Topic
Algorithm design
About: Algorithm design is a research topic. Over the lifetime, 27860 publications have been published within this topic receiving 454211 citations.
Papers published on a yearly basis
1 / 2
Papers
20 Jun 2011
TL;DR: A dynamic cloud resource provisioning algorithm is proposed which can effectively support VoD streaming with low cloud utilization cost and is verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform.
Abstract: Internet-based cloud computing is a new computing paradigm aiming to provide agile and scalable resource access in a utility-like fashion. Other than being an ideal platform for computation-intensive tasks, clouds are believed to be also suitable to support large-scale applications with periods of flash crowds by providing elastic amounts of bandwidth and other resources on the fly. The fundamental question is how to configure the cloud utility to meet the highly dynamic demands of such applications at a modest cost. In this paper, we address this practical issue with solid theoretical analysis and efficient algorithm design using Video on Demand (VoD) as the example application. Having intensive bandwidth and storage demands in real time, VoD applications are purportedly ideal candidates to be supported on a cloud platform, where the on-demand resource supply of the cloud meets the dynamic demands of the VoD applications. We introduce a queueing network based model to characterize the viewing behaviors of users in a multichannel VoD application, and derive the server capacities needed to support smooth playback in the channels for two popular streaming models: client-server and P2P. We then propose a dynamic cloud resource provisioning algorithm which, using the derived capacities and instantaneous network statistics as inputs, can effectively support VoD streaming with low cloud utilization cost. Our analysis and algorithm design are verified and extensively evaluated using large-scale experiments under dynamic realistic settings on a home-built cloud platform.
166 citations
Proceedings Article•
10 Jun 1987TL;DR: The architecture proposed not only makes design of the controller simple but also its implementation, which can be built right now from off the shelf components or integrated using standard VLSI cells.
Abstract: A new CAD method and associated architectures are proposed for linear controllers. The design method and architecture are based on recent results which parametrize all controllers which stabilize a given plant. With this architecture, the design of controllers is a convex programming problem which can be solved numerically. Constraints on the closed-loop system such as asymptotic tracking, decoupling, limits on peak excursions of variables, step response settling time and overshoot, as well as frequency domain inequalities are readily incorporated in the design. The minimization objective is quite general, with LQG, H ? , and new l 1 types as special cases. The constraints and objective are specified in a control specification language which is natural for the control engineer, referring directly to step responses, noise powers, transfer functions, and so on. This control specification language will be the input to a compiler which will translate the specifications into a standard convex program in RL, which is then solved by some numerical convex program solver. A small but powerful subset of the language has been specified and its associated compiler implemented. The architecture proposed not only makes design of the controller simple but also its implementation. These controllers can be built right now from off the shelf components or integrated using standard VLSI cells.
166 citations
TL;DR: A new approach to directly handle the challenges to solve many-objective optimization problems (MaOPs) is proposed, which includes two stages: first, the whole population quickly approaches a small number of "target” points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and well distribute.
Abstract: Evolutionary algorithms have been successfully applied for exploring both converged and diversified approximate Pareto-optimal fronts in multiobjective optimization problems, two- or three-objective in general. However, when solving problems with many objectives, nearly all algorithms perform poorly due to the loss of selection pressure in fitness evaluation. An extremely large objective space could inadvertently deteriorate the effect of an evolutionary operator. In this paper, we propose a new approach to directly handle the challenges to solve many-objective optimization problems (MaOPs). This novel design includes two stages: first, the whole population quickly approaches a small number of “target” points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and well distribute. As a case study, the proposed algorithm based on this design is compared with five state-of-the-art algorithms. Experimental results show that the proposed method exhibits improved performance in both convergence and diversity for solving MaOPs.
166 citations
TL;DR: To assess stability, which is also a precondition for scalability, the authors introduce and measure the load-sharing hit-ratio, the ratio of remote execution requests concluded successfully.
Abstract: A method for qualitative and quantitative analysis of load sharing algorithms is presented, using a number of well known examples as illustration Algorithm design choice are considered with respect to the main activities of information dissemination and allocation decision making It is argued that nodes must be capable of making local decisions, and for this efficient state, dissemination techniques are necessary Activities related to remote execution should be bounded and restricted to a small proportion of the activity of the system The quantitative analysis provides both performance and efficiency measures, including consideration of the load and delay characteristics of the environment To assess stability, which is also a precondition for scalability, the authors introduce and measure the load-sharing hit-ratio, the ratio of remote execution requests concluded successfully Using their analysis method, they are able to suggest improvements to some published algorithms >
166 citations
1 Mar 2014
TL;DR: Experimental results show that the proposed multi-level label propagation (MLP) method can partition billion-node graphs within several hours on a distributed memory system consisting of merely several machines, and the quality of the partitions produced is comparable to state-of-the-art approaches applied on toy-size graphs.
Abstract: Billion-node graphs pose significant challenges at all levels from storage infrastructures to programming models. It is critical to develop a general purpose platform for graph processing. A distributed memory system is considered a feasible platform supporting online query processing as well as offline graph analytics. In this paper, we study the problem of partitioning a billion-node graph on such a platform, an important consideration because it has direct impact on load balancing and communication overhead. It is challenging not just because the graph is large, but because we can no longer assume that the data can be organized in arbitrary ways to maximize the performance of the partitioning algorithm. Instead, the algorithm must adopt the same data and programming model adopted by the system and other applications. In this paper, we propose a multi-level label propagation (MLP) method for graph partitioning. Experimental results show that our solution can partition billion-node graphs within several hours on a distributed memory system consisting of merely several machines, and the quality of the partitions produced by our approach is comparable to state-of-the-art approaches applied on toy-size graphs.
165 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 17 |
| 2024 | 41 |
| 2023 | 29 |
| 2022 | 22 |
| 2021 | 148 |
| 2020 | 173 |