Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

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

Service Isolation vs. Consolidation: Implications for Iaas Cloud Application Deployment

Service isolation, achieved by deploying components of multi -tier applications using separate virtual machines (VMs), is a common \u27best\u27 practice. Various advantages cited include simpler deployment architectures, easier resource scalability for supporting dynamic application throughput requirements, and support for component-level fault tolerance . This paper presents results from an empirical study which investigates the performance implications of component placement for deployments of multi -tier applications to Infrastructure-as-a- Service (IaaS) clouds. Relationship s between performance and resource utilization (CPU, disk, network) are investigated to better understand the implications which result from how applications are deployed. All possible deployments for two variants of a multi -tier application were tested, one computationally bound by the model, the other bound by a geospatial database. The best performing deployments required as few as 2 VMs, half the number required for service isolation, demonstrating potential cost savings with service consolidation. Resource use (CPU time, disk I/O, and network I/O) varied based on component placement and VM memory allocation. Using separate VMs to host each application component resulted in performance overhead of ~1 -2%. Relationships between resource utilization an d performance were harnessed to build a multiple linear regression model to predict performance of component deployments. CPU time, disk sector reads, and disk sector writes are identified as the most powerful performance predictors for component deployments