Results 1 - 10
of
334
Pervasive Computing: Vision and Challenges
- IEEE Personal Communications
, 2001
"... This paper discusses the challenges in computer systems research posed by the emerging field of pervasive computing. It first examines the relationship of this new field to its predecessors: distributed systems and mobile computing. It then identifies four new research thrusts: effective use of smar ..."
Abstract
-
Cited by 686 (22 self)
- Add to MetaCart
(Show Context)
This paper discusses the challenges in computer systems research posed by the emerging field of pervasive computing. It first examines the relationship of this new field to its predecessors: distributed systems and mobile computing. It then identifies four new research thrusts: effective use of smart spaces, invisibility, localized scalability, and masking uneven conditioning. Next, it sketches a couple of hypothetical pervasive computing scenarios, and uses them to identify key capabilities missing from today's systems. The paper closes with a discussion of the research necessary to develop these capabilities.
Managing Energy and Server Resources in Hosting Centers
- In Proceedings of the 18th ACM Symposium on Operating System Principles (SOSP
, 2001
"... Interact 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 op-erating system, with an emphasis on energy as a driving resource management issue for large server cl ..."
Abstract
-
Cited by 574 (37 self)
- Add to MetaCart
Interact 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 op-erating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provi-sion server resources for co-hosted services in a way that automati-cally adapts to offered load, improve the energy efficiency of server dusters 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 func-tion of delivered performance. The system continuously moni-tors 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 avail-ability, and can reduce server energy usage by 29 % or more for a typical Web workload. 1.
Real-Time Dynamic Voltage Scaling for Low-Power Embedded Operating Systems
, 2001
"... In recent years, there has been a rapid and wide spread of nontraditional computing platforms, especially mobile and portable computing devices. As applications become increasingly sophisticated and processing power increases, the most serious limitation on these devices is the available battery lif ..."
Abstract
-
Cited by 501 (4 self)
- Add to MetaCart
(Show Context)
In recent years, there has been a rapid and wide spread of nontraditional computing platforms, especially mobile and portable computing devices. As applications become increasingly sophisticated and processing power increases, the most serious limitation on these devices is the available battery life. Dynamic Voltage Scaling (DVS) has been a key technique in exploiting the hardware characteristics of processors to reduce energy dissipation by lowering the supply voltage and operating frequency. The DVS algorithms are shown to be able to make dramatic energy savings while providing the necessary peak computation power in general-purpose systems. However, for a large class of applications in embedded real-time systems like cellular phones and camcorders, the variable operating frequency interferes with their deadline guarantee mechanisms, and DVS in this context, despite its growing importance, is largely overlooked/under-developed. To provide real-time guarantees, DVS must consider deadlines and periodicity of real-time tasks, requiring integration with the real-time scheduler. In this paper, we present a class of novel algorithms called real-time DVS (RT-DVS) that modify the OS's real-time scheduler and task management service to provide significant energy savings while maintaining real-time deadline guarantees. We show through simulations and a working prototype implementation that these RT-DVS algorithms closely approach the theoretical lower bound on energy consumption, and can easily reduce energy consumption 20% to 40% in an embedded real-time system.
Aura: an architectural framework for user mobility in ubiquitous computing environments
- In Proceedings of the 3rd Working IEEE/IFIP Conference on Software Architecture
, 2002
"... Ubiquitous computing poses a number of challenges for software architecture. One of the most important is the ability to design software systems that accommodate dynamically-changing resources. Resource variability arises naturally in a ubiquitous computing setting through user mobility (a user move ..."
Abstract
-
Cited by 248 (3 self)
- Add to MetaCart
(Show Context)
Ubiquitous computing poses a number of challenges for software architecture. One of the most important is the ability to design software systems that accommodate dynamically-changing resources. Resource variability arises naturally in a ubiquitous computing setting through user mobility (a user moves from one computing environment to another), and through the need to exploit time-varying resources in a given environment (such as wireless bandwidth). Traditional approaches to handling resource variability in applications attempt to address the problem by imposing uniformity on the environment. We argue that those approaches are inadequate, and describe an alternative architectural framework that is better matched to the needs of ubiquitous computing. A key feature of the architecture is that user tasks become first class entities. User proxies, or Auras, use models of user tasks to set up, monitor and adapt computing environments proactively. The architectural framework has been implemented and is currently being used as a central component of Project Aura, a campus-wide ubiquitous computing effort. Ubiquitous computing, mobility, architectural framework, architectural style. 1.
ECOSystem: Managing Energy as a First Class Operating System Resource
, 2002
"... Energy consumption has recently been widely recognized as a major challenge of computer systems design. This paper explores how to support energy as a first-class operating system resource. Energy, because of its global system nature, presents challenges beyond those of conventional resource managem ..."
Abstract
-
Cited by 221 (5 self)
- Add to MetaCart
(Show Context)
Energy consumption has recently been widely recognized as a major challenge of computer systems design. This paper explores how to support energy as a first-class operating system resource. Energy, because of its global system nature, presents challenges beyond those of conventional resource management. To meet these challenges we propose the Currentcy Model that unifies energy accounting over diverse hardware components and enables fair allocation of available energy among applications. Our particular goal is to extend battery lifetime by limiting the average discharge rate and to share this limited resource among competing tasks according to user preferences. To demonstrate how our framework supports explicit control over the battery resource we implemented ECOSystem, a modified Linux, that incorporates our currentcy model. Experimental results show that ECOSystem accurately accounts for the energy consumed by asynchronous device operation, can achieve a target battery lifetime, and proportionally shares the limited energy resource among competing tasks.
Load Balancing and Unbalancing for Power and Performance in Cluster-Based Systems
, 2001
"... In this paper we address power conservation for clusters of workstations or PCs. Our approach is to develop systems that dynamically turn cluster nodes on -- to be able to handle the load imposed on the system efficiently -- and off -- to save power under lighter load. The key component of our syst ..."
Abstract
-
Cited by 194 (10 self)
- Add to MetaCart
In this paper we address power conservation for clusters of workstations or PCs. Our approach is to develop systems that dynamically turn cluster nodes on -- to be able to handle the load imposed on the system efficiently -- and off -- to save power under lighter load. The key component of our systems is an algorithm that makes load balancing and unbalancing decisions by considering both the total load imposed on the cluster and the power and performance implications of turning nodes off. The algorithm is implemented in two different ways: (1) at the application level for a cluster-based, localityconscious network server; and (2) at the operating system level for an operating system for clustered cycle servers. Our experimental results are very favorable, showing that our systems conserve both power and energy in comparison to traditional systems.
Improving Dynamic Voltage Scaling Algorithms with PACE
, 2001
"... This paper addresses algorithms for dynamically varying (scaling) CPU speed and voltage in order to save energy. Such scaling is useful and effective when it is immaterial when a task completes, as long as it meets some deadline. We show how to modify any scaling algorithm to keep performance the sa ..."
Abstract
-
Cited by 174 (2 self)
- Add to MetaCart
(Show Context)
This paper addresses algorithms for dynamically varying (scaling) CPU speed and voltage in order to save energy. Such scaling is useful and effective when it is immaterial when a task completes, as long as it meets some deadline. We show how to modify any scaling algorithm to keep performance the same but minimize expected energy consumption. We refer to our approach as PACE (Processor Acceleration to Conserve Energy) since the resulting schedule increases speed as the task progresses. Since PACE depends on the probability distribution of the task's work requirement, we present methods for estimating this distribution and evaluate these methods on a variety of real workloads. We also show how to approximate the optimal schedule with one that changes speed a limited number of times. Using PACE causes very little additional overhead, and yields substantial reductions in CPU energy consumption. Simulations using real workloads show it reduces the CPU energy consumption of previously published algorithms by up to 49.5%, with an average of 20.6%, without any effect on performance.
System-Level Power Optimization: Techniques and Tools
- ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS
, 2000
"... ..."
VirtualPower: Coordinated Power Management in Virtualized Enterprise Systems
- In Proceedings of International Symposium on Operating System Principles (SOSP
, 2007
"... Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environment ..."
Abstract
-
Cited by 161 (12 self)
- Add to MetaCart
(Show Context)
Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environments. The goals of the proposed VirtualPower approach to online power management are (i) to support the isolated and independent operation assumed by guest virtual machines (VMs) running on virtualized platforms and (ii) to make it possible to control and globally coordinate the effects of the diverse power management policies applied by these VMs to virtualized resources. To attain these goals, VirtualPower extends to guest VMs ‘soft ’ versions of the hardware power states for which their policies are designed. The resulting technical challenge is to appropriately map VM-level updates made to soft power states to actual changes in the states or in the allocation of underlying virtualized hardware. An implementation of VirtualPower Management (VPM) for the Xen hypervisor addresses this challenge by provision of multiple system-level abstractions including VPM states, channels, mechanisms, and rules. Experimental evaluations on modern multicore platforms highlight resulting improvements in online power management capabilities, including minimization of power consumption with little or no performance penalties and the ability to throttle power consumption while still meeting application requirements. Finally, coordination of online methods for server consolidation with VPM management techniques in heterogeneous server systems is shown to provide up to 34% improvements in power consumption.
Energy-Efficient Server Clusters
- In Proceedings of the 2nd Workshop on Power-Aware Computing Systems
, 2002
"... This paper evaluates five policies for cluster-wide power management in server farms. The policies employ various combinations of dynamic voltage scaling and node vary-on/vary-off (VOVO) to reduce the aggregate power consumption of a server cluster during periods of reduced workload. We evaluate ..."
Abstract
-
Cited by 156 (2 self)
- Add to MetaCart
(Show Context)
This paper evaluates five policies for cluster-wide power management in server farms. The policies employ various combinations of dynamic voltage scaling and node vary-on/vary-off (VOVO) to reduce the aggregate power consumption of a server cluster during periods of reduced workload. We evaluate the policies using a validated simulator that calculates the energy usage and response times of a Web server cluster serving traces culled from real-life Web server workloads.
