Live virtual machine migration aims at enabling the dynamic balanced use of the networking computing physical resources of virtualized data-centers, so to lead to reduced energy consumption. Here, we analytically characterize, prototype in software and test an optimal bandwidth manager for live migration of VMs in wireless channel. In this paper we present the optimal tunable-complexity bandwidth manager (TCBM) for the QoS live migration of VMs under a wireless channel from smartphone to access point. The goal is the minimization of the migration-induced communication energy under service level agreement (SLA)-induced hard constrains on the total migration time, downtime and overall available bandwidth.
CloneCloud @cite_4 @cite_3 : is a system that has the ability to automatically transform mobile device application in such a way that they can run into the cloud; VOLARE @cite_9 : is a middelware-based solution which allows context-aware adaptive cloud service discovery for the mobile devices. Cuckoo @cite_20 : is a computational offloading framework for mobile devices; Cloudlet @cite_12 : is a set of widely dispersed and decentralized Internet infrastructure components, with non-trivial characteristic to make available for the nearby mobile devices computing resource and storage resources; MAUI @cite_14 : is a system that is able to minimize the energy due to the VM migration by means of fine-grained offloading.
@cite_14 @cite_4 @cite_9 @cite_3 @cite_12 @cite_20
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Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to partition applications automatically at a fine granularity while optimizing execution time and energy use for a target computation and communication environment. At runtime, the application partitioning is effected by migrating a thread from the mobile device at a chosen point to the clone in the cloud, executing there for the remainder of the partition, and re-integrating the migrated thread back to the mobile device. Our evaluation shows that CloneCloud can adapt application partitioning to different environments, and can help some applications achieve as much as a 20x execution speed-up and a 20-fold decrease of energy spent on the mobile device. With the recent widespread use of smart mobile devices, as well as the increasing availability of fast and reliable wireless Internet connections for mobile devices, there is increased interest in mobile applications where the majority of the processing occurs on the server side. The flexibility, stability and scalability offered by cloud services make them an ideal architecture to use in client applications in a resource limited mobile environment. This is because mobile application usage patterns tend to be uneven, with various usage spikes according to time and location. However, the mobile setting presents a set of new challenges that cloud service discovery methods developed for non-mobile environments cannot address. The requirements a mobile client device will have from a cloud service may change due to changes in the context of the device, which may include hardware resources, environmental variables or user preferences. Binding to a service offering different quality of service levels from the ones required may lead to excess consumption of mobile resources such as battery life, as well as unnecessarily high provision costs. This paper introduces VOLARE, a middleware-based solution that monitors the resources and context of the device, and dynamically adapts cloud service requests accordingly, at discovery time or at runtime. This approach will allow for more resource-efficient and reliable cloud service discovery, as well as significant cost savings at runtime. paper describes a research in the area of mobile cloud computing. Cloud computing can be considered as a model that can provide network access to a shared pool of resources, such as storage and computing power, that can be rapidly provisioned and released with minimal management effort. The solutions discussed in this paper focus on different aspects of cloud computing in connection with mobile usage. By combining the different approaches and merging them into a common solution, it might be possible to generate a new solution that covers most of the issues currently experienced. Such a solution might have the chance to finally make cloud computing usable on mobile devices, resulting in new and interesting usage scenarios and offering execution speedups and energy savings to mobile users. Keywordscomputing, mobile devices, smart phones Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement. In this paper, a primary-secondary resource-management controller on Vehicular Networks is designed and tested. We cast the resource-management problem into a suitable constrained stochastic Network Utility Maximization problem and derive the optimal cognitive resource management controller, which dynamically allocates the access time-windows. We provide the optimal steady-state memoryless controllers under hard and soft primary-secondary collision constraints, showing as the hard controller does not present any optimality gap in the average utility with respect to the soft one, while, on the contrary, it is able to make the outage-probability vanishing. Then we generalize the framework integrating the controllers with different data fusion techniques, and test the controller behaviour in a non-stationary application scenario. Finally we provide the optimal steady-state hard controller with memory and compare it with the memoryless one.