A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Monge, David. Universidad Nacional de Cuyo; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Budget-aware scheduling algorithm for scientific workflow applications across multiple clouds. A Mathematical Optimization-Based Approach

Scientific workflows have become a prevailing means of achieving significant scientific advances at an ever-increasing rate. Scheduling mechanisms and approaches are vital to automating these large-scale scientific workflows efficiently. On the other hand, with the advent of cloud computing and its easier availability and lower cost of use, more attention has been paid to the execution and scheduling of scientific workflows in this new paradigm environment. For scheduling large-scale workflows, a multi-cloud environment will typically have a more significant advantage in various computing resources than a single cloud provider. Also, the scheduling makespan and cost can be reduced if the computing resources are used optimally in a multi-cloud environment. Accordingly, this thesis addressed the problem of scientific workflow scheduling in the multi-cloud environment under budget constraints to minimize associated makespan. Furthermore, this study tries to minimize costs, including fees for running VMs and data transfer, minimize the data transfer time, and fulfill budget and resource constraints in the multi-clouds scenario. To this end, we proposed Mixed-Integer Linear Programming (MILP) models that can be solved in a reasonable time by available solvers. We divided the workflow tasks into small segments, distributed them among VMs with multi-vCPU, and formulated them in mathematical programming. In the proposed mathematical model, the objective of a problem and real and physical constraints or restrictions are formulated using exact mathematical functions. We analyzed the treatment of optimal makespan under variations in budget, workflow size, and different segment sizes. The evaluation's results signify that our proposed approach has achieved logical and expected results in meeting the set objectives

Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum

The current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.This research was funded by the European project PIACERE (Horizon 2020 research and innovation Program, under grant agreement no 101000162)

The Contemporary Review of Notable Cloud Resource Scheduling Strategies

Cloud computing has become a revolutionary development that has changed the dynamics of business for the organizations and in IT infrastructure management. While in one dimension, it has improved the scope of access, reliability, performance and operational efficiency, in the other dimension, it has created a paradigm shift in the way IT systems are managed in an organizational environment. However, with the increasing demand for cloud based solutions, there is significant need for improving the operational efficiency of the systems and cloud based services that are offered to the customers. As cloud based solutions offer finite pool of virtualized on-demand resources, there is imperative need for the service providers to focus on effective and optimal resource scheduling systems that could support them in offering reliable and timely service, workload balancing, optimal power efficiency and performance excellence. There are numerous models of resource scheduling algorithms that has been proposed in the earlier studies, and in this study the focus is upon reviewing varied range of resource scheduling algorithms that could support in improving the process efficiency. In this manuscript, the focus is upon evaluating various methods that could be adapted in terms of improving the resource scheduling solutions

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Cloud computing is a systematic delivery of computing resources as services to the consumers via the Internet. Infrastructure as a Service (IaaS) is the capability provided to the consumer by enabling smarter access to the processing, storage, networks, and other fundamental computing resources, where the consumer can deploy and run arbitrary software including operating systems and applications. The resources are sometimes available in the form of Virtual Machines (VMs). Cloud services are provided to the consumers based on the demand, and are billed accordingly. Usually, the VMs run on various datacenters, which comprise of several computing resources consuming lots of energy resulting in hazardous level of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods for reducing the energy consumption in datacenters. One such solutions are the Nature-Inspired algorithms. Towards this end, this paper presents a comprehensive review of the state-of-the-art Nature-Inspired algorithms suggested for solving the energy issues in the Cloud datacenters. A taxonomy is followed focusing on three key dimension in the literature including virtualization, consolidation, and energy-awareness. A qualitative review of each techniques is carried out considering key goal, method, advantages, and limitations. The Nature-Inspired algorithms are compared based on their features to indicate their utilization of resources and their level of energy-efficiency. Finally, potential research directions are identified in energy optimization in data centers. This review enable the researchers and professionals in Cloud computing datacenters in understanding literature evolution towards to exploring better energy-efficient methods for Cloud computing datacenters

Highly scalable algorithms for scheduling tasks and provisioning machines on heterogeneous computing systems

Includes bibliographical references.2015 Summer.As high performance computing systems increase in size, new and more efficient algorithms are needed to schedule work on the machines, understand the performance trade-offs inherent in the system, and determine which machines to provision. The extreme scale of these newer systems requires unique task scheduling algorithms that are capable of handling millions of tasks and thousands of machines. A highly scalable scheduling algorithm is developed that computes high quality schedules, especially for large problem sizes. Large-scale computing systems also consume vast amounts of electricity, leading to high operating costs. Through the use of novel resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. Trading-off energy and makespan is often difficult for companies because it is unclear how each affects the profit. A monetary-based model of high performance computing is presented and a highly scalable algorithm is developed to quickly find the schedule that maximizes the profit per unit time. As more high performance computing needs are being met with cloud computing, algorithms are needed to determine the types of machines that are best suited to a particular workload. An algorithm is designed to find the best set of computing resources to allocate to the workload that takes into account the uncertainty in the task arrival rates, task execution times, and power consumption. Reward rate, cost, failure rate, and power consumption can be optimized, as desired, to optimally trade-off these conflicting objectives

Security risk assessment in cloud computing domains

Cyber security is one of the primary concerns persistent across any computing platform. While addressing the apprehensions about security risks, an infinite amount of resources cannot be invested in mitigation measures since organizations operate under budgetary constraints. Therefore the task of performing security risk assessment is imperative to designing optimal mitigation measures, as it provides insight about the strengths and weaknesses of different assets affiliated to a computing platform. The objective of the research presented in this dissertation is to improve upon existing risk assessment frameworks and guidelines associated to different key assets of Cloud computing domains - infrastructure, applications, and users. The dissertation presents various informal approaches of performing security risk assessment which will help to identify the security risks confronted by the aforementioned assets, and utilize the results to carry out the required cost-benefit tradeoff analyses. This will be beneficial to organizations by aiding them in better comprehending the security risks their assets are exposed to and thereafter secure them by designing cost-optimal mitigation measures --Abstract, page iv

BALANCING NON-FUNCTIONAL REQUIREMENTS IN CLOUD-BASED SOFTWARE: AN APPROACH BASED ON SECURITY-AWARE DESIGN AND MULTI-OBJECTIVE SOFTWARE DYNAMIC MANAGEMENT

Beyond its functional requirements, architectural design, the quality of a software system is also defined by the degree to which it meets its non-functional requirements. The complexity of managing these non-functional requirements is exacerbated by the fact that they are potentially conflicting with one another. For cloud-based software, i.e., software whose service is delivered through a cloud infrastructure, other constraints related to the features of the hosting data center, such as cost, security and performance, have to be considered by system and software designers. For instance, the evaluation of requests to access sensitive resources results in performance overhead introduced by policy rules evaluation and message exchange between the different geographically distributed components of the authorization system. Duplicating policy rule evaluation engines traditionally solves such performance issues, however such a decision has an impact on security since it introduces additional potential private data leakage points. Taking into account all the aforementioned features is a key factor to enhance the perceived quality of service (QoS) of the cloud as a whole. Maximizing users and software developers satisfaction with cloud-based software is a challenging task since trade-off decisions have to be dynamically taken between these conflicting quality attributes to adapt to system requirements evolution. In this thesis, we tackle the challenges of building a decision support method to optimize software deployment in a cloud environment. Our proposed holistic method operates both at the level of 1) Platform as a service (PaaS) by handling software components deployment to achieve an efficient runtime optimization to satisfy cloud providers and customers objectives 2) Guest applications by making inroads into the design of applications to enable the design of secure systems that also meet flexibility, performance and cost requirements. To thoroughly investigate these challenges, we identify three main objectives that we address as follows: The first objective is to achieve a runtime optimization of cloud-based software deployment at the Platform as a service (PaaS) layer, by considering both cloud customers and providers constraints. To fulfill this objective, we leverage the [email protected] paradigm to build an abstraction layer to model a cloud infrastructure. In a second step, we model the software placement problem as a multi-objective optimization problem and we use multi-objective evolutionary algorithms (MOEAs) to identify a set of possible cloud optimal configurations that exhibit best trade-offs between conflicting objectives. The approach is validated through a case study that we defined with EBRC1, a cloud provider in Luxembourg, as a representative of a software component placement problem in heterogeneous distributed cloud nodes. The second objective is to ameliorate the convergence speed of MOEAs that we have used to achieve a run-time optimization of cloud-based software. To cope with elasticity requirements of cloud-based applications, we improve the way the search strategy operates by proposing a hyper-heuristic that operates on top of MOEAs. Our hyper-heuristic uses the history of mutation effect on fitness functions to select the most relevant mutation operators. Our evaluation shows that MOEAs in conjunction with our hyper-heuristic has a significant performance improvement in terms of resolution time over the original MOEAs. The third objective aims at optimizing cloud-based software trade-offs by exploring applications design as a complementary step to the optimization at the level of the cloud infrastructure, tackled in the first and second objectives. We aimed at achieving security trade-offs at the level of guest applications by revisiting current practices in software methods. We focus on access control as a main security concern and we opt for guest applications that manage resources regulated by access control policies specified in XACML2. This focus is mainly motivated by two key factors: 1) Access control is the pillar of computer security as it allows to protect sensitive resources in a given system from unauthorized accesses 2) XACML is the de facto standard language to specify access control policies and proposes an access control architectural model that supports several advanced access requirements such as interoperability and portability. To attain this objective, we advocate the design of applications based on XACML architectural model to achieve a trade-off between security and flexibility and we adopt a three-step approach: First, we identify a lack in the literature in XACML with obligation handling support. Obligations enable to specify user actions that have to be performed before/during/after the access to resources. We propose an extension of the XACML reference model and language to use the history of obligations states at the decision making time. In this step, we extend XACML access control architecture to support a wider range of usage control scenarios. Second, in order to avoid degrading performance while using a secure architecture based on XACML, we propose a refactoring technique applied on access control policies to enhance request evaluation time. Our approach, evaluated on three Java policy-based systems, enables to substantially reduce request evaluation time. Finally, to achieve a trade-off between a safe security policy evolution and regression testing costs, we develop a regression-test-selection approach for selecting test cases that reveal faults caused by policy changes. To sum up, in all aforementioned objectives, we pursue the goal of analysing and improving the current landscape in the development of cloud-based software. Our focus on security quality attributes is driven by its crucial role in widening the adoption of cloud computing. Our approach brings to light a security-aware design of guest applications that is based on XACML architecture. We provide useful guidelines, methods with underlying algorithms and tools for developers and cloud solution designers to enhance tomorrow’s cloud-based software design. Keywords: XACML-policy based systems, Cloud Computing, Trade-offs, Multi-Objective Optimizatio

Novel optimization schemes for service composition in the cloud using learning automata-based matrix factorization

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyService Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment