A Middleware framework for self-adaptive large scale distributed services

Modern service-oriented applications demand the ability to adapt to changing conditions and unexpected situations while maintaining a required QoS. Existing self-adaptation approaches seem inadequate to address this challenge because many of their assumptions are not met on the large-scale, highly dynamic infrastructures where these applications are generally deployed on. The main motivation of our research is to devise principles that guide the construction of large scale self-adaptive distributed services. We aim to provide sound modeling abstractions based on a clear conceptual background, and their realization as a middleware framework that supports the development of such services. Taking the inspiration from the concepts of decentralized markets in economics, we propose a solution based on three principles: emergent self-organization, utility driven behavior and model-less adaptation. Based on these principles, we designed Collectives, a middleware framework which provides a comprehensive solution for the diverse adaptation concerns that rise in the development of distributed systems. We tested the soundness and comprehensiveness of the Collectives framework by implementing eUDON, a middleware for self-adaptive web services, which we then evaluated extensively by means of a simulation model to analyze its adaptation capabilities in diverse settings. We found that eUDON exhibits the intended properties: it adapts to diverse conditions like peaks in the workload and massive failures, maintaining its QoS and using efficiently the available resources; it is highly scalable and robust; can be implemented on existing services in a non-intrusive way; and do not require any performance model of the services, their workload or the resources they use. We can conclude that our work proposes a solution for the requirements of self-adaptation in demanding usage scenarios without introducing additional complexity. In that sense, we believe we make a significant contribution towards the development of future generation service-oriented applications.Las Aplicaciones Orientadas a Servicios modernas demandan la capacidad de adaptarse a condiciones variables y situaciones inesperadas mientras mantienen un cierto nivel de servio esperado (QoS). Los enfoques de auto-adaptación existentes parecen no ser adacuados debido a sus supuestos no se cumplen en infrastructuras compartidas de gran escala. La principal motivación de nuestra investigación es inerir un conjunto de principios para guiar el desarrollo de servicios auto-adaptativos de gran escala. Nuesto objetivo es proveer abstraciones de modelaje apropiadas, basadas en un marco conceptual claro, y su implemetnacion en un middleware que soporte el desarrollo de estos servicios. Tomando como inspiración conceptos económicos de mercados decentralizados, hemos propuesto una solución basada en tres principios: auto-organización emergente, comportamiento guiado por la utilidad y adaptación sin modelos. Basados en estos principios diseñamos Collectives, un middleware que proveer una solución exhaustiva para los diversos aspectos de adaptación que surgen en el desarrollo de sistemas distribuidos. La adecuación y completitud de Collectives ha sido provada por medio de la implementación de eUDON, un middleware para servicios auto-adaptativos, el ha sido evaluado de manera exhaustiva por medio de un modelo de simulación, analizando sus propiedades de adaptación en diversos escenarios de uso. Hemos encontrado que eUDON exhibe las propiedades esperadas: se adapta a diversas condiciones como picos en la carga de trabajo o fallos masivos, mateniendo su calidad de servicio y haciendo un uso eficiente de los recusos disponibles. Es altamente escalable y robusto; puedeoo ser implementado en servicios existentes de manera no intrusiva; y no requiere la obtención de un modelo de desempeño para los servicios. Podemos concluir que nuestro trabajo nos ha permitido desarrollar una solucion que aborda los requerimientos de auto-adaptacion en escenarios de uso exigentes sin introducir complejidad adicional. En este sentido, consideramos que nuestra propuesta hace una contribución significativa hacia el desarrollo de la futura generación de aplicaciones orientadas a servicios.Postprint (published version

Learning to Dispatch Multi-Server Jobs in Bipartite Graphs with Unknown Service Rates

Multi-server jobs are imperative in modern cloud computing systems. A multi-server job has multiple components and requests multiple servers for being served. How to allocate restricted computing devices to jobs is a topic of great concern, which leads to the job scheduling and load balancing algorithms thriving. However, current job dispatching algorithms require the service rates to be changeless and knowable, which is difficult to realize in production systems. Besides, for multi-server jobs, the dispatching decision for each job component follows the All-or-Nothing property under service locality constraints and resource capacity limits, which is not well supported by mainstream algorithms. In this paper, we propose a dispatching algorithm for multi-server jobs that learns the unknown service rates and simultaneously maximizes the expected Accumulative Social Welfare (Asw). We formulate the Asw as the sum of utilities of jobs and servers achieved over each time slot. The utility of a job is proportional to the valuation for being served, which is mainly impacted by the fluctuating but unknown service rates. We maximize the Asw without knowing the exact valuations, but approximate them with exploration-exploitation. From this, we bring in several evolving statistics and maximize the statistical Asw with dynamic programming. The proposed algorithm is proved to have a polynomial complexity and a State-of-the-Art regret. We validate it with extensive simulations and the results show that the proposed algorithm outperforms several benchmark policies with improvements by up to 73%, 36%, and 28%, respectively

A distributed platform for the volunteer execution of workflows on a local area network

Thesis submitted in fulfilment of the requirements for the Degree of Master of Science in Computer ScienceAlbatroz Engineering has developed a framework for over-head power lines inspection data acquisition and analysis, which includes hardware and software. The framework’s software components include inspection data analysis and reporting tools, commonly known as PLMI2 application/platform. In PLMI2, the analysis of over-head power line maintenance inspection data consists of a sequence of Automatic Tasks (ATs) interleaved with Manual Tasks (MTs). An AT consists of a set of algorithms that receives as input one or more datasets, processes them and returns new datasets. In turn, an MT enables human supervisors (also known as lines inspection operators) to correct, improve and validate the results of ATs. ATs run faster than MTs and in the overall work cycle, ATs take less than 10% of total processing time, but still take a few minutes. There is data flow dependency among tasks, which can be modelled with a workflow and even if MTs are omitted from this workflow, it is possible to carry the sequence of ATs, postponing MTs. In fact, if the computing cost and waiting time are negligible, it may be advantageous to run ATs earlier in the workflow, prior to validation. To address this opportunity, Albatroz Engineering has invested in a new procedure to stream the data through all ATs fully unattended. Considering these scenarios, it could be useful to have a system capable of detecting available workstations at a given instant and subsequently distribute the ATs to them. In this way, operators could schedule the execution of future ATs for a given inspection data, while they are performing MTs of another. The requirements of the system to implement fall within the field Volunteer Computing Systems and we will address some of the challenges posed by these kinds of systems, namely the hosts volatility and failures. Volunteer Computing is a type of distributed computing which exploits idle CPU cycles from computing resources donated by volunteers and connected through the Internet/Intranet to compute large-scale simulations. This thesis proposes and designs a new distributed task scheduling system in the context of Volunteer Computing Systems, able to schedule the ATs of PLMI2 and exploit idle CPU cycles from workstations within the company’s local area network (LAN) to accelerate the data analysis, being aware of data flow interdependencies. To evaluate the proposed system, a prototype has been implemented, and the simulations results have shown that it is scalable and supports fault-tolerance of tasks execution, by employing the rescheduling mechanism

LBSim: A simulation system for dynamic load-balancing algorithms for distributed systems.

In a distributed system consisting of autonomous computational units, the total computational power of all the units needs to be utilized efficiently by applying suitable load-balancing policies. For accomplishing the task, a large number of load balancing algorithms have been proposed in the literature. To facilitate the performance study of each of these load-balancing strategies, simulation has been widely used. However comparison of the load balancing algorithms becomes difficult if a different simulator is used for each case. There have been few studies on generalized simulation of load-balancing algorithms in distributed systems. Most of the simulation systems address the experiments for some particular load-balancing algorithms, whereas this thesis aims to study the simulation for a broad range of algorithms. After the characterization of the distributed systems and the extraction of the common components of load-balancing algorithms, a simulation system, called LBSim, has been built. LBSim is a generalized event-driven simulator for studying load-balancing algorithms with coarse-grained applications running on distributed networks of autonomous processing nodes. In order to verify that the simulation model can represent actual systems reasonably well, we have validated LBSim both qualitatively and quantitatively. As a toolkit of simulation, LBSim programming libraries can be reused to implement load-balancing algorithms for the purpose of performance measurement and analysis from different perspectives. As a framework of algorithm simulation can be extended with a moderate effort by following object-oriented methodology, to meet any new requirements that may arise in the future.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .D8. Source: Masters Abstracts International, Volume: 43-05, page: 1747. Adviser: A. K. Aggarwal. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Intrusion Detection and Prevention Systems In the Cloud Environment

Cloud computing provides users with computing resources on demand. Despite the recent boom in adoption of cloud services, security remains an important issue. The aim of this work is to study the structure of cloud systems and propose a new security architecture in protecting cloud against attacks. This work also investigates auto-scaling and how it affects cloud computing security. Finally, this thesis studies load balancing and scheduling in cloud computing particularly when some of the workload is faulty or malicious. The first original contribution proposes a hierarchical model for intrusion detection in the cloud environment. Finite state machines (FSM) of the model were produced and verified then analyzed using probabilistic model checker. Results indicate that given certain conditions the proposed model will be in a state that efficiently utilize resources despite the presence of attack. In this part of work how cloud handles failure and its relationship to auto-scaling mechanisms within the cloud has been investigated. The second original contribution proposes a lightweight robust scheduling algorithm for load balancing in the cloud. Here some of the traffic is not reliable. Formal analysis of the algorithm were conducted and results showed that given some arrival rates of both genuine and malicious traffic average queues will stabilize, i.e. they will not grow to infinity. Experimental results studied both queues and latency, and they showed that under the same conditions naive algorithms do not stabilize. The algorithm was then extended to decentralized settings where servers maintain separate queues. In this approach when a job arrives, a dispatching algorithm is used to decide which server to send it to. Different dispatching algorithms were proposed and experimental results indicate that the new algorithms perform better than some of the existing algorithms. The results were further extended to heterogeneous (servers with different configuration) settings and it was shown that some algorithms that were stable in homogeneous setting are not stable under this setting. Simulations monitoring queue sizes confirmed that some algorithms which are stable in homogeneous setting, are not stable under this setting. It is hoped that this study with inform and enlighten cloud service providers about new ways to improve the security of the cloud in the presence of failure/attacks

Scalable and Distributed Resource Management Protocols for Cloud and Big Data Clusters

Cloud data centers require an operating system to manage resources and satisfy operational requirements and management objectives. The growth of popularity in cloud services causes the appearance of a new spectrum of services with sophisticated workload and resource management requirements. Also, data centers are growing by addition of various type of hardware to accommodate the ever-increasing requests of users. Nowadays a large percentage of cloud resources are executing data-intensive applications which need continuously changing workload fluctuations and specific resource management. To this end, cluster computing frameworks are shifting towards distributed resource management for better scalability and faster decision making. Such systems benefit from the parallelization of control and are resilient to failures. Throughout this thesis we investigate algorithms, protocols and techniques to address these challenges in large-scale data centers. We introduce a distributed resource management framework which consolidates virtual machine to as few servers as possible to reduce the energy consumption of data center and hence decrease the cost of cloud providers. This framework can characterize the workload of virtual machines and hence handle trade-off energy consumption and Service Level Agreement (SLA) of customers efficiently. The algorithm is highly scalable and requires low maintenance cost with dynamic workloads and it tries to minimize virtual machines migration costs. We also introduce a scalable and distributed probe-based scheduling algorithm for Big data analytics frameworks. This algorithm can efficiently address the problem job heterogeneity in workloads that has appeared after increasing the level of parallelism in jobs. The algorithm is massively scalable and can reduce significantly average job completion times in comparison with the-state of-the-art. Finally, we propose a probabilistic fault-tolerance technique as part of the scheduling algorithm

Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking

Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN). However, the use of distributed controllers introduces a new and important Request Dispatching (RD) problem with the goal for every SDN switch to properly dispatch their requests among all controllers so as to optimize network performance. This goal can be fulfilled by designing an RD policy to guide distribution of requests at each switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach to automatically design RD policies with high adaptability and performance. This is achieved through a new problem formulation in the form of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies show that our MA-DRL technique can effectively train RD policies to significantly outperform man-made policies, model-based policies, as well as RD policies learned via single-agent DRL algorithms