National Institute of Standards and Technology における超伝導研究及び生活

Cloud computing services are becoming ubiquitous, and are starting to serve as the primary source of computing power for both enterprises and personal computing applications. We consider a stochastic model of a cloud computing cluster, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are specified in terms of resources such as CPU, memory and storage space. While there are many design issues associated with such systems, here we focus only on resource allocation problems, such as the design of algorithms for load balancing among servers, and algorithms for scheduling VM configurations. Given our model of a cloud, we first define its capacity, i.e., the maximum rates at which jobs can be processed in such a system. Then, we show that the widely-used Best-Fit scheduling algorithm is not throughput-optimal, and present alternatives which achieve any arbitrary fraction of the capacity region of the cloud. We then study the delay performance of these alternative algorithms through simulations.

/pdf/stochastic-models-of-load-balancing-and-scheduling-in-cloud-2skmuqplr2.pdf

Stochastic models of load balancing and scheduling in cloud computing clusters

http://romisatriawahono.net/lecture/rm/survey/network%20security/Fatema%20-%20Cloud%20Monitoring%20Tools%20-%202014.pdf

A survey of Cloud monitoring tools: Taxonomy, capabilities and objectives

The cloud-computing paradigm offers on-demand services over the Internet and supports a wide variety of applications. With the recent growth of Internet of Things (IoT)--based applications, the use of cloud services is increasing exponentially. The next generation of cloud computing must be energy efficient and sustainable to fulfill end-user requirements, which are changing dynamically. Presently, cloud providers are facing challenges to ensure the energy efficiency and sustainability of their services. The use of a large number of cloud datacenters increases cost as well as carbon footprints, which further affects the sustainability of cloud services. In this article, we propose a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used to investigate the existing techniques for sustainability that need careful attention and investigation as proposed by several academic and industry groups. The current research on sustainable cloud computing is organized into several categories: application design, sustainability metrics, capacity planning, energy management, virtualization, thermal-aware scheduling, cooling management, renewable energy, and waste heat utilization. The existing techniques have been compared and categorized based on common characteristics and properties. A conceptual model for sustainable cloud computing has been presented along with a discussion on future research directions.

A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View

Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit.

Heavy Traffic Optimal Resource Allocation Algorithms for Cloud Computing Clusters

Cloud computing is based on the notion of shared computational, storage, network, and application resources provided by a third party. This paper explores in detail the concept of cloud computing, its advantages and disadvantages and describes several existing cloud computing platforms. It then discusses the results of quantitative experiments carried out using PlanetLab, a cloud computing platform. The paper also discusses how the methods of capacity planning are impacted by the advent of cloud computing from the point of view of the cloud user and from the cloud provider.

/pdf/understanding-cloud-computing-experimentation-and-capacity-2rtw1q28lg.pdf

Understanding Cloud Computing: Experimentation and Capacity Planning

Ensuring effective communications during emergencies is an important issue for any functional government. One way to address this issue is to ensure the availability of the key personnel capable of making the appropriate decisions and taking timely actions with sufficient resources. Many XML-based languages such as the Emergency Data Exchange Language (EDXL) and associated Common Alert Protocol (CAP) have been designed to provide a basis for such communications. To ensure that messages are delivered in a timely manner, we propose some role and task based ontological enhancements for these languages. We show by example how the ontological enhancements can be used to enhance availability of emergency personnel in case of a need.

http://ceur-ws.org/Vol-713/STIDS_R5_NgoWijesekera.pdf

Using Ontological Information to Enhance Responder Availability in Emergency Response

The U.S. Department of Homeland Security initiated the Commercial Mobile Alert System (CMAS) to inform the general public of emergencies. CMAS utilizes the commercial telecommunications infrastructure to broadcast emergency alert text messages to mobile users in an area affected by an emergency. Because CMAS uses cell broadcast service, the smallest area that CMAS can broadcast messages is a cell site, which is usually quite large for local emergencies. This paper proposes an enhancement that uses CMAS as a transport protocol to distribute local emergency alerts to areas smaller than a cell site. The paper also conducts an investigation of the Common Alerting Protocol (CAP), the current emergency protocol standard, and suggests an enhancement to the CAP message structure for CMAS emergency alerts. The viability of the approach is demonstrated using a prototype implementation, which simulates broadcasts of emergency alerts to confined areas such as a city block or an apartment complex.

/pdf/enhancing-the-usability-of-the-commercial-mobile-alert-3w4wqcoo7x.pdf

Enhancing the Usability of the Commercial Mobile Alert System

: Controller Area Network (CAN) bus data is used on most vehicles today to report and communicate sensor data. However, this data is generally encoded and is not directly interpretable by simply viewing the raw data on the bus. However, it is possible to decode CAN bus data and reverse engineer the encodings by leveraging knowledge about how signals are encoded and using independently recorded ground-truth signal values for correlation. While methods exist to support the decoding of possible signals, these methods often require additional manual work to label the function of each signal. In this paper, we present CANClassify — a method that takes in raw CAN bus data, and automatically decodes and labels CAN bus signals, using a novel convolutional interpretation method to preprocess CAN messages. We evaluate CANClassify’s performance on a previously undecoded vehicle and confirm the encodings manually. We demonstrate performance comparable to the state of the art while also providing automated labeling. Examples and code are available at https://github.com/ngopaul/CANClassify .

CANClassify: Automated Decoding and Labeling of CAN Bus Signals

Emergencies occur every day at unexpected times and impact our lives in unimaginable ways. In any emergency situation, there are two type of victims: direct victims and indirect victims. Both will have their current plans disrupted in order to deal with the emergency. Federal, State, and Local governments have established a 911 system to assist direct victims. However, there is still lack of assistance provided to the indirect victims. In this paper, we propose a Navigation Assistance Framework that allows emergency organizations to provide emergency information that can assist victims navigating out of the emergency area and reaching their intended destinations in a reasonable amount of time. We develop an emergency prototype ERSimMon to simulate this capability in a small scale to show the effectiveness of the proposed solution. In addition, we develop the Emergency Response Application (ERApp) for a smart phone platform, which intercepts the enhanced Commercial Mobile Alert System (CMAS) broadcast message, displays the user’s location with respect to the emergency location on the map and provides navigational assistance and recommend actions to help the user navigate out of ongoing emergencies.

/pdf/navigation-assistance-framework-for-emergencies-3obt8ayjch.pdf

Paul Ngo

Papers

Understanding Cloud Computing: Experimentation and Capacity Planning

Using Ontological Information to Enhance Responder Availability in Emergency Response

Enhancing the Usability of the Commercial Mobile Alert System

CANClassify: Automated Decoding and Labeling of CAN Bus Signals

Navigation Assistance Framework for Emergencies.