CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limited resources among VMs. Many techniques such as VM scheduling and resource provisioning are proposed to maximize the cost-efficiency of the computing servers while alleviating the performance inference between VMs. However, these management techniques require accurate performance prediction of the application running inside the VM, which is challenging to get in the public cloud due to the black-box nature of the VMs. From this perspective, this paper proposes a novel machine learning-based performance prediction approach for applications running in the cloud. To achieve high accuracy predictions for black-box VMs, the proposed method first identifies the running application inside the virtual machine. It then selects highly-correlated runtime metrics as the input of the machine learning approach to accurately predict the performance level of the cloud application. Experimental results with state-of-the-art cloud benchmarks demonstrate that our proposed method outperforms the existing prediction methods by more than 2x in terms of worst prediction error. In addition, we successfully tackle the challenge in performance prediction for applications with variable workloads by introducing the performance degradation index, which other comparison methods fail to consider. The workflow versatility of the proposed approach has been verified with different modern servers and VM configurations.Comment: 15 pages, 11 figures, summited to IEEE Transactions on Sustainable Computin

Study of the Epidemiological Features and Clinical Manifestations of the Preceding Epidemic of Influenza A (H1N1) as a Guide for Dealing With the 2015 Outbreak in the Qazvin Province, Iran

Background: In 2009, a pandemic associated with a new type of influenza A virus (H1N1) affected many countries worldwide. After five years of silence, in 2015 we encountered another outbreak of H1N1 influenza A. Objectives: The present study aimed to study the epidemiological and clinical features of this disease in the cold and dry climate of Qazvin province, Iran in the last epidemic, during 2009. Patients and Methods: This was a cross-sectional study in which the demographic characteristics and clinical manifestations of confirmed cases of influenza A virus (H1N1) in the province of Qazvin were investigated. The definite diagnosis of cases was performed using real time Polymerase Chain Reaction (PCR) on oropharyngeal washing specimens from adults and throat swabs from children and severely ill patients. Results: During the time course between July to December 2009, 76 confirmed cases of influenza A (H1N1) were discovered in the province of Qazvin. The mean age of patients was 25.67 ± 16.9 years. The most affected people were students and housewives. Coughing was found to be the most common clinical symptom (96.1%) followed by fever (92.1%), myalgia (48.5%), and diarrhea and vomiting (34.2%). In laboratory confirmed patients, 62 were hospitalized and two cases deceased. Regarding the total population of the Qazvin province (1,100,000), the rate of hospitalization was calculated at 5.42 per 100,000 individuals, with a mortality rate of 0.175 per 100,000 individuals (3.2% of hospitalized cases). Conclusions: Concerning the higher prevalence of disease in younger age groups, and more severe disease in high-risk groups, including overweight patients and pregnant women, the authors recommend special attention to clinical symptoms such as diarrhea and vomiting, cough, myalgia and fever in patients with cold symptoms. Also, for severely ill patients, the allocation of adequate intensive care units should be of prime importance. Keywords: Influenza A Virus, H1N1 Subtype; Comorbidity; Epidemiolog

The mediating role of meta-cognitive beliefs between alexithymia and chronic pain intensity

 Aims and background: chronic pain isn’t always psychosomatic. Chronic pain, is a disorder that has a lot of psychological components and onethat a lot of people have at some point in  their life. The aim of this study was to determine the role meta-cognitive beliefs play in mediating between alexithymia and the intensity of pain that is perceived percipience by the patients with chronic pain. Materials and Methods: This study evaluated patients aged 20-60 with chronic pain who had been referred to the Mahan clinic and the physical medicine and rehabilitation clinic of Arman in Tehran from the spring of 1396 to autumn of 1396. During this time frame 440 patients who had at least 3 months of musculoskeletal pain, were chosen.  Theyanswered the Toronto Alexithymia Scale (TAS-20) the Meta-cognition Questionnaire (MCQ-30), and the Numeric Rating Scale (NRS). Findings: The intensity of pain was coorelated positively with with alexithymia (p< 0.001) and meta-cognitive beliefs (p< 0.001). Alexithymia had a positive coorelationwith meta-cognitive beliefs (p< 0.001). Alexithymia (t=6.68, β= 0.29), and meta-cognitive beliefs (t= 2.42, β= 0.11) could clarify the variance of the pain intensity. Alexithymia could also clarify the meta-cognitive beliefs (t= 9.48, β= 0.40). Conclusion: Based on the findings, the relation between alexithymia and the intensity of pain, was not a simple linear relationship, but meta-cognitive beliefs, could affect this relationship

Enhancing Two-Phase Cooling Efficiency through Thermal- Aware Workload Mapping for Power-Hungry Servers

The power density and, consequently, power hungriness of server processors is growing by the day. Traditional air cooling systems fail to cope with such high heat densities, whereas single-phase liquid-cooling still requires high mass flowrate, high pumping power, and large facility size. On the contrary, in a micro-scale gravity-driven thermosyphon attached on top of a processor, the refrigerant, absorbing the heat, turns into a two-phase mixture. The vapor-liquid mixture exchanges heat with a coolant at the condenser side, turns back to liquid state, and descends thanks to gravity, eliminating the need for pumping power. However, similar to other cooling technologies, thermosyphon efficiency can considerably vary with respect to workload performance requirements and thermal profile, in addition to the platform features, such as packaging and die floorplan. In this work, we first address the workload- and platform-aware design of a two-phase thermosyphon. Then, we propose a thermal-aware workload mapping strategy considering the potential and limitations of a two-phase thermosyphon to further minimize hot spots and spatial thermal gradients. Our experiments, performed on an 8-core Intel Xeon E5 CPU reveal, on average, up to 10ºC reduction in thermal hot spots, and 45% reduction in the maximum spatial thermal gradient on the die. Moreover, our design and mapping strategy are able to decrease the chiller cooling power at least by 45%

Energy Proportionality in Near-Threshold Computing Servers and Cloud Data Centers: Consolidating or Not?

Cloud Computing aims to efficiently tackle the increasing demand of computing resources, and its popularity has led to a dramatic increase in the number of computing servers and data centers worldwide. However, as effect of post-Dennard scaling, computing servers have become power-limited, and new system-level approaches must be used to improve their energy efficiency. This paper first presents an accurate power modelling characterization for a new server architecture based on the FD-SOI process technology for near-threshold computing (NTC). Then, we explore the existing energy vs. performance trade-offs when virtualized applications with different CPU utilization and memory footprint characteristics are executed. Finally, based on this analysis, we propose a novel dynamic virtual machine (VM) allocation method that exploits the knowledge of VMs characteristics together with our accurate server power model for next-generation NTC-based data centers, while guaranteeing quality of service (QoS) requirements. Our results demonstrate the inefficiency of current workload consolidation techniques for new NTC-based data center designs, and how our proposed method provides up to 45% energy savings when compared to state-of-the-art consolidation-based approaches

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15% compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86% and 90%, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7% time overhead to execute for a data center with 1500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload

Towards near-threshold server processors

The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques such as near-threshold computing (NTC) can be used to improve energy efficiency in the post-Dennard scaling era. This paper describes an architecture based on the FD-SOI process technology for near-threshold operation in servers. Our work explores the trade-offs in energy and performance when running a wide range of applications found in private and public clouds, ranging from traditional scale-out applications, such as web search or media streaming, to virtualized banking applications. Our study demonstrates the benefits of near-threshold operation and proposes several directions to synergistically increase the energy proportionality of a near-threshold server

Online Efficient Bio-Medical Video Transcoding on MPSoCs Through Content-Aware Workload Allocation

Bio-medical image processing in the field of telemedicine, and in particular the definition of systems that allow medical diagnostics in a collaborative and distributed way is experiencing an undeniable growth. Due to the high quality of bio-medical videos and the subsequent large volumes of data generated, to enable medical diagnosis on-the-go it is imperative to efficiently transcode and stream the stored videos on real time, without quality loss. However, online video transcoding is a high-demanding computationally-intensive task and its efficient management in Multiprocessor Systems-on-Chip (MPSoCs) poses an important challenge. In this work, we propose an efficient motion- and texture-aware frame-level parallelization approach to enable online medical imaging transcoding on MPSoCs for next generation video encoders. By exploiting the unique characteristics of bio-medical videos and the medical procedure that enable diagnosis, we split frames into tiles based on their motion and texture, deciding the most adequate level of parallelization. Then, we employ the available encoding parameters to satisfy the required video quality and compression. Moreover, we propose a new fast motion search algorithm for bio-medical videos that allows to drastically reduce the computational complexity of the encoder, thus achieving the frame rates required for online transcoding. Finally, we heuristically allocate the threads to the most appropriate available resources and set the operating frequency of each one. We evaluate our work on an enterprise multicore server achieving online medical imaging with 1.6x higher throughput and 44% less power consumption when compared to the state-of-the-art techniques

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries