اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMicro-CernVM: Slashing the Cost of Building and Deploying Virtual Machines
157
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The traditional virtual machine building and and deployment process is centered around the virtual machine hard disk image. The packages comprising the VM operating system are carefully selected, hard disk images are built for a variety of different hypervisors, and images have to be distributed and decompressed in order to instantiate a virtual machine. Within the HEP community, the CernVM File System has been established in order to decouple the distribution from the experiment software from the building and distribution of the VM hard disk images. We show how to get rid of such pre-built hard disk images altogether. Due to the high requirements on POSIX compliance imposed by HEP application software, CernVM-FS can also be used to host and boot a Linux operating system. This allows the use of a tiny bootable CD image that comprises only a Linux kernel while the rest of the operating system is provided on demand by CernVM-FS. This approach speeds up the initial instantiation time and reduces virtual machine image sizes by an order of magnitude. Furthermore, security updates can be distributed instantaneously through CernVM-FS. By leveraging the fact that CernVM-FS is a versioning file system, a historic analysis environment can be easily re-spawned by selecting the corresponding CernVM-FS file system snapshot.
قيم البحث
اقرأ أيضاً
Container technique is gaining increasing attention in recent years and has become an alternative to traditional virtual machines. Some of the primary motivations for the enterprise to adopt the container technology include its convenience to encapsu
late and deploy applications, lightweight operations, as well as efficiency and flexibility in resources sharing. However, there still lacks an in-depth and systematic comparison study on how big data applications, such as Spark jobs, perform between a container environment and a virtual machine environment. In this paper, by running various Spark applications with different configurations, we evaluate the two environments from many interesting aspects, such as how convenient the execution environment can be set up, what are makespans of different workloads running in each setup, how efficient the hardware resources, such as CPU and memory, are utilized, and how well each environment can scale. The results show that compared with virtual machines, containers provide a more easy-to-deploy and scalable environment for big data workloads. The research work in this paper can help practitioners and researchers to make more informed decisions on tuning their cloud environment and configuring the big data applications, so as to achieve better performance and higher resources utilization.
In virtualized data centers, consolidation of Virtual Machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a Cloud
Data center. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, such that the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approached called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information, therefore it is very simple to implement. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about 30% against VMWare DRS and 10-20% against EcoCloud on average.
Container technologies, like Docker, are becoming increasingly popular. Containers provide exceptional developer experience because containers offer lightweight isolation and ease of software distribution. Containers are also widely used in productio
n environments, where a different set of challenges arise such as security, networking, service discovery and load balancing. Container cluster management tools, such as Kubernetes, attempt to solve these problems by introducing a new control layer with the container as the unit of deployment. However, adding a new control layer is an extra configuration step and an additional potential source of runtime errors. The virtual machine technology offered by cloud providers is more mature and proven in terms of security, networking, service discovery and load balancing. However, virtual machines are heavier than containers for local development, are less flexible for resource allocation, and suffer longer boot times. This paper presents an alternative to containers that enjoy the best features of both approaches: (1) the use of mature, proven cloud vendor technology; (2) no need for a new control layer; and (3) as lightweight as containers. Our solution is i2kit, a deployment tool based on the immutable infrastructure pattern, where the virtual machine is the unit of deployment. The i2kit tool accepts a simplified format of Kubernetes Deployment Manifests in order to reuse Kubernetes most successful principles, but it creates a lightweight virtual machine for each Pod using Linuxkit. Linuxkit alleviates the drawback in size that using virtual machines would otherwise entail, because the footprint of Linuxkit is approximately 60MB. Finally, the attack surface of the system is reduced since Linuxkit only installs the minimum set of OS dependencies to run containers, and different Pods are isolated by hypervisor technology.
In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities. While doing so, we bring to light the successes and failures of past work in this area, challe
nges being faced in doing so, and what they have achieved. Throughout this paper, we take a problem-facing approach and describe essential factors which the success of such technologies hinges upon. We present the various aspects in a manner which clarify and lay out the different tasks involved, which can aid organizations looking to make an impact in this area. We take the example of Gondi, an extremely-low resource Indian language, to reinforce and complement our discussion.
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, an
d board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
