Design Architecture-Based on Web Server and Application Cluster in Cloud Environment

Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the work of searching and fetching from thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The major idea is to design a web server in the map phase using the jetty web server which will give a fast and efficient way of searching data in MapReduce paradigm. For real time processing on Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in web server with multi-level index keys. The web server uses to handle traffic throughput. By web clustering technology we can improve the application performance. To keep the work down, the load balancer should automatically be able to distribute load to the newly added nodes in the server

Assessment of Response Time for New Multi Level Feedback Queue Scheduler

Response time is one of the characteristics of scheduler, happens to be a prominent attribute of any CPU scheduling algorithm. The proposed New Multi Level Feedback Queue [NMLFQ] Scheduler is compared with dynamic, real time, Dependent Activity Scheduling Algorithm (DASA) and Lockes Best Effort Scheduling Algorithm (LBESA). We abbreviated beneficial result of NMLFQ scheduler in comparison with dynamic best effort schedulers with respect to response time.Comment: 7 pages, 5 figure

Determination of micro-scale plastic strain caused by orthogonal cutting

An electron beam lithography technique has been used to produce microgrids in order to measure local plastic strains, induced during an orthogonal cutting process, at the microscopic scale in the shear zone and under the machined surface. Microgrids with a 10 μm pitch and a line width less than 1 μm have been printed on the polished surface of an aluminium alloy AA 5182 to test the applicability of the technique in metal cutting operations. Orthogonal cutting tests were carried out at 40 mm/s. Results show that the distortion of the grids could successfully be used to compute plastic strains due to orthogonal cutting with higher accuracy compared to other techniques reported in the literature. Strain maps of the machined specimens have been produced and show high-strain gradients very close to the machined surface with local values reaching 2.2. High-resolution strain measurements carried out in the primary deformation zone also provide new insight into the material deformation during the chip formation process

CodePlan: Repository-level Coding using LLMs and Planning

Software engineering activities such as package migration, fixing errors reports from static analysis or testing, and adding type annotations or other specifications to a codebase, involve pervasively editing the entire repository of code. We formulate these activities as repository-level coding tasks. Recent tools like GitHub Copilot, which are powered by Large Language Models (LLMs), have succeeded in offering high-quality solutions to localized coding problems. Repository-level coding tasks are more involved and cannot be solved directly using LLMs, since code within a repository is inter-dependent and the entire repository may be too large to fit into the prompt. We frame repository-level coding as a planning problem and present a task-agnostic framework, called CodePlan to solve it. CodePlan synthesizes a multi-step chain of edits (plan), where each step results in a call to an LLM on a code location with context derived from the entire repository, previous code changes and task-specific instructions. CodePlan is based on a novel combination of an incremental dependency analysis, a change may-impact analysis and an adaptive planning algorithm. We evaluate the effectiveness of CodePlan on two repository-level tasks: package migration (C#) and temporal code edits (Python). Each task is evaluated on multiple code repositories, each of which requires inter-dependent changes to many files (between 2-97 files). Coding tasks of this level of complexity have not been automated using LLMs before. Our results show that CodePlan has better match with the ground truth compared to baselines. CodePlan is able to get 5/6 repositories to pass the validity checks (e.g., to build without errors and make correct code edits) whereas the baselines (without planning but with the same type of contextual information as CodePlan) cannot get any of the repositories to pass them