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

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

Large-scale Internet services require a computing infrastructure that can beappropriately described as a warehouse-sized computing system. The cost ofbuilding datacenter facilities capable of delivering a given power capacity tosuch a computer can rival the recurring energy consumption costs themselves.Therefore, there are strong economic incentives to operate facilities as closeas possible to maximum capacity, so that the non-recurring facility costs canbe best amortized. That is difficult to achieve in practice because ofuncertainties in equipment power ratings and because power consumption tends tovary significantly with the actual computing activity. Effective powerprovisioning strategies are needed to determine how much computing equipmentcan be safely and efficiently hosted within a given power budget.In this paper we present the aggregate power usage characteristics of largecollections of servers (up to 15 thousand) for different classes ofapplications over a period of approximately six months. Those observationsallow us to evaluate opportunities for maximizing the use of the deployed powercapacity of datacenters, and assess the risks of over-subscribing it. We findthat even in well-tuned applications there is a noticeable gap (7 - 16%)between achieved and theoretical aggregate peak power usage at the clusterlevel (thousands of servers). The gap grows to almost 40% in wholedatacenters. This headroom can be used to deploy additional compute equipmentwithin the same power budget with minimal risk of exceeding it. We use ourmodeling framework to estimate the potential of power management schemes toreduce peak power and energy usage. We find that the opportunities for powerand energy savings are significant, but greater at the cluster-level (thousandsof servers) than at the rack-level (tens). Finally we argue that systems needto be power efficient across the activity range, and not only at peakperformance levels.

/pdf/power-provisioning-for-a-warehouse-sized-computer-3iyo1da2iy.pdf

Power provisioning for a warehouse-sized computer

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

VMware ESX Server is a thin software layer designed to multiplex hardware resources efficiently among virtual machines running unmodified commodity operating systems. This paper introduces several novel ESX Server mechanisms and policies for managing memory. A ballooning technique reclaims the pages considered least valuable by the operating system running in a virtual machine. An idle memory tax achieves efficient memory utilization while maintaining performance isolation guarantees. Content-based page sharing and hot I/O page remapping exploit transparent page remapping to eliminate redundancy and reduce copying overheads. These techniques are combined to efficiently support virtual machine workloads that overcommit memory.

/pdf/memory-resource-management-in-vmware-esx-server-3qar0gwl8r.pdf

Memory resource management in VMware ESX server

Data center power consumption is growing to unprecedented levels: the EPA estimates U.S. data centers will consume 100 billion kilowatt hours annually by 2011. Much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. Typical idle periods though frequent--last seconds or less, confounding simple energy-conservation approaches.In this paper, we propose PowerNap, an energy-conservation approach where the entire system transitions rapidly between a high-performance active state and a near-zero-power idle state in response to instantaneous load. Rather than requiring fine-grained power-performance states and complex load-proportional operation from each system component, PowerNap instead calls for minimizing idle power and transition time, which are simpler optimization goals. Based on the PowerNap concept, we develop requirements and outline mechanisms to eliminate idle power waste in enterprise blade servers. Because PowerNap operates in low-efficiency regions of current blade center power supplies, we introduce the Redundant Array for Inexpensive Load Sharing (RAILS), a power provisioning approach that provides high conversion efficiency across the entire range of PowerNap's power demands. Using utilization traces collected from enterprise-scale commercial deployments, we demonstrate that, together, PowerNap and RAILS reduce average server power consumption by 74%.

/pdf/powernap-eliminating-server-idle-power-1h9okdlk0a.pdf

PowerNap: eliminating server idle power

Internet hosting centers serve multiple service sites from a common hardware base. This paper presents the design and implementation of an architecture for resource management in a hosting center operating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provision server resources for co-hosted services in a way that automatically adapts to offered load, improve the energy efficiency of server clusters by dynamically resizing the active server set, and respond to power supply disruptions or thermal events by degrading service in accordance with negotiated Service Level Agreements (SLAs).Our system is based on an economic approach to managing shared server resources, in which services "bid" for resources as a function of delivered performance. The system continuously monitors load and plans resource allotments by estimating the value of their effects on service performance. A greedy resource allocation algorithm adjusts resource prices to balance supply and demand, allocating resources to their most efficient use. A reconfigurable server switching infrastructure directs request traffic to the servers assigned to each service. Experimental results from a prototype confirm that the system adapts to offered load and resource availability, and can reduce server energy usage by 29% or more for a typical Web workload.

/pdf/managing-energy-and-server-resources-in-hosting-centers-2zu02bqt14.pdf

Managing energy and server resources in hosting centers

Internet service utilities host multiple server applications on a shared server cluster. A key challenge for these systems is to provision shared resources on demand to meet service quality targets at least cost. This paper presents a new approach to utility resource management focusing on coordinated provisioning of memory and storage resources. Our approach is model-based: it incorporates internal models of service behavior to predict the value of candidate resource allotments under changing load. The model-based approach enables the system to achieve important resource management goals, including differentiated service quality, performance isolation, storage-aware caching, and proactive allocation of surplus resources to meet performance goals. Experimental results with a prototype demonstrate model-based dynamic provisioning under Web workloads with static content.

Model-based resource provisioning in a web service utility

Methods, systems, computer program products, and methods of doing business whereby document components are secured or controlled using “security containers” which encapsulate the components (and other component metadata). A “security container” encapsulates the component (i.e., content) that is to be controlled within a higher-level construct such as a compound document. The security container also contains rules for interacting with the encapsulated component, and one or more encryption keys usable for decrypting the component and rules for authorized requesters.

Security containers for document components

Methods, systems and computer program products are provided for determining if a packet has a spoofed source Internet Protocol (IP) address. A source media access control (MAC) address of the packet and the source IP address are evaluated to determine if the source IP address of the packet has been bound to the source MAC address at a source device of the packet. The packet is determined to have a spoofed source IP address if the evaluation indicates that the source IP address is not bound to the source MAC address. Such an evaluation may be made for packets having a subnet of the source IP address which matches a subnet from which the packet originated.

Methods, systems and computer program products for detecting a spoofed source address in IP datagrams

A method, system, computer program product, and method of doing business by improving the security of transactions performed using smart cards, and also a card with an integrated biometric sensor. The disclosed techniques also improve the security of transactions carried out with devices such as personal or “pervasive computing” devices. Biometric sensors are used for obtaining identifying information from users of smart cards. In one aspect, this may be done by securely attaching a smart card reader and a biometric sensor to a security core which authenticates these devices using public key infrastructure techniques, and which securely controls information exchanged with these devices. In another aspect, this is done by integrating a biometric sensor with a smart card.

Ronald P. Doyle

Papers

Managing energy and server resources in hosting centers

Model-based resource provisioning in a web service utility

Security containers for document components

Methods, systems and computer program products for detecting a spoofed source address in IP datagrams

Smart card with integrated biometric sensor