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

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications