This paper addresses the challenge of multi-policy optimization in decentralized autonomic systems. We evaluate several multi-policy reinforcement learning-based optimization techniques in an urban traffic control sim...
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ISBN:
(纸本)9781615673346
This paper addresses the challenge of multi-policy optimization in decentralized autonomic systems. We evaluate several multi-policy reinforcement learning-based optimization techniques in an urban traffic control simulation, a canonical example of a decentralized autonomic system. Our results indicate that W-learning, which learns separately for each policy and then selects between nominated actions based on current action importance, is a suitable approach for optimization towards multiple policies on non-collaborating agents in heterogeneous autonomic environments.
Cloud Computing infrastructures have been developed as individual islands, and mostly proprietary solutions so far. However, as more and more infrastructure providers apply the technology, users face the inevitable qu...
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Cloud Computing infrastructures have been developed as individual islands, and mostly proprietary solutions so far. However, as more and more infrastructure providers apply the technology, users face the inevitable question of using multiple infrastructures in parallel. Federated cloud management systems offer a simplified use of these infrastructures by hiding their proprietary solutions. As the infrastructure becomes more complex underneath these systems, the situations (like system failures, handling of load peaks and slopes) that users cannot easily handle, occur more and more frequently. Therefore, federations need to manage these situations autonomously without user interactions. This paper introduces a methodology to autonomously operate cloud federations by controlling their behavior with the help of knowledge management systems. Such systems do not only suggest reactive actions to comply with established Service Level Agreements (SLA) between provider and consumer, but they also find a balance between the fulfillment of established SLAs and resource consumption. The paper adopts rule-based techniques as its knowledge management solution and provides an extensible rule set for federated clouds built on top of multiple infrastructures.
In computersystems, printable characters include not only 62 alphanumeric characters (0-9, a-z and A-Z) but also non-alphanumeric characters (e.g. *, \, ∧, %). Base64 is a commonly used encoding scheme that rep...
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In computersystems, printable characters include not only 62 alphanumeric characters (0-9, a-z and A-Z) but also non-alphanumeric characters (e.g. *, \, ∧, %). Base64 is a commonly used encoding scheme that represents binary data in an ASCII string format However, for Base64, non-alphanumeric characters (e.g. *, \, ∧, %) are problematic for filenames and URLs. For example, “+” is error-prone in URL and “/” is not allowed in filenames. To address this issue, we propose a new method, Base62x, as an alternative approach to Base64. The proposed method is more effective in application development and fully compatible with symbol-sensitive applications, such as, file systems, IP addresses and safe transmission of non-ASCII over the Internet.
Attribute-based access control (ABAC) represents a generic model of access control that provides a high level of flexibility and promotes information and security sharing. Since ABAC considers a large set of attribute...
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In this paper, we evaluate and compare the performance of several spanning tree routing strategies for divisible load scheduling on arbitrary graphs and derive recommendations as to which routing strategy provides a b...
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ISBN:
(纸本)9781424449224;9781424449217
In this paper, we evaluate and compare the performance of several spanning tree routing strategies for divisible load scheduling on arbitrary graphs and derive recommendations as to which routing strategy provides a better trade-off between complexity and time performance. We consider a network comprising heterogeneous processors interconnected by heterogeneous links in an arbitrary manner. We evaluate the performance over a wide range of arbitrary dense graphs with varying connectivity and processor densities and study the effect of network scalability. In addition, we introduce a novel spanning tree routing strategy, which is referred to as minimum equivalent network spanning tree (EST), and analyze its performance. We apply the resource-aware optimal load distribution with optimal sequencing (RAOLD-OS) scheduling algorithm presented in the literature for obtaining an optimal solution. This study attempts to pool all known and applicable divisible load scheduling algorithms for arbitrary networks and presents a collective and comparative view of their performance.
To date, software development for wireless sensor network nodes is still characterized by low-level ad-hoc programming to a large extent. This short-paper argues for a methodologically more systematic approach on the ...
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Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the traffic light phase schedule, they could predict the traffic light state at arrival time and could reduce fu...
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Traffic lights strongly impact vehicle movement and fuel consumption in cities. If drivers were aware of the traffic light phase schedule, they could predict the traffic light state at arrival time and could reduce fuel consumption. To acquire information like traffic light phase schedules, our vision is that drivers share their velocity profiles in a digital cloud, and in return benefit from smart algorithms evaluating the collected data. We present one such algorithm, Traffic Light State Estimation (TLSE), that operates on the velocity profiles to backward-estimate phase schedules of traffic light signal groups operating with fixed cycle length (representing about 80% of all traffic lights in the US). We present simulation results showing that phase schedule prediction on the base of TLSE is correct more than 90% of the time.
Fuel-efficient driving is difficult in unknown or complex environments. To aid the driver with this task, we present a novel method of tactical route optimization by calculating a short-term fuel-reduced velocity prof...
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Fuel-efficient driving is difficult in unknown or complex environments. To aid the driver with this task, we present a novel method of tactical route optimization by calculating a short-term fuel-reduced velocity profile. This profile is based on knowledge of location-dependent velocity profiles that are collected by the vehicles over time and shared with other vehicles. To determine a fuel-efficient velocity profile, we first split the planned route into segments. We cluster the historical velocity profiles within each segment using a Dynamic Time Warping algorithm, obtaining classes of velocity profiles and their probabilities. We construct a transition graph between velocity profile classes from adjacent segments and calculate the most probable path through the next segments ahead. This path represents the most likely future velocity profile under the assumption that the driver behaves like previous drivers on the same segment. Given the constraints defined by this profile, we calculate the fuel-reduced velocity profile with help of a shortest path algorithm in a vehicle-specific fuel-consumption graph. First results in an urban environment indicate possible fuel savings of about 8.3% compared to the most probable profile.
9.1 Storage Load Balancing of Data in distributed Hash Tables 9.1.1 Definitions 9.1.2 A Statistical Analysis 9.1.3 Algorithms for Load Balancing in DHTs 9.1.4 Comparison of Load-Balancing Approaches 9.2 Reliability of...
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