929 research outputs found
Rule-Based System Architecting of Earth Observing Systems: Earth Science Decadal Survey
This paper presents a methodology to explore the architectural trade space of Earth observing satellite systems, and applies it to the Earth Science Decadal Survey. The architecting problem is formulated as a combinatorial optimization problem with three sets of architectural decisions: instrument selection, assignment of instruments to satellites, and mission scheduling. A computational tool was created to automatically synthesize architectures based on valid combinations of options for these three decisions and evaluate them according to several figures of merit, including satisfaction of program requirements, data continuity, affordability, and proxies for fairness, technical, and programmatic risk. A population-based heuristic search algorithm is used to search the trade space. The novelty of the tool is that it uses a rule-based expert system to model the knowledge-intensive components of the problem, such as scientific requirements, and to capture the nonlinear positive and negative interactions between instruments (synergies and interferences), which drive both requirement satisfaction and cost. The tool is first demonstrated on the past NASA Earth Observing System program and then applied to the Decadal Survey. Results suggest that the Decadal Survey architecture is dominated by other more distributed architectures in which DESDYNI and CLARREO are consistently broken down into individual instruments."La Caixa" FoundationCharles Stark Draper LaboratoryGoddard Space Flight Cente
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure
Recent technology advancements in the areas of compute, storage and
networking, along with the increased demand for organizations to cut costs
while remaining responsive to increasing service demands have led to the growth
in the adoption of cloud computing services. Cloud services provide the promise
of improved agility, resiliency, scalability and a lowered Total Cost of
Ownership (TCO). This research introduces a framework for minimizing cost and
maximizing resource utilization by using an Integer Linear Programming (ILP)
approach to optimize the assignment of workloads to servers on Amazon Web
Services (AWS) cloud infrastructure. The model is based on the classical
minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin
Autonomic Cloud Computing: Open Challenges and Architectural Elements
As Clouds are complex, large-scale, and heterogeneous distributed systems,
management of their resources is a challenging task. They need automated and
integrated intelligent strategies for provisioning of resources to offer
services that are secure, reliable, and cost-efficient. Hence, effective
management of services becomes fundamental in software platforms that
constitute the fabric of computing Clouds. In this direction, this paper
identifies open issues in autonomic resource provisioning and presents
innovative management techniques for supporting SaaS applications hosted on
Clouds. We present a conceptual architecture and early results evidencing the
benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape
Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation
One of the challenges of deploying multitenant cloud-hosted
services that are designed to use (or be integrated with) several
components is how to implement the required degree
of isolation between the components when there is a change
in the workload. Achieving the highest degree of isolation
implies deploying a component exclusively for one tenant;
which leads to high resource consumption and running cost
per component. A low degree of isolation allows sharing of
resources which could possibly reduce cost, but with known
limitations of performance and security interference. This
paper presents a model-based algorithm together with four
variants of a metaheuristic that can be used with it, to provide
near-optimal solutions for deploying components of a
cloud-hosted application in a way that guarantees multitenancy
isolation. When the workload changes, the model based
algorithm solves an open multiclass QN model to
determine the average number of requests that can access
the components and then uses a metaheuristic to provide
near-optimal solutions for deploying the components. Performance
evaluation showed that the obtained solutions had
low variability and percent deviation when compared to the
reference/optimal solution. We also provide recommendations
and best practice guidelines for deploying components
in a way that guarantees the required degree of isolation
Augmenting the Space Domain Awareness Ground Architecture via Decision Analysis and Multi-Objective Optimization
Purpose — The US Government is challenged to maintain pace as the world’s de facto provider of space object cataloging data. Augmenting capabilities with nontraditional sensors present an expeditious and low-cost improvement. However, the large tradespace and unexplored system of systems performance requirements pose a challenge to successful capitalization. This paper aims to better define and assess the utility of augmentation via a multi-disiplinary study. Design/methodology/approach — Hypothetical telescope architectures are modeled and simulated on two separate days, then evaluated against performance measures and constraints using multi-objective optimization in a heuristic algorithm. Decision analysis and Pareto optimality identifies a set of high-performing architectures while preserving decision-maker design flexibility. Findings — Capacity, coverage and maximum time unobserved are recommended as key performance measures. A total of 187 out of 1017 architectures were identified as top performers. A total of 29% of the sensors considered are found in over 80% of the top architectures. Additional considerations further reduce the tradespace to 19 best choices which collect an average of 49–51 observations per space object with a 595–630 min average maximum time unobserved, providing redundant coverage of the Geosynchronous Orbit belt. This represents a three-fold increase in capacity and coverage and a 2 h (16%) decrease in the maximum time unobserved compared to the baseline government-only architecture as-modeled. Originality/value — This study validates the utility of an augmented network concept using a physics-based model and modern analytical techniques. It objectively responds to policy mandating cataloging improvements without relying solely on expert-derived point solutions
Theoretical Analysis for Scale-down-Aware Service Allocation in Cloud Storage Systems
Servcie allocation algorithms have been drawing popularity in cloudcomputing research community. There has been lots of research onimprovingservice allocation schemes for high utilization, latency reductionand VM migration enfficient, but few work focus on energy consumptionaffected by instance placement in data centers. In this paper we propose an algorithm in which to maximize the number of freed-up machines in data centers, machines that host purely scale-down instances, which are reuiqred to be shut down for energy saving at certain points of time. We intuitively employ a probability partitioning mechanism to schedule services such that the goal of the maximization can be achieved. Furthermore we perform a set of experiments to test the partitioning rules, which show that the proposed algorithms can dynamically increase the number of freed-up machines substantially.DOI:http://dx.doi.org/10.11591/ijece.v3i1.179
Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
Cloud computing is offering utility-oriented IT services to users worldwide.
Based on a pay-as-you-go model, it enables hosting of pervasive applications
from consumer, scientific, and business domains. However, data centers hosting
Cloud applications consume huge amounts of energy, contributing to high
operational costs and carbon footprints to the environment. Therefore, we need
Green Cloud computing solutions that can not only save energy for the
environment but also reduce operational costs. This paper presents vision,
challenges, and architectural elements for energy-efficient management of Cloud
computing environments. We focus on the development of dynamic resource
provisioning and allocation algorithms that consider the synergy between
various data center infrastructures (i.e., the hardware, power units, cooling
and software), and holistically work to boost data center energy efficiency and
performance. In particular, this paper proposes (a) architectural principles
for energy-efficient management of Clouds; (b) energy-efficient resource
allocation policies and scheduling algorithms considering quality-of-service
expectations, and devices power usage characteristics; and (c) a novel software
technology for energy-efficient management of Clouds. We have validated our
approach by conducting a set of rigorous performance evaluation study using the
CloudSim toolkit. The results demonstrate that Cloud computing model has
immense potential as it offers significant performance gains as regards to
response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference
on Parallel and Distributed Processing Techniques and Applications (PDPTA
2010), Las Vegas, USA, July 12-15, 201
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