SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE International Conference on Cloud and Service Computing (CSC 2011, IEEE Press, USA), Hong Kong, China, December 12-14, 201

Disaster recovery in single-cloud and multi-cloud environments: Issues and challenges

Information Technology (IT) data services provided by cloud providers (CPs) face significant challenges in maintaining services and their continuity during a disaster. The primary concern for data recovery (DR) in the cloud is finding ways to ensure that the process of data backup and recovery is effective in providing high data availability, flexibility, and reliability at a reasonable cost. Numerous data backup solutions have been designed for a single-cloud architecture; however, making a single copy of data may not be sufficient because damage to data may cause irrecoverable loss during a disaster. Other solutions have involved multiple replications on more than one remote cloud provider (Multi-Cloud). Most suggested solutions have proposed obtaining a high level of reliability by producing at least three replicas of the data and either storing all replicas at a single location or distributing them over numerous remote locations. The drawbacks to this approach are high costs, large storage space consumption and (especially in the case of data-intensive cloud-based applications) increased network traffic. In this paper, we discuss the issues raised by DR for both Single-Cloud and MultiCloud environments. We also examine previous studies concerning cloud-based DR to highlight issues that researchers of cloud-based DR have considered to be most important

Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks

Recently, due to rapid development of information and communication technologies, the data are created and consumed in the avalanche way. Distributed computing create preconditions for analyzing and processing such Big Data by distributing the computations among a number of compute nodes. In this work, performance of distributed computing environments on the basis of Hadoop and Spark frameworks is estimated for real and virtual versions of clusters. As a test task, we chose the classic use case of word counting in texts of various sizes. It was found that the running times grow very fast with the dataset size and faster than a power function even. As to the real and virtual versions of cluster implementations, this tendency is the similar for both Hadoop and Spark frameworks. Moreover, speedup values decrease significantly with the growth of dataset size, especially for virtual version of cluster configuration. The problem of growing data generated by IoT and multimodal (visual, sound, tactile, neuro and brain-computing, muscle and eye tracking, etc.) interaction channels is presented. In the context of this problem, the current observations as to the running times and speedup on Hadoop and Spark frameworks in real and virtual cluster configurations can be very useful for the proper scaling-up and efficient job management, especially for machine learning and Deep Learning applications, where Big Data are widely present.Comment: 5 pages, 1 table, 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF-2017) (Lviv, Ukraine