Today, it is a major issue for mobile applications to maintain a replica state of the server on mobile devices. This creates the need to keep data on both the server and the mobile. In such cases, when the data changes on the server, the new state of the data has to be updated on the mobile in order to maintain a consistent view of the data flow. However, mobile devices communicate over wireless mediums (.e.g., Bluetooth, Wi-Fi, 3.5G/4G, etc.) which can experience intermittent connectivity. The volatility of the network is also influenced by low-bandwidth. The direct effects of these issues are high latency and inconsistency issues between the data on the mobile clients and the remote servers. In this work, I present a detail review on the topic of data synchronization in mobile networks. Then, a generic architecture called MobiQ is proposed which can keep working in an offline mode to record local modifications and can synchronize with the remote servers when connectivity is restored. This is achieved through the proposal of an efficient synchronization protocol which combines different synchronization and replication strategies. Moreover, the MobiQ framework provides a secured environment to work with data. The implemented architecture is designed and tested in mobile questionnaire system and the result is encouraging.

This thesis considers mobile computation offloading where task completion times are subject to hard deadline constraints. Hard deadlines are difficult to meet in conventional computation offloading due to the stochastic nature of the wireless channels involved. Rather than using binary offload decisions, we permit concurrent remote and local job execution when it is needed to ensure task completion deadlines. The thesis addresses this problem for homogeneous Markovian wireless channels. Two online energy-optimal computation offloading algorithms, OnOpt and MultiOpt, are proposed. OnOpt uploads the job to the server continuously and MultiOpt uploads the job in separate parts, each of which requires a separate offload initiation decision. The energy optimality of the algorithms is shown by constructing a time-dilated absorbing Markov process and applying dynamic programming. Closed form results are derived for general Markovian channels. The Gilbert-Elliott channel model is used to show how a particular Markov chain structure can be exploited to compute optimal offload initiation times more efficiently. The performance of the proposed algorithms is compared to three others, namely, Immediate Offloading, Channel Threshold, and Local Execution. Performance results show that the proposed algorithms can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines. / Thesis / Master of Applied Science (MASc)

This thesis investigates energy fairness in an environment where multiple mobile cloud computing users are attempting to utilize both a shared channel and a shared server to offload jobs to remote computation resources, a technique known as mobile computation offloading. This offloading is done in an effort to reduce energy consumption at the mobile device, which has been demonstrated to be highly effective in previous work. However, insufficient resources are available for all mobile devices to offload all generated jobs due to constraints at the shared channel and server. In addition to these constraints, certain mobile devices are at a disadvantage relative to others in their achievable offloading rate. Hence, the shared resources are not necessarily shared fairly, and an effort must be made to do so. A method for improving offloading fairness in terms of total energy is derived, in which the state of the queue of jobs waiting for offloading is evaluated in an online fashion, at each job arrival, in order to inform an offloading decision for that newest arrival; no prior state or future predictions are used to determine the optimal decision. This algorithm is evaluated by comparing it on several criteria to standard scheduling methods, as well as to an optimal offline (i.e., non-causal) schedule derived from the solution of a min-max energy integer linear program. Various results derived by simulation demonstrate the improvements in energy fairness achieved. / Thesis / Master of Applied Science (MASc)

Mobile devices are the dominant interface for interacting with online services as well as an efficient platform for cloud data consumption. Cloud computing allows the delivery of applications/functionalities as services over the internet and provides the software/hardware infrastructure to host these services in a scalable manner. In mobile cloud computing, the apps running on the mobile device use cloud hosted services to overcome resource constraints of the host device. This approach allows mobile devices to outsource the resource-consuming tasks. Furthermore, as the number of devices owned by a single user increases, there is the growing demand for cross-platform application deployment to ensure a consistent user experience. However, the mobile devices communicate through unstable wireless networks, to access the data and services hosted in the cloud. The major challenges that mobile clients face when accessing services hosted in the cloud, are network latency and synchronization of data. To address the above mentioned challenges, this research proposed an architecture which introduced a policy-based middleware that supports user to access cloud hosted digital assets and services via an application across multiple mobile devices in a seamless manner. The major contribution of this thesis is identifying different information, used to configure the behavior of the middleware towards reliable and consistent communication among mobile clients and the cloud hosted services. Finally, the advantages of the using policy-based middleware architecture are illustrated by experiments conducted on a proof-of-concept prototype.

Mobile computation offloading (MCO) can alleviate the hardware limitations of mobile devices by migrating heavy computational tasks from mobile devices to more powerful cloud servers. This can lead to better performance and energy savings for the mobile devices. This thesis considers MCO over stochastic wireless channels when task completion times are subject to hard deadline constraints. Hard deadlines, however, are difficult to meet in conventional computation offloading due to the randomness caused by the wireless channels. In the proposed offloading policies, concurrent local execution (CLE) is used to guarantee task execution time constraints. By sometimes allowing simultaneous local and remote execution, CLE ensures that job deadlines are always satisfied in the face of any unexpected wireless channel conditions. The thesis introduces online optimal algorithms that reduce the remote and local execution overlap so that energy wastage is minimized. Markov processes are used to model the communication channels. MCO is addressed for three different job offloading schemes: continuous, multi-part, and preemptive. In the case of continuous offloading, referred to as 1-Part offloading, the mobile device will upload the entire job in one piece without interruption, when the scheduler decides to do so. In multi-part computation offloading, the job is partitioned into a known number (K) of parts, and each part is uploaded separately. In this offloading mechanism, which is referred to as K-Part Offloading, the upload initiation times of each part must be determined dynamically during runtime, and there may be waiting time periods between consecutive upload parts. Preemptive offloading is a generalization of K-Part Offloading where the number of task upload parts is unknown. In this scheme, a decision to either continue offloading or to temporarily interrupt the offload is made at the start of each time slot. Compared to the conventional contiguous computation offloading, interrupted offloading mechanisms (i.e., K-Part and preemptive offloading) allow the system to adapt when channel conditions change and therefore may result in lower mobile device energy consumption. This energy reduction will be obtained at the expense of having higher computational complexity. In this thesis, for each offloading scheme, an online computation offloading algorithm is introduced by constructing a time-dilated absorbing Markov chain (TDAMC) and applying dynamic programming (DP). These algorithms are shown to be energy-optimal while ensuring that the hard task deadline constraints are always satisfied. The optimality of these algorithms is proved using Markovian decision process stopping theory. Since the computational complexity of the proposed online algorithms, especially in the case of preemptive offloading, can be significant, three simpler and computationally efficient approximation methods are introduced: Markovian Compression (MC), Time Compression (TC), and Preemption Using Continuous Offloading (Preemption-CO). MC and TC reduce the state space of the offloading Markovian process by using a novel notion of geometric similarity or by running an optimal online offloading algorithm in periodic time steps. In Preemption-CO, while a task is offloaded preemptively, the offloading decision at every time-slot is based on non-preemptive calculations. These methods are used alone or in combination to construct practical offloading algorithms. A variety of results are presented that show the tradeoffs between complexity and mobile energy-saving performance for the different algorithms. / Thesis / Doctor of Philosophy (PhD)

In this thesis, we compare the energy efficiency of various strategies of mobile computation offloading over stochastic transmission channels where the task completion time is subject to a latency constraint. In the proposed methods, finite-state Markov chains are used to model the wireless channels between the mobile devices and the remote servers. We analyze the mechanisms of efficient mobile computation offloading under both soft and hard latency constraints. For the case of soft latency constraint, the task completion could miss the deadline below a specified probability threshold. On the other hand, under a hard deadline constraint, the task execution result must be available at the mobile device before the deadline. In order to make sure the task completes before the hard deadline, the hard deadline constraint approach requires concurrent execution in both local and cloud in specific circumstances. The closed-form solutions are often obtained using the broad Markov processes. The GE (Gilbert-Elliott) model is a more efficient method for demonstrating how the Markov chain’s structure can be used to compute the best offload initiation (Hekmati et al., 2019a).The effectiveness of the algorithms is studied under various deadline constraints and offloading methods. In this thesis, six algorithms are assessed in various scenarios. 1) Single user optimal (Zhang et al., 2013), 2) LARAC (Lagrangian Relaxation Based Aggregated Cost) (Zhang et al., 2014), 3) OnOpt (Online Optimal) algorithm (Hekmati et al., 2019a), 4) Water-Filling With Equilibrium (WF-Equ), 5) Water-Filling With Exponentiation (WFExp) (Teymoori et al., 2021), 6) MultiOPT (Multi-Decision Online Optimal). The experiment demonstrates that the studied algorithms perform dramatically different in the same setting. The various types of deadline restrictions also affect how efficiently the algorithms use energy. The experiment also highlights the trade-off between computational complexities and mobile energy savings (Teymoori et al., 2021).

COSTA, Philipp Bernardino. Uma abordagem para offloading em múltiplas plataformas móveis. 2014. 104 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2014. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-12T15:14:02Z No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-20T13:52:31Z (GMT) No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) / Made available in DSpace on 2016-07-20T13:52:31Z (GMT). No. of bitstreams: 1 2014_dis_pbcosta.pdf: 2579064 bytes, checksum: 96c47daf2c7ccea16a7e2f6945cb1a7a (MD5) Previous issue date: 2014 / The mobile devices, like smartphones and tablets, have evolved considerably in last years in computational terms. Despite advances in their hardware, these devices have energy constraints regarded to their poor computing performance. Therefore, on this context, a new paradigm called Mobile Cloud Computing (MCC) has emerged. MCC studies new ways to extend the computational and energy resources, on mobile devices using the offloading techniques. A literature survey about MCC, has shown that there is no support heterogeneity on reported studies. In response, we propose a framework called MpOS (Multi-platform Offloading System), which supports the offloading technique in mobile application development, for two mobile platforms (Android and Windows Phone). Two case studies were developed with MpOS solution in order to evaluate the framework for each mobile platform. These case studies show how the offloading technique works on several perspectives. In BenchImage experiment, the offloading performance was analyzed, concerning to its execution on a remote execution site (a cloudlet on local network and public cloud in the Internet). The Collision application promotes the analysis of the offloading technique performance on real-time application, also using different serialization systems. In both experiments, results show some situations where it was better to run locally on smarphone, than performing the offloading operation and vice versa. / Os dispositivos móveis, especificamente os smartphones e os tablets, evoluíram bastante em termos computacionais nos últimos anos, e estão cada vez mais presentes no cotidiano das pessoas. Apesar dos avanços tecnológicos, a principal limitação desses dispositivos está relacionada com a questão energética e com seu baixo desempenho computacional, quando comparado com um notebook ou computador de mesa. Com base nesse contexto, surgiu o paradigma do Mobile Cloud Computing (MCC), o qual estuda formas de estender os recursos computacionais e energéticos dos dispositivos móveis através da utilização das técnicas de offloading. A partir do levantamento bibliográfico dos frameworks em MCC verificou-se, para o problema da heterogeneidade em plataformas móveis, ausência de soluções de offloading. Diante deste problema, esta dissertação apresenta um framework denominado de MpOS (Multiplataform Offloading System), que suporta a técnica de offloading, em relação ao desenvolvimento de aplicações para diferentes plataformas móveis, sendo desenvolvido inicialmente para as plataformas Android e Windows Phone. Para validação foram desenvolvidas para cada plataforma móvel, duas aplicações móveis, denominadas de BenchImage e Collision, que demonstram o funcionamento da técnica de offloading em diversos cenários. No caso do experimento realizado com BenchImage foi analisado o desempenho da aplicação móvel, em relação à execução local, no cloudlet server e em uma nuvem pública na Internet, enquanto no experimento do Collision (um aplicativo de tempo real) foi analisado o desempenho do offloading, utilizando também diferentes sistemas de serialização de dados. Em ambos os experimentos houve situações que era mais vantajoso executar localmente no smartphone, do que realizar a operação de offloading e vice-versa, por causa de diversos fatores associados com a qualidade da rede e com volume de processamento exigido nesta operação.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Os dispositivos mÃveis, especificamente os smartphones e os tablets, evoluÃram bastante em termos computacionais nos Ãltimos anos, e estÃo cada vez mais presentes no cotidiano das pessoas. Apesar dos avanÃos tecnolÃgicos, a principal limitaÃÃo desses dispositivos està relacionada com a questÃo energÃtica e com seu baixo desempenho computacional, quando comparado com um notebook ou computador de mesa. Com base nesse contexto, surgiu o paradigma do Mobile Cloud Computing (MCC), o qual estuda formas de estender os recursos computacionais e energÃticos dos dispositivos mÃveis atravÃs da utilizaÃÃo das tÃcnicas de offloading. A partir do levantamento bibliogrÃfico dos frameworks em MCC verificou-se, para o problema da heterogeneidade em plataformas mÃveis, ausÃncia de soluÃÃes de offloading. Diante deste problema, esta dissertaÃÃo apresenta um framework denominado de MpOS (Multiplataform Offloading System), que suporta a tÃcnica de offloading, em relaÃÃo ao desenvolvimento de aplicaÃÃes para diferentes plataformas mÃveis, sendo desenvolvido inicialmente para as plataformas Android e Windows Phone. Para validaÃÃo foram desenvolvidas para cada plataforma mÃvel, duas aplicaÃÃes mÃveis, denominadas de BenchImage e Collision, que demonstram o funcionamento da tÃcnica de offloading em diversos cenÃrios. No caso do experimento realizado com BenchImage foi analisado o desempenho da aplicaÃÃo mÃvel, em relaÃÃo à execuÃÃo local, no cloudlet server e em uma nuvem pÃblica na Internet, enquanto no experimento do Collision (um aplicativo de tempo real) foi analisado o desempenho do offloading, utilizando tambÃm diferentes sistemas de serializaÃÃo de dados. Em ambos os experimentos houve situaÃÃes que era mais vantajoso executar localmente no smartphone, do que realizar a operaÃÃo de offloading e vice-versa, por causa de diversos fatores associados com a qualidade da rede e com volume de processamento exigido nesta operaÃÃo. / The mobile devices, like smartphones and tablets, have evolved considerably in last years in computational terms. Despite advances in their hardware, these devices have energy constraints regarded to their poor computing performance. Therefore, on this context, a new paradigm called Mobile Cloud Computing (MCC) has emerged. MCC studies new ways to extend the computational and energy resources, on mobile devices using the offloading techniques. A literature survey about MCC, has shown that there is no support heterogeneity on reported studies. In response, we propose a framework called MpOS (Multi-platform Offloading System), which supports the offloading technique in mobile application development, for two mobile platforms (Android and Windows Phone). Two case studies were developed with MpOS solution in order to evaluate the framework for each mobile platform. These case studies show how the offloading technique works on several perspectives. In BenchImage experiment, the offloading performance was analyzed, concerning to its execution on a remote execution site (a cloudlet on local network and public cloud in the Internet). The Collision application promotes the analysis of the offloading technique performance on real-time application, also using different serialization systems. In both experiments, results show some situations where it was better to run locally on smarphone, than performing the offloading operation and vice versa.

Recently, companies are adopting Mobile Cloud Computing (MCC) to efficiently deliver enterprise services to users (or consumers) on their personalized devices. MCC is the facilitation of mobile devices (e.g., smartphones, tablets, notebooks, and smart watches) to access virtualized services such as software applications, servers, storage, and network services over the Internet. With the advancement and diversity of the mobile landscape, there has been a growing trend in consumer attitude where a single user owns multiple mobile devices. This paradigm of supporting a single user or consumer to access multiple services from n-devices is referred to as the Ubiquitous Cloud Computing (UCC) or the Personal Cloud Computing. In the UCC era, consumers expect to have application and data consistency across their multiple devices and in real time. However, this expectation can be hindered by the intermittent loss of connectivity in wireless networks, user mobility, and peak load demands. Hence, this dissertation presents an architectural framework called, Cloud Services Brokerage for Mobile Ubiquitous Cloud Computing (CSB-UCC), which ensures soft real-time and reliable services consumption on multiple devices of users. The CSB-UCC acts as an application middleware broker that connects the n-devices of users to the multi-cloud services. The designed system determines the multi-cloud services based on the user's subscriptions and the n-devices are determined through device registration on the broker. The preliminary evaluations of the designed system shows that the following are achieved: 1) high scalability through the adoption of a distributed architecture of the brokerage service, 2) providing soft real-time application synchronization for consistent user experience through an enhanced mobile-to-cloud proximity-based access technique, 3) reliable error recovery from system failure through transactional services re-assignment to active nodes, and 4) transparent audit trail through access-level and context-centric provenance.

Handheld devices are becoming increasingly common, and they have varied range of resources. Mobile Cloud Computing (MCC) allows resource constrained devices to offload computation and use storage capacities of more resourceful surrogate machines. This enables creation of new and interesting applications for all devices. We propose a scheme that constructs a high-performance de-centralized system by a group of volunteer mobile devices which come together to form a resourceful unit (cloudlet). The idea is to design a model to operate as a public-resource between mobile devices in close geographical proximity. This cloudlet can provide larger storage capability and can be used as a computational resource by other devices in the network. The system needs to watch the movement of the participating nodes and restructure the topology if some nodes that are providing support to the cloudlet fail or move out of the network. In this work, we discuss the need of the system, our goals and design issues in building a scalable and reconfigurable system. We achieve this by leveraging the concept of virtual dominating set to create an overlay in the broads of the network and distribute the responsibilities in hosting a cloudlet server. We propose an architecture for such a system and develop algorithms that are requited for its operation. We map the resources available in the network by first scoring each device individually, and then gathering these scores to determine suitable candidate cloudlet nodes. We have simulated cloudlet functionalities for several scenarios and show that our approach is viable alternative for many applications such as sharing GPS, crowd sourcing, natural language processing, etc.