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Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: This paper focuses on minimizing the total number of Virtual Network Function (VNF) instances to provide a specific service to all the flows in a network, and designs two simple greedy algorithms that achieve an approximation ratio of (1 − o(1))ln m + 2, which is asymptotically optimal.
Abstract: Network Function Virtualization (NFV) has the potential to significantly reduce the capital and operating expenses, shorten product release cycle, and improve service agility. In this paper, we focus on minimizing the total number of Virtual Network Function (VNF) instances to provide a specific service (possibly at different locations) to all the flows in a network. Certain network security and analytics applications may allow fractional processing of a flow at different nodes (corresponding to datacenters), giving an opportunity for greater optimization of resources. Through a reduction from the set cover problem, we show that this problem is NP-hard and cannot even be approximated within a factor of (1 - o(1)) ln(m) (where m is the number of flows) unless P=NP. Then, we design two simple greedy algorithms and prove that they achieve an approximation ratio of (1 - o(1)) ln(m) + 2, which is asymptotically optimal. For special cases where each node hosts multiple VNF instances (which is typically true in practice), we also show that our greedy algorithms have a constant approximation ratio. Further, for tree topologies we develop an optimal greedy algorithm by exploiting the inherent topological structure. Finally, we conduct extensive numerical experiments to evaluate the performance of our proposed algorithms in various scenarios.
82 citations
18 May 2009
TL;DR: In this article, the authors propose a novel approach for sharing cluster resources among competing jobs, which increases cluster utilization while optimizing a user-centric metric that captures both notions of performance and fairness.
Abstract: We propose a novel approach for sharing cluster resources among competing jobs. The key advantage of our approach over current solutions is that it increases cluster utilization while optimizing a user-centric metric that captures both notions of performance and fairness. We motivate and formalize the corresponding resource allocation problem, determine its complexity, and propose several algorithms to solve it in the case of a static workload that consists of sequential jobs. Via extensive simulation experiments we identify an algorithm that runs quickly, that is always on par with or better than its competitors, and that produces resource allocations that are close to optimal. We find that the extension of our approach to parallel jobs leads to similarly good results. Finally, we explain how to extend our work to dynamicworkloads.
82 citations
TL;DR: A greedy algorithm called Greedy-W SC and an ant colony optimization based algorithm called ACO-WSC are presented, which attempt to select cloud combinations that are feasible and use the minimum number of clouds, and experimental results show that the proposed ant colonies optimization method can effectively and efficiently find cloud combinations with a minimal number of Clouds.
82 citations
TL;DR: The results show that GA is competitive only for pairwise testing for subjects with a small number of constraints; the results for the greedy algorithm are actually slightly superior, however, the results are critically dependent on the approach adopted to constraint handling.
Abstract: Combinatorial interaction testing (CIT) is important because it tests the interactions between the many features and parameters that make up the configuration space of software systems. Simulated Annealing (SA) and Greedy Algorithms have been widely used to find CIT test suites. From the literature, there is a widely-held belief that SA is slower, but produces more effective tests suites than Greedy and that SA cannot scale to higher strength coverage. We evaluated both algorithms on seven real-world subjects for the well-studied two-way up to the rarely-studied six-way interaction strengths. Our findings present evidence to challenge this current orthodoxy: real-world constraints allow SA to achieve higher strengths. Furthermore, there was no evidence that Greedy was less effective (in terms of time to fault revelation) compared to SA; the results for the greedy algorithm are actually slightly superior. However, the results are critically dependent on the approach adopted to constraint handling. Moreover, we have also evaluated a genetic algorithm for constrained CIT test suite generation. This is the first time strengths higher than 3 and constraint handling have been used to evaluate GA. Our results show that GA is competitive only for pairwise testing for subjects with a small number of constraints.
82 citations
04 Jan 1997
TL;DR: This paper model the hardware software partitioning problem as a Constraint Satisfaction Problem (CSP), and presents a genetic algorithm based approach to solve the CSP in order to obtain the partitioning solution.
Abstract: Hardware software co-design is gaining importance with the advent of CAD for embedded systems. A key phase in such designs is partitioning the specification into hardware and software implementation sets. The problem being combinatorically explosive, several greedy search algorithms have been proposed for hardware software partitioning. In this paper, we model the hardware software partitioning problem as a Constraint Satisfaction Problem (CSP), and present a genetic algorithm based approach to solve the CSP in order to obtain the partitioning solution.
81 citations