIP/IEEE International conference on Mobile and Wireless CommunicationRed de Universidades con Carreras en Informática (RedUNCI

System for improving the efficiency of wireless networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 30-31).Wireless data networks are widespread and growing quickly. As their use increases, many wireless networks are becoming congested. In addition, as wireless data capability moves into ever-smaller devices, power becomes a significant issue. This thesis presents a system that increases network bandwidth and decreases energy use without changing existing network hardware or protocols. We use specialized proxy servers to transparently modify the traffic sent over the mobile link such that the total energy used by the receiver is reduced and the effective bandwidth is increased. Our techniques include optimizing packet size, eliminating unnecessary traffic, and masking wireless packet losses. We design and implement two proxies--one for access points and one for mobile devices--which when used together, achieve up to a 20% decrease in energy and 38% increase in throughput.by Hans Robertson.M.Eng

Adaptive Disk Spindown via Optimal Rent-to-Buy in Probabilistic Environments

The original publication is available at www.springerlink.comIn the single rent-to-buy decision problem, without a priori knowledge of the amount of time a resource will be used we need to decide when to buy the resource, given that we can rent the resource for $1 per unit time or buy it once and for all for$c. In this paper we study algorithms that make a sequence of single rent-to-buy decisions, using the assumption that the resource use times are independently drawn from an unknown probability distribution. Our study of this rent- to-buy problem is motivated by important systems applications, speci cally, problems arising from deciding when to spindown disks to conserve energy in mobile computers [DKM, LKH, MDK], thread blocking decisions during lock acquisition in multiprocessor applications [KLM], and virtual circuit holding times in IP-over-ATM networks [KLP, SaK]. We develop a provably optimal and computationally e cient algorithm for the rent-to-buy problem. Our algorithm uses O(pt) time and space, and its expected cost for the tth resource use converges to optimal as O(plog t=t), for any bounded probability distribution on the resource use times. Alternatively, using O(1) time and space, the algorithm almost converges to optimal. We describe the experimental results for the application of our algorithm to one of the motivating systems problems: the question of when to spindown a disk to save power in a mobile computer. Simulations using disk access traces obtained from an HP workstation environment suggest that our algorithm yields signi cantly improved power/response time performance over the non-adaptive 2-competitive algorithm which is optimal in the worst-case competitive analysis model

A comparative analysis of adaptive middleware architectures based on computational reflection and aspect oriented programming to support mobile computing applications

Mobile computing applications are required to operate in environments in which the availability for resources and services may change significantly during system operation. As a result, mobile computing applications need to be capable of adapting to these changes to offer the best possible level of service to their users. However, traditional middleware is limited in its capability of adapting to environment changes and different users requirements. Computational Reflection and Aspect Oriented Programming paradigms have been used in the design and implementation of adaptive middleware architectures. In this paper, we propose two adaptive middleware architectures, one based on reflection and other based on aspects, which can be used to develop adaptive mobile applications. The reflection based architecture is compared to an aspect oriented based architecture from a quantitative perspective. The results suggest that middleware based on Aspect Oriented Programming can be used to build mobile adaptive applications that require less processor running time and more memory space than Computational Reflection while producing code that is easier to comprehend and modify.8th IFIP/IEEE International conference on Mobile and Wireless CommunicationRed de Universidades con Carreras en Informática (RedUNCI

Impact of processing energy on the capacity of wireless channels

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 61-64).Power efficiency is a capital issue in the study of mobile wireless nodes owing to constraints on their battery size and weight. A careful examination of the power consumption in low-power nodes shows that, as the total power available to such nodes decreases, the ratio of power consumed for transmission purposes to the power consumed on other non-transmission processes also decreases. The latter power therefore constitutes a considerable fraction of the total power available to such devices. We perform our study in terms of power per unit of time, or energy. Traditional in- formation theoretic energy constraints consider only the energy used for transmission purposes. We study optimal transmission strategies by explicitly taking into account the energy expended by processes other than transmission, that run when the transmitter is in the 'on' state. We term this energy by 'processing energy'. We first derive the capacity of a single user Additive White Gaussian Noise (AWGN) channel in the presence of processing energy. We prove that, unlike the case where only transmission energy is taken into account, achieving capacity may require intermittent, or 'bursty', transmission. We show that in the low Signal to Noise Ratio (SNR) regime, burstiness becomes optimal when the processing energy is greater than half the square of the total energy available to the transmitter. This analysis is extended to the AWGN multiple access channel with M senders and a single receiver. We first show that, under a processing energy constraint, Time Division Multiple Access (TDMA) outperforms other known multiple access techniques in the maximization of the sum rate.(cont.) We prove that, for that same purpose, burstiness is capacity achieving in the low SNR regime when the sum of the ratios of total energy to processing energy is less than unity. Moreover, we present numerical results that show the improvement in the shape of the general two-user achievable rate region obtained with a bursty transmission scheme. We compare the rates obtained by bursty signaling to the rates that can be achieved by TDMA and to the Cover-Wyner region under a processing energy constraint. Finally, we show that, in low SNR regime, a time-variable channel can be analyzed as a channel with no variability but with processing energy. In fact, the results we obtain for the bursty signaling in time-variable channels from the processing energy correspondence match those of other studies ([3], [4], [5]) that do not make use of the processing energy argument. This leads to posit a model in which energy can be regarded as a unifying cost or penalty for various communication impediments.by Pamela Youssef-Massaad.S.M

EFFECTIVE GROUPING FOR ENERGY AND PERFORMANCE: CONSTRUCTION OF ADAPTIVE, SUSTAINABLE, AND MAINTAINABLE DATA STORAGE

The performance gap between processors and storage systems has been increasingly critical overthe years. Yet the performance disparity remains, and further, storage energy consumption israpidly becoming a new critical problem. While smarter caching and predictive techniques domuch to alleviate this disparity, the problem persists, and data storage remains a growing contributorto latency and energy consumption.Attempts have been made at data layout maintenance, or intelligent physical placement ofdata, yet in practice, basic heuristics remain predominant. Problems that early studies soughtto solve via layout strategies were proven to be NP-Hard, and data layout maintenance todayremains more art than science. With unknown potential and a domain inherently full of uncertainty,layout maintenance persists as an area largely untapped by modern systems. But uncertainty inworkloads does not imply randomness; access patterns have exhibited repeatable, stable behavior.Predictive information can be gathered, analyzed, and exploited to improve data layouts. Ourgoal is a dynamic, robust, sustainable predictive engine, aimed at improving existing layouts byreplicating data at the storage device level.We present a comprehensive discussion of the design and construction of such a predictive engine,including workload evaluation, where we present and evaluate classical workloads as well asour own highly detailed traces collected over an extended period. We demonstrate significant gainsthrough an initial static grouping mechanism, and compare against an optimal grouping method ofour own construction, and further show significant improvement over competing techniques. We also explore and illustrate the challenges faced when moving from static to dynamic (i.e. online)grouping, and provide motivation and solutions for addressing these challenges. These challengesinclude metadata storage, appropriate predictive collocation, online performance, and physicalplacement. We reduced the metadata needed by several orders of magnitude, reducing the requiredvolume from more than 14% of total storage down to less than 12%. We also demonstrate how ourcollocation strategies outperform competing techniques. Finally, we present our complete modeland evaluate a prototype implementation against real hardware. This model was demonstrated tobe capable of reducing device-level accesses by up to 65%

Transaction-filtering data mining and a predictive model for intelligent data management

This thesis, first of all, proposes a new data mining paradigm (transaction-filtering association rule mining) addressing a time consumption issue caused by the repeated scans of original transaction databases in conventional associate rule mining algorithms. An in-memory transaction filter is designed to discard those infrequent items in the pruning steps. This filter is a data structure to be updated at the end of each iteration. The results based on an IBM benchmark show that an execution time reduction of 10% - 19% is achieved compared with the base case. Next, a data mining-based predictive model is then established contributing to intelligent data management within the context of Centre for Grid Computing. The capability of discovering unseen rules, patterns and correlations enables data mining techniques favourable in areas where massive amounts of data are generated. The past behaviours of two typical scenarios (network file systems and Data Grids) have been analyzed to build the model. The future popularity of files can be forecasted with an accuracy of 90% by deploying the above predictor based on the given real system traces. A further step towards intelligent policy design is achieved by analyzing the prediction results of files’ future popularity. The real system trace-based simulations have shown improvements of 2-4 times in terms of data response time in network file system scenario and 24% mean job time reduction in Data Grids compared with conventional cases.EThOS - Electronic Theses Online ServiceGBUnited Kingdo