Traditional Greedy Algorithm Research Articles

An On-line Multi-CBR Agent Dispatching Algorithm

Case-based reasoning (CBR) is an effective and fast problem-solving methodology, which solves new problems by remembering and adaptation of past cases. With the increasing requests for useful references for all kinds of problems and from different locations, keeping a single CBR system seems to be outdated and not practical. Multi-CBR agents located in different places are of great support to fast meet these requests. In this paper, the architecture of a multi-CBR agent system is proposed, where the CBR agents locate at different places, and are assumed to have the same ability to deal with new problem independently. When the requests in a request queue from different places are coming one by one, we propose a new policy of dispatching which agent to satisfy the request queue. Throughout the paper, we assume that the system must solve the coming request by considering only past requests. In this context, the performance of traditional greedy algorithms is not satisfactory. We apply a new but simple approach --- competitive algorithm for on-line problem (called On-line multi-CBR agent dispatching algorithm) to determine the dispatching policy to keep comparative low cost. The corresponding on-line dispatching algorithm is proposed and the competitive ratio is given. Based on the competitive algorithm, the dispatching of multi-CBR agents is optimized.

A greedier approach for finding tag SNPs

Recent studies have shown that a small subset of Single Nucleotide Polymorphisms (SNPs) (called tag SNPs) is sufficient to capture the haplotype patterns in a high linkage disequilibrium region. To find the minimum set of tag SNPs, exact algorithms for finding the optimal solution could take exponential time. On the other hand, approximation algorithms are more efficient but may fail to find the optimal solution. We propose a hybrid method that combines the ideas of the branch-and-bound method and the greedy algorithm. This method explores larger solution space to obtain a better solution than a traditional greedy algorithm. It also allows the user to adjust the efficiency of the program and quality of solutions. This algorithm has been implemented and tested on a variety of simulated and biological data. The experimental results indicate that our program can find better solutions than previous methods. This approach is quite general since it can be used to adapt other greedy algorithms to solve their corresponding problems. The program is available upon request.

Adaptive workload-dependent scheduling for large-scale content delivery systems

delivery has become an important application in the Internet. Content in this context can be a range of objects from movies to Web pages to software distribution. A streaming content delivery server should provide nearly immediate and continuous service by provisioning sufficient server and local network resources for the duration of playout. Because of the resource implications of delivering many large files simultaneously, scalability is an important requirement. Good scalability can be achieved by using a single channel to serve multiple users waiting for the same object (referred to as batching). Batching is especially useful during high load periods. Typical strategies in use today for allocating channels use a greedy allocate-as-needed policy with little consideration for anything other than satisfying the current request or maximizing the number of batched requests. Macroscopic system characteristics, like request arrival patterns, have stable long-term averages, but can vary unpredictably across shorter intervals. This variability can cause scheduling algorithms to suffer poor and unpredictable short-term performance. We propose a set of rate-based allocation algorithms to solve these limitations. We present our work in developing a set of workloads with variable request rates, quantify the drawbacks of traditional greedy channel-allocation algorithms, and quantify the advantages of the rate-based algorithms. We also generalize the content delivery model and discuss when rate-based algorithms might be effective other kinds of systems.