Distributed workload and response time management for web applications

Abstract-Managing workload for large scale web applications is a fundamental task for satisfactory quality of service, low management and operation cost. In this paper, we present SCOPS, a system of distributed workload management to achieve service differentiation and overload protection in such large scale deployment. Our system splits the workload management logic into distributed components on each back-end server and frontend proxy. The control solution is designed to protect the backend server from overloading and to achieve both efficient usage of system resource and service differentiation by employing a unique optimization target. The control components are automatically organized based on the flow of workloads, such that management overhead is minimized. SCOPS is extremely flexible because it requires no source code changes to host OS, application servers, or web applications. Additionally, the distributed design makes it scalable and robust for cloud scale server deployment. Experiments with our implementation confirm SCOPS's performance with dynamic heavy workload, incurring neglectable runtime overhead. More importantly, SCOPS also ensures fault-tolerance and fast convergence to system failures

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing enables sharing, selection and aggregation of resources for solving complex and large-scale scientific problems. Grids computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in resource management. Grid scheduling is the key issue in grid environment in which its system must meet the functional requirements of heterogeneous domains, which are sometimes conflicting in nature also, like user, application, and network. Moreover, the system must satisfy non-functional requirements like reliability, efficiency, performance, effective resource utilization, and scalability. Thus, overall aim of this research is to introduce new grid scheduling algorithms for resource allocation as well as for job scheduling for enabling a highly efficient and effective utilization of the resources in executing various applications. The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. The issue is not only to develop new algorithms, but also to evaluate them on an experimental computational grid, using synthetic and real workload traces, along with the other existing job scheduling algorithms. Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability

Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

A Framework for QoS-aware Execution of Workflows over the Cloud

The Cloud Computing paradigm is providing system architects with a new powerful tool for building scalable applications. Clouds allow allocation of resources on a "pay-as-you-go" model, so that additional resources can be requested during peak loads and released after that. However, this flexibility asks for appropriate dynamic reconfiguration strategies. In this paper we describe SAVER (qoS-Aware workflows oVER the Cloud), a QoS-aware algorithm for executing workflows involving Web Services hosted in a Cloud environment. SAVER allows execution of arbitrary workflows subject to response time constraints. SAVER uses a passive monitor to identify workload fluctuations based on the observed system response time. The information collected by the monitor is used by a planner component to identify the minimum number of instances of each Web Service which should be allocated in order to satisfy the response time constraint. SAVER uses a simple Queueing Network (QN) model to identify the optimal resource allocation. Specifically, the QN model is used to identify bottlenecks, and predict the system performance as Cloud resources are allocated or released. The parameters used to evaluate the model are those collected by the monitor, which means that SAVER does not require any particular knowledge of the Web Services and workflows being executed. Our approach has been validated through numerical simulations, whose results are reported in this paper