Towards a Performance Interference-aware Virtual Machine Placement Strategy for Supporting Soft Real-time Applications in the Cloud

REACTION 2014. 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications. Rome, Italy. December 2nd, 2014.It is standard practice for cloud service providers (CSPs) to overbook physical system resources to maximize the resource utilization and make their business model more profitable. Resource overbooking can lead to performance interference, however, among the virtual machines (VMs) hosted on the physical resources causing performance un-predictability for soft real-time applications hosted in the VMs, which is unacceptable to these applications. Balancing these conflicting requirements needs a careful design of the placement strategies for hosting soft real-time applications such that the performance interference effects are minimized while still allowing resource overbooking. These placement decisions cannot be made offline because workloads change at run time. Moreover, satisfying the priorities of collocated VMs may require VM migrations, which require an online solution. This paper presents a machine learning-based, online placement solution to this problem where the system is trained using a publicly available trace of a large data center owned by Google. Our approach first classifies the VMs based on their historic mean CPU and memory usage, and performance features. Subsequently, it learns the best patterns of collocating the classified VMs by employing machine learning techniques. These extracted patterns are those that provide the lowest performance interference level on the specified host machines making them amenable to hosting soft real-time applications while still allowing resource overbooking.This work was supported in part by the National Science Foundation CAREER CNS 0845789 and AFOSR DDDAS FA9550-13-1-0227.Publicad

A Framework for Effective Placement of Virtual Machine Replicas for Highly Available Performance-sensitive Cloud-based Applications

REACTION 2012. 1st International workshop on Real-time and distributed computing in emerging applications. December 4th, 2012, San Juan, Puerto Rico.Applications are increasingly being deployed in the Cloud due to benefits stemming from economy of scale, scalability, flexibility and utility-based pricing model. Although most cloud-based applications have hitherto been enterprisestyle, there is a new trend towards hosting performancesensitive applications in the cloud that demand both high availability and good response times. In the current stateof- the-art in cloud computing research, there does not exist solutions that provide both high availability and acceptable response times to these applications in a way that also optimizes resource consumption in data centers, which is a key consideration for cloud providers. This paper addresses this dual challenge by presenting a design of a fault-tolerant framework for virtualized data centers that makes two important contributions. First, it describes an architecture of a fault-tolerance framework that can be used to automatically deploy replicas of virtual machines in data centers in a way that optimizes resources while assures availability and responsiveness. Second, it describes a specific formulation of a replica deployment combinatorial optimization problem that can be plugged into our strategizable deployment framework.This work was supported in part by the National Science Foundation NSF SHF/CNS Award CNS 0915976 and NSF CAREER CNS 0845789. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation

Serum Presepsin Levels Are Not Elevated in Patients with Controlled Hypertension

Introduction. Hypertension (HT) is a common serious condition associated with cardiovascular morbidity and mortality. The pathogenesis of HT is multifactorial and has been widely investigated. Besides the vascular, hormonal, and neurological factors, inflammation plays a crucial role in HT. Many inflammatory markers such as C-reactive protein, cytokines, and adhesion molecules have been studied in HT, which supported the role of inflammation in the pathogenesis of HT. Presepsin (PSP) is a novel biomarker of inflammation. Therefore, the potential relationship between PSP and HT was investigated in this study. Methods. Forty-eight patients with controlled HT and 48 controls without HT were included in our study. Besides routine clinical and laboratory data, PSP levels were measured in peripheral venous blood samples from all the participants. Results. PSP levels were significantly lower in patients with HT than in controls (144.98±75.98 versus 176.67±48.12 pg/mL, p=0.011). PSP levels were positively correlated with hsCRP among both the patient and the control groups (p=0.015 and p=0.009, resp.). However, PSP levels were not correlated with WBC among both groups (p=0.09 and p=0.67, resp.). Conclusions. PSP levels are not elevated in patients with well-controlled HT compared to controls. This result may be associated with anti-inflammatory effects of antihypertensive medicines

Development of an autonomous mobile robot outdoor navigation system

Navigation is a fundamental capability that mobile robots must possess. Outdoor navigation is especially challenging due to numerous uncertainties and dynamic changes in the environment such as varying lightning conditions. A robust outdoor navigation system utilizing navigational sensors such as GPS, vision, laser, and sonar improves autonomy of robots and their task handling capabilities. This thesis focuses on the development and testing of an outdoor navigation system for mobile robots that improves autonomy. This navigation system enables a mobile robot equipped with GPS, laser and sonar range finders, and vision system to navigate from a known initial position to a given target location in an outdoor environment. Waypoints that the robot will track are created in XML format by waypoints generator web interface and are given to robot prior to starting movement. The real-time tracking web interface continuously displays the position of the robot on the map by utilizing the GPS readings. An artificial neural network was designed and developed for detecting roads. Real-time computer vision algorithms were developed for online path planning and obstacle avoidance. The developed system was also tested for road detection success rate and eighty seven (87%) percent of successful road center detection rate was accomplished. The system has been successfully implemented and tested using a Pioneer 3-AT mobile robot in outdoor environments

iOverbook: Intelligent Resource-Overbooking to Support Soft Real-Time Applications in the Cloud

Abstract—Cloud service providers (CSPs) often overbook their resources with user applications despite having to maintain service-level agreements with their customers. Overbooking is attractive to CSPs because it helps to reduce power consumption in the data center by packing more user jobs in less number of resources while improving their profits. Overbooking becomes feasible because user applications tend to overestimate their resource requirements utilizing only a fraction of the allocated resources. Arbitrary resource overbooking ratios, however, may be detrimental to soft real-time applications, such as airline reservations or Netflix video streaming, which are increasingly hosted in the cloud. The changing dynamics of the cloud preclude an offline determination of overbooking ratios. To address these concerns, this paper presents iOverbook, which uses a machine learning approach to make systematic and online determination of overbooking ratios such that the quality of service needs of soft real-time systems can be met while still benefiting from overbooking. Specifically, iOverbook utilizes historic data of tasks and host machines in the cloud to extract their resource usage patterns and predict future resource usage along with the expected mean performance of host machines. To evaluate our approach, we have used a large usage trace made available by Google of one of its production data centers. In the context of the traces, our experiments show that iOverbook can help CSPs improve their resource utilization by an average of 12.5 % and save 32 % power in the data center. Keywords-resource overbooking, cloud computing, soft real-time performance. I

A Publish/Subscribe Middleware for Dependable and Real-time Resource Monitoring in the Cloud ∗

Providing scalable and QoS-enabled (i.e., real-time and reliable) monitoring of resources (both virtual and physical) in the cloud is essential to supporting application QoS properties in the cloud as well as identifying security threats. Existing approaches to resource monitoring in the cloud are based on web interfaces, such as RESTful APIs and SOAP, which cannot provide real-time information efficiently and scalably because of a lack of support for fine-grained and differentiated monitoring capabilities. Moreover, their implementation overhead results in a distinct loss in performance, incurs latency jitter, and degrades reliable delivery of time-sensitive information. To address these challenges this paper presents a novel lighter weight and scalable resource monitoring and dissemination solution based on the publish/subscribe (pub/sub) paradigm. Our solution called SQRT-C leverages the OMG Data Distribution Service (DDS) real-time pub/sub middleware, and uses effective software engineering principles to make it usable with multiple cloud platforms. Preliminary empirical results comparing SQRT-C with contemporary web-based resource usage monitoring services reveals that SQRT-C is significantly better than the conventional approaches in terms of latency, jitter and scalability

A Framework for Effective Placement of Virtual Machine Replicas for Highly Available Performance-sensitive Cloud-based Applications

Abstract-Applications are increasingly being deployed in the Cloud due to benefits stemming from economy of scale, scalability, flexibility and utility-based pricing model. Although most cloud-based applications have hitherto been enterprisestyle, there is a new trend towards hosting performancesensitive applications in the cloud that demand both high availability and good response times. In the current stateof-the-art in cloud computing research, there does not exist solutions that provide both high availability and acceptable response times to these applications in a way that also optimizes resource consumption in data centers, which is a key consideration for cloud providers. This paper addresses this dual challenge by presenting a design of a fault-tolerant framework for virtualized data centers that makes two important contributions. First, it describes an architecture of a fault-tolerance framework that can be used to automatically deploy replicas of virtual machines in data centers in a way that optimizes resources while assures availability and responsiveness. Second, it describes a specific formulation of a replica deployment combinatorial optimization problem that can be plugged into our strategizable deployment framework

Transitioning to the cloud?: a model-driven analysis and automated deployment capability for cloud services

ABSTRACT As cloud computing becomes increasingly popular and appealing, application and service providers increasingly face questions on whether moving to the cloud would be beneficial to their business, and how should the cloud deployment of their application be realized. Analysis techniques, such as simulations, hold promise in analyzing the benefits of moving to the cloud, and while generative mechanisms can automate the deployment of applications in the cloud. This paper describes how model-driven engineering (MDE) supports both these desired capabilities by providing intuitive and automated capabilities for driving simulations of cloud infrastructures and application services to analyze the benefits of moving the applications to the cloud, and automating the deployment of these applications in the cloud