Efficient data access in mobile cloud computing

This thesis focuses on the development of efficient data transfer mechanism among mobile devices using Mobile cloud computing paradigm. Mobile cloud computing is coupling of mobile computing and cloud computing. In the Mobile cloud computing paradigm, users connect to cloud service providers over the Internet and leverage the cloud resources to perform their processing, storage and communication tasks. In this thesis, the focus is on communication tasks among mobile devices performed using Mobile cloud computing paradigm. Communication or data sharing among mobile devices is often limited by proximity of the devices. This limitation can be removed by employing Mobile cloud computing paradigm wherein each physical mobile device has a corresponding virtual machine in the cloud servers. All the computation and communication tasks can be offloaded to the virtual machines in the cloud retaining only a thin client in the physical device to display results. The exchange of data or communication between mobile devices is done through the corresponding virtual machines in the cloud. In this work, we designed a layered architecture involving mobile devices, access points and cloud server together for efficiency. We also proposed pre-distribution scheme based on this architecture for efficient data sharing among potential users with supporting data access mechanism, update propagation mechanism and cache replacement mechanisms. We also performed complexity analysis for data access using the proposed architecture and scheme. Finally, simulated the proposed architecture and scheme with actual devices and verified the efficiency of the scheme. --Abstract, page iv

Cloud computing load balancing technique with virtual machine migration / Rabiatul Addawiyah Mat Razali

The demand for cloud computing usage is speedily increasing day by day due to the facilities and advantages it offers. Such rapid growth of the large-scale computing systems usage will lead to an instant increase of Power consumption and emission of carbon by cloud platforms. In this context, a major concern of cloud computing operation is in achieving a load balanced system which can improve the platform for more efficient operations towards realizing a green cloud computing environment. The load balancing process normally emphasizes on optimal resource utilization, maximum response time, maximum throughput, and prevention of overload. In this scope of research, our study explores on the integration of load balancing process with virtual machine migration across multiple hosts which were determined by CPU utilization was implemented in this paper. There are two different types of resources, specifically low-powered and high-powered resources, which are based on Million Instructions per Second (MIPS) metrics. The minimal process execution time can be achieved if both types of resources are being evenly and efficiently matched and deploy onto suitable types of processing; i.e. low-powered or high-powered. Besides that, for a more efficient load balancing, the migration of virtual machines can be determined based on the current CPU utilization by following the thresholds where when the CPU usage reaches its 90% and 10% thresholds marker. Based on this idea, an algorithm in activating and deciding on the virtual machine migration operation is proposed, in which the overall load balancing process could be improved. Besides that, the performance of this technique is analyzed by using a Cloudsim simulator. Based on the analysis, positive results of the proposed algorithm are demonstrated, which shows the load balancing process is shown to have improved based on the distribution of virtual machines onto matching-type of resources, while a more efficient migration of virtual machines can be seen based on the defined CPU usage thresholds. Having a combination of these load balancing and migration techniques offers benefits that can avoid a long wait, make full use of resources and avoid idle resources. Minimal power consumption throughout the cloud platform will definitely lead to an efficient green cloud computing system

Energy Efficient Cloud Networks

Cloud computing is expected to be a major factor that will dominate the future Internet service model. This paper summarizes our work on energy efficiency for cloud networks. We develop a framework for studying the energy efficiency of four cloud services in IP over WDM networks: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications and infrastructure as a service (IaaS).Our approach is based on the co-optimization of both external network related factors such as whether to geographically centralize or distribute the clouds, the influence of users’ demand distribution, content popularity, access frequency and renewable energy availability and internal capability factors such as the number of servers, switches and routers as well as the amount of storage demanded in each cloud. Our investigation of the different energy efficient approaches is backed with Mixed Integer Linear Programming (MILP) models and real time heuristic

High performance cloud computing on multicore computers

The cloud has become a major computing platform, with virtualization being a key to allow applications to run and share the resources in the cloud. A wide spectrum of applications need to process large amounts of data at high speeds in the cloud, e.g., analyzing customer data to find out purchase behavior, processing location data to determine geographical trends, or mining social media data to assess brand sentiment. To achieve high performance, these applications create and use multiple threads running on multicore processors. However, existing virtualization technology cannot support the efficient execution of such applications on virtual machines, making them suffer poor and unstable performance in the cloud. Targeting multi-threaded applications, the dissertation analyzes and diagnoses their performance issues on virtual machines, and designs practical solutions to improve their performance. The dissertation makes the following contributions. First, the dissertation conducts extensive experiments with standard multicore applications, in order to evaluate the performance overhead on virtualization systems and diagnose the causing factors. Second, focusing on one main source of the performance overhead, excessive spinning, the dissertation designs and evaluates a holistic solution to make effective utilization of the hardware virtualization support in processors to reduce excessive spinning with low cost. Third, focusing on application scalability, which is the most important performance feature for multi-threaded applications, the dissertation models application scalability in virtual machines and analyzes how application scalability changes with virtualization and resource sharing. Based on the modeling and analysis, the dissertation identifies key application features and system factors that have impacts on application scalability, and reveals possible approaches for improving scalability. Forth, the dissertation explores one approach to improving application scalability by making fully utilization of virtual resources of each virtual machine. The general idea is to match the workload distribution among the virtual CPUs in a virtual machine and the virtual CPU resource of the virtual machine manager

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

HVSTO: Efficient Privacy Preserving Hybrid Storage in Cloud Data Center

In cloud data center, shared storage with good management is a main structure used for the storage of virtual machines (VM). In this paper, we proposed Hybrid VM storage (HVSTO), a privacy preserving shared storage system designed for the virtual machine storage in large-scale cloud data center. Unlike traditional shared storage, HVSTO adopts a distributed structure to preserve privacy of virtual machines, which are a threat in traditional centralized structure. To improve the performance of I/O latency in this distributed structure, we use a hybrid system to combine solid state disk and distributed storage. From the evaluation of our demonstration system, HVSTO provides a scalable and sufficient throughput for the platform as a service infrastructure.Comment: 7 pages, 8 figures, in proceeding of The Second International Workshop on Security and Privacy in Big Data (BigSecurity 2014