Smart execution of distributed application by balancing resources in mobile devices and cloud-based avatars

L’obiettivo del progetto di tesi svolto è quello di realizzare un servizio di livello middleware dedicato ai dispositivi mobili che sia in grado di fornire il supporto per l’offloading di codice verso una infrastruttura cloud. In particolare il progetto si concentra sulla migrazione di codice verso macchine virtuali dedicate al singolo utente. Il sistema operativo delle VMs è lo stesso utilizzato dal device mobile. Come i precedenti lavori sul computation offloading, il progetto di tesi deve garantire migliori performance in termini di tempo di esecuzione e utilizzo della batteria del dispositivo. In particolare l’obiettivo più ampio è quello di adattare il principio di computation offloading a un contesto di sistemi distribuiti mobili, migliorando non solo le performance del singolo device, ma l’esecuzione stessa dell’applicazione distribuita. Questo viene fatto tramite una gestione dinamica delle decisioni di offloading basata, non solo, sullo stato del device, ma anche sulla volontà e/o sullo stato degli altri utenti appartenenti allo stesso gruppo. Per esempio, un primo utente potrebbe influenzare le decisioni degli altri membri del gruppo specificando una determinata richiesta, come alta qualità delle informazioni, risposta rapida o basata su altre informazioni di alto livello. Il sistema fornisce ai programmatori un semplice strumento di definizione per poter creare nuove policy personalizzate e, quindi, specificare nuove regole di offloading. Per rendere il progetto accessibile ad un più ampio numero di sviluppatori gli strumenti forniti sono semplici e non richiedono specifiche conoscenze sulla tecnologia. Il sistema è stato poi testato per verificare le sue performance in termini di mecchanismi di offloading semplici. Successivamente, esso è stato anche sottoposto a dei test per verificare che la selezione di differenti policy, definite dal programmatore, portasse realmente a una ottimizzazione del parametro designato.

Streaming Support for Data Intensive Cloud-Based Sequence Analysis

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client’s site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation.

An Architecture to offer Cloud-Based Radio Access Network as a Service

This paper addresses the novel notion of offering a radio access network as a service. Its components may be instantiated on general purpose platforms with pooled resources (both radio and hardware ones) dimensioned on-demand, elastically and following the pay-per-use principle. A novel architecture is proposed that supports this concept. The architecture's success is in its modularity, well-defined functional elements and clean separation between operational and control functions. By moving much processing traditionally located in hardware for computation in the cloud, it allows the optimisation of hardware utilization and reduction of deployment and operation costs. It enables operators to upgrade their network as well as quickly deploy and adapt resources to demand. Also, new players may easily enter the market, permitting a virtual network operator to provide connectivity to its users.

A cloud-based system for measuring radiation treatment plan similarity

PURPOSE: Radiation therapy is used to treat cancer using carefully designed plans that maximize the radiation dose delivered to the target and minimize damage to healthy tissue, with the dose administered over multiple occasions. Creating treatment plans is a laborious process and presents an obstacle to more frequent replanning, which remains an unsolved problem. However, in between new plans being created, the patient's anatomy can change due to multiple factors including reduction in tumor size and loss of weight, which results in poorer patient outcomes. Cloud computing is a newer technology that is slowly being used for medical applications with promising results. The objective of this work was to design and build a system that could analyze a database of previously created treatment plans, which are stored with their associated anatomical information in studies, to find the one with the most similar anatomy to a new patient. The analyses would be performed in parallel on the cloud to decrease the computation time of finding this plan. METHODS: The system used SlicerRT, a radiation therapy toolkit for the open-source platform 3D Slicer, for its tools to perform the similarity analysis algorithm. Amazon Web Services was used for the cloud instances on which the analyses were performed, as well as for storage of the radiation therapy studies and messaging between the instances and a master local computer. A module was built in SlicerRT to provide the user with an interface to direct the system on the cloud, as well as to perform other related tasks. RESULTS: The cloud-based system out-performed previous methods of conducting the similarity analyses in terms of time, as it analyzed 100 studies in approximately 13 minutes, and produced the same similarity values as those methods. It also scaled up to larger numbers of studies to analyze in the database with a small increase in computation time of just over 2 minutes. CONCLUSION: This system successfully analyzes a large database of radiation therapy studies and finds the one that is most similar to a new patient, which represents a potential step forward in achieving feasible adaptive radiation therapy replanning.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Read-only-memory-based quantum computation: Experimental explorations using nuclear magnetic resonance and future prospects

Read-only-memory-based (ROM-based) quantum computation (QC) is an alternative to oracle-based QC. It has the advantages of being less magical, and being more suited to implementing space-efficient computation (i.e., computation using the minimum number of writable qubits). Here we consider a number of small (one- and two-qubit) quantum algorithms illustrating different aspects of ROM-based QC. They are: (a) a one-qubit algorithm to solve the Deutsch problem; (b) a one-qubit binary multiplication algorithm; (c) a two-qubit controlled binary multiplication algorithm; and (d) a two-qubit ROM-based version of the Deutsch-Jozsa algorithm. For each algorithm we present experimental verification using nuclear magnetic resonance ensemble QC. The average fidelities for the implementation were in the ranges 0.9-0.97 for the one-qubit algorithms, and 0.84-0.94 for the two-qubit algorithms. We conclude with a discussion of future prospects for ROM-based quantum computation. We propose a four-qubit algorithm, using Grover's iterate, for solving a miniature real-world problem relating to the lengths of paths in a network.

A logical foundation for session-based concurrent computation

Linear logic has long been heralded for its potential of providing a logical basis for concurrency. While over the years many research attempts were made in this regard, a Curry-Howard correspondence between linear logic and concurrent computation was only found recently, bridging the proof theory of linear logic and session-typed process calculus. Building upon this work, we have developed a theory of intuitionistic linear logic as a logical foundation for session-based concurrent computation, exploring several concurrency related phenomena such as value-dependent session types and polymorphic sessions within our logical framework in an arguably clean and elegant way, establishing with relative ease strong typing guarantees due to the logical basis, which ensure the fundamental properties of type preservation and global progress, entailing the absence of deadlocks in communication. We develop a general purpose concurrent programming language based on the logical interpretation, combining functional programming with a concurrent, session-based process layer through the form of a contextual monad, preserving our strong typing guarantees of type preservation and deadlock-freedom in the presence of general recursion and higher-order process communication. We introduce a notion of linear logical relations for session typed concurrent processes, developing an arguably uniform technique for reasoning about sophisticated properties of session-based concurrent computation such as termination or equivalence based on our logical approach, further supporting our goal of establishing intuitionistic linear logic as a logical foundation for sessionbased concurrency.

Empirical study on the adoption, use and effects of cloud-based testing

Cloud computing is a practically relevant paradigm in computing today. Testing is one of the distinct areas where cloud computing can be applied. This study addressed the applicability of cloud computing for testing within organizational and strategic contexts. The study focused on issues related to the adoption, use and effects of cloudbased testing. The study applied empirical research methods. The data was collected through interviews with practitioners from 30 organizations and was analysed using the grounded theory method. The research process consisted of four phases. The first phase studied the definitions and perceptions related to cloud-based testing. The second phase observed cloud-based testing in real-life practice. The third phase analysed quality in the context of cloud application development. The fourth phase studied the applicability of cloud computing in the gaming industry. The results showed that cloud computing is relevant and applicable for testing and application development, as well as other areas, e.g., game development. The research identified the benefits, challenges, requirements and effects of cloud-based testing; and formulated a roadmap and strategy for adopting cloud-based testing. The study also explored quality issues in cloud application development. As a special case, the research included a study on applicability of cloud computing in game development. The results can be used by companies to enhance the processes for managing cloudbased testing, evaluating practical cloud-based testing work and assessing the appropriateness of cloud-based testing for specific testing needs.

Cloud based global positioning system as a safety monitor for dementia patients

The aim of using GPS for Alzheimer's Patients is to give carers and families of those affected by Alzheimer's Disease, as well as all the other dementia related conditions, a service that can, via SMS text message, notify them should their loved one leave their home. Through a custom website, it enables the carer to remotely manage a contour boundary that is specifically assigned to the patient as well as the telephone numbers of the carers. The technique makes liberal use of such as Google Maps.

Exact dynamics of single-qubit-gate fidelities under the measurement-based quantum computation scheme

Measurement-based quantum computation is an efficient model to perform universal computation. Nevertheless, theoretical questions have been raised, mainly with respect to realistic noise conditions. In order to shed some light on this issue, we evaluate the exact dynamics of some single-qubit-gate fidelities using the measurement-based quantum computation scheme when the qubits which are used as a resource interact with a common dephasing environment. We report a necessary condition for the fidelity dynamics of a general pure N-qubit state, interacting with this type of error channel, to present an oscillatory behavior, and we show that for the initial canonical cluster state, the fidelity oscillates as a function of time. This state fidelity oscillatory behavior brings significant variations to the values of the computational results of a generic gate acting on that state depending on the instants we choose to apply our set of projective measurements. As we shall see, considering some specific gates that are frequently found in the literature, the fast application of the set of projective measurements does not necessarily imply high gate fidelity, and likewise the slow application thereof does not necessarily imply low gate fidelity. Our condition for the occurrence of the fidelity oscillatory behavior shows that the oscillation presented by the cluster state is due exclusively to its initial geometry. Other states that can be used as resources for measurement-based quantum computation can present the same initial geometrical condition. Therefore, it is very important for the present scheme to know when the fidelity of a particular resource state will oscillate in time and, if this is the case, what are the best times to perform the measurements.

Exact dynamics of single-qubit-gate fidelities under the measurement-based quantum computation scheme

Measurement-based quantum computation is an efficient model to perform universal computation. Nevertheless, theoretical questions have been raised, mainly with respect to realistic noise conditions. In order to shed some light on this issue, we evaluate the exact dynamics of some single-qubit-gate fidelities using the measurement-based quantum computation scheme when the qubits which are used as a resource interact with a common dephasing environment. We report a necessary condition for the fidelity dynamics of a general pure N-qubit state, interacting with this type of error channel, to present an oscillatory behavior, and we show that for the initial canonical cluster state, the fidelity oscillates as a function of time. This state fidelity oscillatory behavior brings significant variations to the values of the computational results of a generic gate acting on that state depending on the instants we choose to apply our set of projective measurements. As we shall see, considering some specific gates that are frequently found in the literature, the fast application of the set of projective measurements does not necessarily imply high gate fidelity, and likewise the slow application thereof does not necessarily imply low gate fidelity. Our condition for the occurrence of the fidelity oscillatory behavior shows that the oscillation presented by the cluster state is due exclusively to its initial geometry. Other states that can be used as resources for measurement-based quantum computation can present the same initial geometrical condition. Therefore, it is very important for the present scheme to know when the fidelity of a particular resource state will oscillate in time and, if this is the case, what are the best times to perform the measurements.

SLA-driven predictive orchestration for distributed cloud-based mobile services

We describe a system for performing SLA-driven management and orchestration of distributed infrastructures composed of services supporting mobile computing use cases. In particular, we focus on a Follow-Me Cloud scenario in which we consider mobile users accessing cloud-enable services. We combine a SLA-driven approach to infrastructure optimization, with forecast-based performance degradation preventive actions and pattern detection for supporting mobile cloud infrastructure management. We present our system's information model and architecture including the algorithmic support and the proposed scenarios for system evaluation.