Virtual machine (VM) consolidation provides a promising approach to save energy and to improve resource utilization in data centers. However, the aggressive consolidation of virtual machines may lead to service-level agreements (SLA) violation, which is essential for data centers and their users. Therefore, it is very meaningful to strike a tradeoff between power efficient and reduction of SLA violation level. In this paper, we propose a host overloading/underloading detection algorithm and a new VM placement algorithm based on our proposed robust simple linear regression prediction model for SLA-aware and energy-efficient consolidation of virtual machines in cloud data centers. Different from the native linear regression, our proposed methods amend the prediction and squint toward over-prediction by adding the error to the prediction; in this paper, we propose eight methods to calculate the error. We evaluate our proposed algorithms by extended the CloudSim simulator using PlanetLab workload and random workload. The experimental results show that our proposed model can reduce SLA violation rates by at most 99.16% and energy consumption by at most 25.43% for the real-world workload.
Molecular communication (MC) system is an emerging technology for nanoscale networks. Therefore, there is a requirement to develop a new end-to-end MC model, which may deliver new perceptions into the aspect of these nanoscale networks. This paper aims to implement the MC framework as an endto-end deep reinforcement learning (DRL) auto encoder (AE). The technique enables training of the MC system without any information about the actual channel (medium) model. For training the receiver and transmitter, the proposed techniques are supervised learning and DRL, respectively. The results show that the performance of the DRL autoencoder (AE) based system achieves nearly the same performance as the traditional modulation and demodulation methods in term of bit-error-rate (BER) under the Gaussian noise channel but with less complexity. The proposed technique can also be joint with the other coding methods to improve their performance.
INDEX TERMSMolecular communication (MC), deep reinforcement learning (DRL), auto encoder (AE), bit-error-rate (BER).
Failure detectors are used to build high availability distributed systems as the fundamental component. To meet the requirement of a complicated large-scale distributed system, accrual failure detectors that can adapt to multiple applications have been studied extensively. However, several implementations of accrual failure detectors do not adapt well to the cloud service environment. To solve this problem, a new accrual failure detector based on Weibull Distribution, called the Weibull Distribution Failure Detector, has been proposed specifically for cloud computing. It can adapt to the dynamic and unexpected network conditions in cloud computing. The performance of the Weibull Distribution Failure Detector is evaluated and compared based on public classical experiment data and cloud computing experiment data. The results show that the Weibull Distribution Failure Detector has better performance in terms of speed and accuracy in unstable scenarios, especially in cloud computing.
Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and few surveys have been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human errors and to simplify management, self-management CPS (SCPS) is a wise choice. To achieve dependable self-management, systematic solutions are necessary to verify the design and to guarantee the safety of self-adaptation decisions, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focusing on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means, then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaptation, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaptation, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.
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