Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.

A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids

Abstract: With the explosive growth in computers and the growing scarcity in electric supply, reduction of energy consumption in large-scale computing systems has become a research issue of paramount importance. In this paper, we study the problem of allocation of tasks onto a computational grid, with the aim to simultaneously minimize the energy consumption and the makespan subject to the constraints of deadlines and tasks' architectural requirements. We propose a solution from cooperative game theory based on the concept of Nash bargaining solution. In this cooperative game, machines collectively arrive at a decision that describes the task allocation that is collectively best for the system, ensuring that the allocations are both energy and makespan optimized. Through rigorous mathematical proofs we show that the proposed cooperative game in mere O(n mlog(m)) time (where n is the number of tasks and m is the number of machines in the system) produces a Nash bargaining solution that guarantees Pareto-optimally. The simulation results show that the proposed technique achieves superior performance compared to the greedy and linear relaxation (LR) heuristics, and with competitive performance relative to the optimal solution implemented in LINDO for small-scale problems.

A Simple Model for Analyzing P2P Streaming Protocols

Abstract: P2P streaming tries to achieve scalability (like P2P file distribution) and at the same time meet real-time playback requirements. It is a challenging problem still not well understood. In this paper, we describe a simple stochastic model that can be used to compare different data-driven downloading strategies based on two performance metrics: continuity (probability of continuous playback), and startup latency (expected time to start playback). We first study two simple strategies: rarest first and greedy. The former is a well-known strategy for P2P file sharing that gives good scalability, whereas the latter an intuitively reasonable strategy to optimize continuity and startup latency from a single peer's viewpoint. Greedy, while achieving low startup latency, fares poorly in continuity by failing to maximize P2P sharing; whereas rarest first is the opposite. This highlights the trade-off between startup latency and continuity, and how system scalability improves continuity. Based on this insight, we propose a mixed strategy that can be used to achieve the best of both worlds. Our algorithm dynamically adapts to the peer population size to ensure scalability; at the same time, it reserves part of a peer's effort to the immediate playback requirements to ensure low startup latency.

Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

Abstract: Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The existing multi-objective workflow scheduling approaches are still limited in many ways, e.g., encoding is restricted by prior experts' knowledge when handling a dynamic real-time problem, which strongly influences the performance of scheduling. In this paper, we apply a deep-Q-network model in a multi-agent reinforcement learning setting to guide the scheduling of multi-workflows over infrastructure-as-a-service clouds. To optimize multi-workflow completion time and user's cost, we consider a Markov game model, which takes the number of workflow applications and heterogeneous virtual machines as state input and the maximum completion time and cost as rewards. The game model is capable of seeking for correlated equilibrium between make-span and cost criteria without prior experts' knowledge and converges to the correlated equilibrium policy in a dynamic real-time environment. To validate our proposed approach, we conduct extensive case studies based on multiple well-known scientific workflow templates and Amazon EC2 cloud. The experimental results clearly suggest that our proposed approach outperforms traditional ones, e.g., non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimization, and game-theoretic-based greedy algorithms, in terms of optimality of scheduling plans generated.

Robust principal component analysis with non-greedy l 1 -norm maximization

Abstract: Principal Component Analysis (PCA) is one of the most important methods to handle high-dimensional data. However, the high computational complexity makes it hard to apply to the large scale data with high dimensionality, and the used l2-norm makes it sensitive to outliers. A recent work proposed principal component analysis based on l1-normmaximization, which is efficient and robust to outliers. In that work, a greedy strategy was applied due to the difficulty of directly solving the l1-norm maximization problem, which is easy to get stuck in local solution. In this paper, we first propose an efficient optimization algorithmto solve a general l1-norm maximization problem, and then propose a robust principal component analysis with non-greedy l1-norm maximization. Experimental results on real world datasets show that the nongreedy method always obtains much better solution than that of the greedy method.

Demand-based radio network planning of cellular mobile communication systems

Abstract: This paper presents a demand-based engineering method for designing radio networks of cellular mobile communication systems. The proposed procedure is based on a forward-engineering method, the integrated approach to cellular network planning and is facilitated by the application of a new discrete population model for the traffic description, the demand node concept. The use of the concept enables the formulation of the transmitter locating task as a maximal coverage location problem (MCLP), which is well known in economics for modeling and solving facility location problems. For the network optimization task, we introduced the set cover base station positioning algorithm (SCBPA), which is based on a greedy heuristic for solving the MCLP problem. Furthermore, we present the planning tool prototype ICEPT (Integrated Cellular network Planning Tool), which is based on these ideas and show a first result from a real world planning case.