Performance-oriented Cloud Provisioning: Taxonomy and Survey

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table

Proactive Scalability and Management of Resources in Hybrid Clouds via Machine Learning

In this paper, we present a novel framework for supporting the management and optimization of application subject to software anomalies and deployed on large scale cloud architectures, composed of different geographically distributed cloud regions. The framework uses machine learning models for predicting failures caused by accumulation of anomalies. It introduces a novel workload balancing approach and a proactive system scale up/scale down technique. We developed a prototype of the framework and present some experiments for validating the applicability of the proposed approache

Design, Implementation and Experiments for Moving Target Defense Framework

The traditional defensive security strategy for distributed systems employs well-established defensive techniques such as; redundancy/replications, firewalls, and encryption to prevent attackers from taking control of the system. However, given sufficient time and resources, all these methods can be defeated, especially when dealing with sophisticated attacks from advanced adversaries that leverage zero-day exploits

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

Reliable and energy efficient resource provisioning in cloud computing systems

Cloud Computing has revolutionized the Information Technology sector by giving computing a perspective of service. The services of cloud computing can be accessed by users not knowing about the underlying system with easy-to-use portals. To provide such an abstract view, cloud computing systems have to perform many complex operations besides managing a large underlying infrastructure. Such complex operations confront service providers with many challenges such as security, sustainability, reliability, energy consumption and resource management. Among all the challenges, reliability and energy consumption are two key challenges focused on in this thesis because of their conflicting nature. Current solutions either focused on reliability techniques or energy efficiency methods. But it has been observed that mechanisms providing reliability in cloud computing systems can deteriorate the energy consumption. Adding backup resources and running replicated systems provide strong fault tolerance but also increase energy consumption. Reducing energy consumption by running resources on low power scaling levels or by reducing the number of active but idle sitting resources such as backup resources reduces the system reliability. This creates a critical trade-off between these two metrics that are investigated in this thesis. To address this problem, this thesis presents novel resource management policies which target the provisioning of best resources in terms of reliability and energy efficiency and allocate them to suitable virtual machines. A mathematical framework showing interplay between reliability and energy consumption is also proposed in this thesis. A formal method to calculate the finishing time of tasks running in a cloud computing environment impacted with independent and correlated failures is also provided. The proposed policies adopted various fault tolerance mechanisms while satisfying the constraints such as task deadlines and utility values. This thesis also provides a novel failure-aware VM consolidation method, which takes the failure characteristics of resources into consideration before performing VM consolidation. All the proposed resource management methods are evaluated by using real failure traces collected from various distributed computing sites. In order to perform the evaluation, a cloud computing framework, 'ReliableCloudSim' capable of simulating failure-prone cloud computing systems is developed. The key research findings and contributions of this thesis are: 1. If the emphasis is given only to energy optimization without considering reliability in a failure prone cloud computing environment, the results can be contrary to the intuitive expectations. Rather than reducing energy consumption, a system ends up consuming more energy due to the energy losses incurred because of failure overheads. 2. While performing VM consolidation in a failure prone cloud computing environment, a significant improvement in terms of energy efficiency and reliability can be achieved by considering failure characteristics of physical resources. 3. By considering correlated occurrence of failures during resource provisioning and VM allocation, the service downtime or interruption is reduced significantly by 34% in comparison to the environments with the assumption of independent occurrence of failures. Moreover, measured by our mathematical model, the ratio of reliability and energy consumption is improved by 14%

Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200