As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

/pdf/context-aware-computing-for-the-internet-of-things-a-survey-3nibvvc921.pdf

Context Aware Computing for The Internet of Things: A Survey

https://beloglazov.info/papers/2011-energy-aware-fgcs.pdf

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers. These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers. Large portions of the hardware and software resources in these facilities must work in concert to efficiently deliver good levels of Internet service performance, something that can only be achieved by a holistic approach to their design and deployment. In other words, we must treat the datacenter itself as one massive warehouse-scale computer (WSC). We describe the architecture of WSCs, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. We hope it will be useful to architects and programmers of today's WSCs, as well as those of future many-core platforms which may one day implement the equivalent of today's WSCs on a single board. Table of Contents: Introduction / Workloads and Software Infrastructure / Hardware Building Blocks / Datacenter Basics / Energy and Power Efficiency / Modeling Costs / Dealing with Failures and Repairs / Closing Remarks

/pdf/the-datacenter-as-a-computer-an-introduction-to-the-design-nu43k2m3iz.pdf

The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

Examining the association between parenting and childhood anxiety: a meta-analysis.

Networks are a shared resource connecting critical IT infrastructure, and the general practice is to always leave them on. Yet, meaningful energy savings can result from improving a network's ability to scale up and down, as traffic demands ebb and flow. We present ElasticTree, a network-wide power1 manager, which dynamically adjusts the set of active network elements -- links and switches--to satisfy changing data center traffic loads.We first compare multiple strategies for finding minimum-power network subsets across a range of traffic patterns. We implement and analyze ElasticTree on a prototype testbed built with production OpenFlow switches from three network vendors. Further, we examine the trade-offs between energy efficiency, performance and robustness, with real traces from a production e-commerce website. Our results demonstrate that for data center workloads, ElasticTree can save up to 50% of network energy, while maintaining the ability to handle traffic surges. Our fast heuristic for computing network subsets enables ElasticTree to scale to data centers containing thousands of nodes. We finish by showing how a network admin might configure ElasticTree to satisfy their needs for performance and fault tolerance, while minimizing their network power bill.

/pdf/elastictree-saving-energy-in-data-center-networks-2w53iv2yv5.pdf

ElasticTree: saving energy in data center networks

Internet-based applications and their resulting multitier distributed architectures have changed the focus of design for large-scale Internet computing. Internet server applications execute in a horizontally scalable topology across hundreds or thousands of commodity servers in Internet data centers. Increasing scale and power density significantly impacts the data center's thermal properties. Effective thermal management is essential to the robustness of mission-critical applications. Internet service architectures can address multisystem resource management as well as thermal management within data centers.

http://www.hpl.hp.com/techreports/2003/HPL-2003-5.pdf

Balance of power: dynamic thermal management for Internet data centers

A cooling system is configured to adjust cooling fluid flow to various racks located throughout a data center based upon the detected or anticipated temperatures at various locations throughout the data center. In one respect, by substantially increasing the cooling fluid flow to those racks dissipating greater amounts of heat and by substantially decreasing the cooling fluid flow to those racks dissipating lesser amounts of heat, the amount of energy required to operate the cooling system may be relatively reduced. Thus, instead of operating the devices, e.g., compressors, fans, etc., of the cooling system at substantially 100 percent of the anticipated heat dissipation from the racks, those devices may be operated according to the actual cooling needs. In addition, the racks may be positioned throughout the data center according to their anticipated heat loads to thereby enable computer room air conditioning (CRAC) units located at various positions throughout the data center to operate in a more efficient manner. In one respect, the positioning of the racks may be determined through implementation of numerical modeling and metrology of the cooling fluid flow throughout the data center. In addition, the numerical modeling may be implemented to control the volume flow rate and velocity of the cooling fluid flow through each of the vents.

Smart cooling of data centers

Recently, the demand for data center computing has surged, increasing the total energy footprint of data centers worldwide. Data centers typically comprise three subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastructure removes heat generated by these subsystems. This work presents a novel approach to model the energy flows in a data center and optimize its operation. Traditionally, supply-side constraints such as energy or cooling availability were treated independently from IT workload management. This work reduces electricity cost and environmental impact using a holistic approach that integrates renewable supply, dynamic pricing, and cooling supply including chiller and outside air cooling, with IT workload planning to improve the overall sustainability of data center operations. Specifically, we first predict renewable energy as well as IT demand. Then we use these predictions to generate an IT workload management plan that schedules IT workload and allocates IT resources within a data center according to time varying power supply and cooling efficiency. We have implemented and evaluated our approach using traces from real data centers and production systems. The results demonstrate that our approach can reduce both the recurring power costs and the use of non-renewable energy by as much as 60% compared to existing techniques, while still meeting the Service Level Agreements.

/pdf/renewable-and-cooling-aware-workload-management-for-21f3o272ec.pdf

Renewable and cooling aware workload management for sustainable data centers

In a cooling system for cooling racks in a data center, cooling fluid is circulated in the data center by a cooling device having a fan. In addition, this system includes a plenum having a plurality of returns and an outlet. The outlet of the plenum is in fluid communication with the fan and the plurality of returns are configured for removing the cooling fluid from the data center. Furthermore, the returns and are operable to vary a characteristic of the removal of the cooling fluid.

Cooling of data centers

A high compute density data center of today is characterized as one consisting of thousands of racks each with multiple computing units. The computing units include multiple microprocessors, each dissipating approximately 250 W of power. The heat dissipation from a rack containing such computing units exceeds 10 KW. Today's data center, with 1000 racks, over 30,000 square feet, requires 10 MW of power for the computing infrastructure. A 100,000 square foot data center of tomorrow will require 50 MW of power for the computing infrastructure. Energy required to dissipate this heat will be an additional 20 MW. A hundred thousand square foot planetary scale data center, with five thousand 10 KW racks, would cost /spl sim/$44 million per year (@ $100/MWh) just to power the servers & $18 million per year to power the cooling infrastructure for the data center. Cooling design considerations by virtue of proper layout of racks can yield substantial savings in energy. This paper shows an overview of a data center cooling design and presents the results of a case study where layout change was made by virtue of numerical modeling to avail efficient use of air conditioning resources.

https://www.hpl.hp.com/research/papers/2002/thermal_may02.pdf

Cullen E. Bash

Papers

Balance of power: dynamic thermal management for Internet data centers

Smart cooling of data centers

Renewable and cooling aware workload management for sustainable data centers

Cooling of data centers

Thermal considerations in cooling large scale high compute density data centers