10,905 research outputs found
E2DR: Energy Efficient Data Replication in Data Grid
Abstractâ Data grids are an important branch of gird computing which provide mechanisms for the management of large volumes of distributed data. Energy efficiency has recently emerged as a hot topic in large distributed systems. The development of computing systems is traditionally focused on performance improvements driven by the demand of client's applications in scientific and business domains. High energy consumption in computer systems leads to their limited performance because of the increased consumption of carbon dioxide and amount of electricity bills. Thus, the goal of design of computer systems has been shifted to power and energy efficiency. Data grids can solve large scale applications that require a large amount of data. Data replication is a common solution to improve availability and file access time in such environments. This solution replicates the data file in many different sites. In this paper, a new data replication method is proposed that is not only data aware, but also is energy efficient. Simulation results with CLOUDSIM show that the proposed method gives better energy consumption, average response time, and network usage than other algorithms and prevents the unnecessary creation of replica, which leads to efficient storage usage
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints
We consider allocation problems that arise in the context of service
allocation in Clouds. More specifically, we assume on the one part that each
computing resource is associated to a capacity constraint, that can be chosen
using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability
of failure. On the other hand, we assume that the service runs as a set of
independent instances of identical Virtual Machines. Moreover, there exists a
Service Level Agreement (SLA) between the Cloud provider and the client that
can be expressed as follows: the client comes with a minimal number of service
instances which must be alive at the end of the day, and the Cloud provider
offers a list of pairs (price,compensation), this compensation being paid by
the Cloud provider if it fails to keep alive the required number of services.
On the Cloud provider side, each pair corresponds actually to a guaranteed
success probability of fulfilling the constraint on the minimal number of
instances. In this context, given a minimal number of instances and a
probability of success, the question for the Cloud provider is to find the
number of necessary resources, their clock frequency and an allocation of the
instances (possibly using replication) onto machines. This solution should
satisfy all types of constraints during a given time period while minimizing
the energy consumption of used resources. We consider two energy consumption
models based on DVFS techniques, where the clock frequency of physical
resources can be changed. For each allocation problem and each energy model, we
prove deterministic approximation ratios on the consumed energy for algorithms
that provide guaranteed probability failures, as well as an efficient
heuristic, whose energy ratio is not guaranteed
Smart-Pixel Cellular Neural Networks in Analog Current-Mode CMOS Technology
This paper presents a systematic approach to design CMOS chips with concurrent picture acquisition and processing capabilities. These chips consist of regular arrangements of elementary units, called smart pixels. Light detection is made with vertical CMOS-BJTâs connected in a Darlington structure. Pixel smartness is achieved by exploiting the Cellular Neural Network paradigm [1], [2], incorporating at each pixel location an analog computing cell which interacts with those of nearby pixels. We propose a current-mode implementation technique and give measurements from two 16 x 16 prototypes in a single-poly double-metal CMOS n-well 1.6-”m technology. In addition to the sensory and processing circuitry, both chips incorporate light-adaptation circuitry for automatic contrast adjustment. They obtain smart-pixel densities up to 89 units/mm2, with a power consumption down to 105 ”W/unit and image processing times below 2 ”s
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A classification of emerging and traditional grid systems
The grid has evolved in numerous distinct phases. It started in the early â90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late â90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to âglueâ different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings.
However, third-generation gridsâ current architecture doesnât meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systemsâ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area
Unlocking the potential of the smart metering technology: How can regulation level the playing-field for new services in smart grids?
By integrating a communications system with the existing power grid, smart grids provide end-to-end connectivity. This enables all entities and components integrated in the electricity supply system to exchange information without knowing the network's structure. New services and applications such as demand response or virtual power plants that will aid to improve and optimize the use of electricity depend on the availability of a smart grid communication network. End-to-end communication networks require that the missing communications gap between consumers' premises and the remaining energy network is bridged by deploying an Advanced Metering Infrastructure (AMI). Given the current liberalized electricity markets' structure incumbent distribution system operators (DSOs) will control the AMI and the meter data. This gives rise to concerns about anti-competitiveness. We argue that leveraging the AMI in a social welfare maximizing way requires non-discriminatory access for all entitled parties to the (1) AMI and the (2) meter data through (3) interoperable standards. We discuss possible regulatory remedies to ensure a level playing-field for innovative services in smart grids and consider implications for research and regulation. --Regulation,Smart Grid,Smart Meter,Antitrust
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