Investigation into Mobile Learning Framework in Cloud Computing Platform

Abstract—Cloud computing infrastructure is increasingly used for distributed applications. Mobile learning applications deployed in the cloud are a new research direction. The applications require specific development approaches for effective and reliable communication. This paper proposes an interdisciplinary approach for design and development of mobile applications in the cloud. The approach includes front service toolkit and backend service toolkit. The front service toolkit packages data and sends it to a backend deployed in a cloud computing platform. The backend service toolkit manages rules and workflow, and then transmits required results to the front service toolkit. To further show feasibility of the approach, the paper introduces a case study and shows its performance

Automated managed cloud-platforms based on energy policies

Delivering environmentally friendly services has become an important issue in Cloud Computing due to awareness provided by governments and environmental conservation organisations about the impact of electricity usage on carbon footprints. Cloud providers and cloud consumers (organisations/ enterprises) have their own defined $green$ $policies$ to control energy consumption at their data centers. At service management level, $green$ $policies$ can be mapped as $energy$ $management$ $policies$ or $management$ $policies$. Focusing at cloud consumer's side, $management$ $policies$ are described by business managers which can change regularly. The continuous changing is based on the nature of the technical environment, changes in regulation; and business requirements. Therefore, there is a gap between the level of describing and implementing $management$ $policies$ in the cloud environment. This thesis provides a method to bridge that gap by (a) defining a specification for formulating $management$ $policies$ into executable form for an infrastructure-as-a-service (IaaS) cloud model; (b) designing a framework to execute the described $management$ $policies$ automatically; (c) proposing a modelling and analysis method to identify the potential $energy$ $management$ $policy$ that would save energy-cost. Each aspect covered in the thesis is evaluated with a help of an Energy Management Case Study for a private cloud scenario

Go microservices runtime optimization in Kubernetes environment: the importance of garbage collection tuning

Many modern web applications are structured with a microservices architecture to allow easier maintenance and greater horizontal scalability. A language that focuses on this, on parallelism and concurrency is Go, which through some interesting abstractions puts these aspects at the center. In order to facilitate the deployment and orchestration of a microservices architecture based on containers, Kubernetes is often used, with the task of simply managing all the services and their connection. The aim of this work is therefore to study a benchmark application, developed as microservices in Go, and to analyze its performance when some parameters change, both in the language runtime and in the Kubernetes environment. Particular attention is paid to the definition and collection of some metrics, both of the language runtime, and of the environment in which it is executed, so the various resources used by the containers and the overall resources of the node where the application is executed. The work was carried out as an internship in Akamas, a company that develops an application aimed at automating and speeding up this parameter tuning process in search of the best configuration that can optimize the defined objectives and meet the required constraints

Automated Dynamic Resource Provisioning and Monitoring in Virtualized Large-Scale Datacenter

Infrastructure as a Service (IaaS) is a pay-as-you go based cloud provision model which on demand outsources the physical servers, guest virtual machine (VM) instances, storage resources, and networking connections. This article reports the design and development of our proposed innovative symbiotic simulation based system to support the automated management of IaaS-based distributed virtualized data enter. To make the ideas work in practice, we have implemented an Open Stack based open source cloud computing platform. A smart benchmarking application "Cloud Rapid Experimentation and Analysis Tool (aka CBTool)" is utilized to mark the resource allocation potential of our test cloud system. The real-time benchmarking metrics of cloud are fed to a distributed multi-agent based intelligence middleware layer. To optimally control the dynamic operation of prototype data enter, we predefine some custom policies for VM provisioning and application performance profiling within a versatile cloud modeling and simulation toolkit "CloudSim". Both tools for our prototypes' implementation can scale up to thousands of VMs, therefore, our devised mechanism is highly scalable and flexibly be interpolated at large-scale level. Autonomic characteristics of agents aid in streamlining symbiosis among the simulation system and IaaS cloud in a closed feedback control loop. The practical worth and applicability of the multiagent-based technology lies in the fact that this technique is inherently scalable hence can efficiently be implemented within the complex cloud computing environment. To demonstrate the efficacy of our approach, we have deployed an intelligible lightweight representative scenario in the context of monitoring and provisioning virtual machines within the test-bed. Experimental results indicate notable improvement in the resource provision profile of virtualized data enter on incorporating our proposed strategy

Intelligent Management of Virtualised Computer Based Workloads and Systems

Managing the complexity within virtualised IT infrastructure platforms is a common problem for many organisations today. Computer systems are often highly consolidated into a relatively small physical footprint compared with previous decades prior to late 2000s, so much thought, planning and control is necessary to effectively operate such systems within the enterprise computing space. With the development of private, hybrid and public cloud utility computing this has become even more relevant; this work examines how such cloud systems are using virtualisation technology and embedded software to leverage advantages, and it uses a fresh approach of developing and creating an Intelligent decision engine (expert system). Its aim is to help reduce the complexity of managing virtualised computer-based platforms, through tight integration, high-levels of automation to minimise human inputs, errors, and enforce standards and consistency, in order to achieve better management and control. The thesis investigates whether an expert system known as the Intelligent Decision Engine (IDE) could aid the management of virtualised computer-based platforms. Through conducting a series of mixed quantitative and qualitative experiments in the areas of research, the initial findings and evaluation are presented in detail, using repeatable and observable processes and provide detailed analysis on the recorded outputs. The results of the investigation establish the advantages of using the IDE (expert system) to achieve the goal of reducing the complexity of managing virtualised computer-based platforms. In each detailed area examined, it is demonstrated how using a global management approach in combination with VM provisioning, migration, failover, and system resource controls can create a powerful autonomous system

A meter band rate mechanism to improve the native QoS capability of OpenFlow and OpenDaylight

The exponential growth of mobile connected devices with advanced multimedia features imposes a requirement to enhance quality of service (QoS) from heterogeneous systems and networks. In order to satisfy mission-critical multimedia QoS requirements new generation mobile networks must present content-optimized mechanisms in order to use valuable network resources efficiently and provide QoS requirements for each application. This research explores a novel solution for quality of service performance for streaming mission-critical video data in OpenFlow SDN networks. A Meter Band Rate Evaluator (MBE) Mechanism is proposed based on a new band rate description language to improve the native QoS capability of OpenFlow and OpenDaylight. Its design and development are presented and the mechanism is verified through a simulated experiment in an SDN testbed. The results revealed a significant percentage increase in QoS performance when the MBE was enabled. These findings provide support and validation for the effectiveness of the MBE to enhance the native capability of OpenFlow and OpenDaylight for efficient QoS provision

Design and validation of a meter band rate in OpenFlow and OpenDaylight for optimizing QoS

Technological developments in the Internet and communications have created a vastly complex and dynamic context with diverse heterogeneous networks and fast growth of mobile devices and multimedia. As the Internet becomes the primary mode of communication for many organisations there is requirement to enhance quality of service (QoS) from heterogeneous systems and networks. Traditional networks such as TETRA have become increasingly incapable of addressing the demand for media rich, bandwidth intensive traffic flows and applications. Mission-critical multimedia over new generation mobile networks face QoS constraints. This research explores a novel solution for quality of service performance for streaming mission-critical video data in OpenFlow SDN networks. A Meter Band Rate Evaluation (MBE) mechanism is advanced that improves the native QoS capability of OpenFlow and OpenDaylight. The MBE is a physical component added to the OpenFlow meter table to evaluate and dynamically adjust traffic rates and allows the traffic volume to be specified relative to other traffic in the network. Its design and development are presented and the mechanism is verified through a simulated experiment in an SDN testbed. The results identified that QoS performance experienced a significant percentage increase when the MBE was active. These findings contribute a novel Meter Band Rate Evaluation mechanism that extends the native capability of OpenFlow and OpenDaylight to enhance the efficiency of QoS provision