Robustness-Driven Resilience Evaluation of Self-Adaptive Software Systems

An increasingly important requirement for certain classes of software-intensive systems is the ability to self-adapt their structure and behavior at run-time when reacting to changes that may occur to the system, its environment, or its goals. A major challenge related to self-adaptive software systems is the ability to provide assurances of their resilience when facing changes. Since in these systems, the components that act as controllers of a target system incorporate highly complex software, there is the need to analyze the impact that controller failures might have on the services delivered by the system. In this paper, we present a novel approach for evaluating the resilience of self-adaptive software systems by applying robustness testing techniques to the controller to uncover failures that can affect system resilience. The approach for evaluating resilience, which is based on probabilistic model checking, quantifies the probability of satisfaction of system properties when the target system is subject to controller failures. The feasibility of the proposed approach is evaluated in the context of an industrial middleware system used to monitor and manage highly populated networks of devices, which was implemented using the Rainbow framework for architecture-based self-adaptation

A fine-grain time-sharing Time Warp system

Although Parallel Discrete Event Simulation (PDES) platforms relying on the Time Warp (optimistic) synchronization protocol already allow for exploiting parallelism, several techniques have been proposed to further favor performance. Among them we can mention optimized approaches for state restore, as well as techniques for load balancing or (dynamically) controlling the speculation degree, the latter being specifically targeted at reducing the incidence of causality errors leading to waste of computation. However, in state of the art Time Warp systems, events’ processing is not preemptable, which may prevent the possibility to promptly react to the injection of higher priority (say lower timestamp) events. Delaying the processing of these events may, in turn, give rise to higher incidence of incorrect speculation. In this article we present the design and realization of a fine-grain time-sharing Time Warp system, to be run on multi-core Linux machines, which makes systematic use of event preemption in order to dynamically reassign the CPU to higher priority events/tasks. Our proposal is based on a truly dual mode execution, application vs platform, which includes a timer-interrupt based support for bringing control back to platform mode for possible CPU reassignment according to very fine grain periods. The latter facility is offered by an ad-hoc timer-interrupt management module for Linux, which we release, together with the overall time-sharing support, within the open source ROOT-Sim platform. An experimental assessment based on the classical PHOLD benchmark and two real world models is presented, which shows how our proposal effectively leads to the reduction of the incidence of causality errors, as compared to traditional Time Warp, especially when running with higher degrees of parallelism

Dependence-driven techniques in system design

Burstiness in workloads is often found in multi-tier architectures, storage systems, and communication networks. This feature is extremely important in system design because it can significantly degrade system performance and availability. This dissertation focuses on how to use knowledge of burstiness to develop new techniques and tools for performance prediction, scheduling, and resource allocation under bursty workload conditions.;For multi-tier enterprise systems, burstiness in the service times is catastrophic for performance. Via detailed experimentation, we identify the cause of performance degradation on the persistent bottleneck switch among various servers. This results in an unstable behavior that cannot be captured by existing capacity planning models. In this dissertation, beyond identifying the cause and effects of bottleneck switch in multi-tier systems, we also propose modifications to the classic TPC-W benchmark to emulate bursty arrivals in multi-tier systems.;This dissertation also demonstrates how burstiness can be used to improve system performance. Two dependence-driven scheduling policies, SWAP and ALoC, are developed. These general scheduling policies counteract burstiness in workloads and maintain high availability by delaying selected requests that contribute to burstiness. Extensive experiments show that both SWAP and ALoC achieve good estimates of service times based on the knowledge of burstiness in the service process. as a result, SWAP successfully approximates the shortest job first (SJF) scheduling without requiring a priori information of job service times. ALoC adaptively controls system load by infinitely delaying only a small fraction of the incoming requests.;The knowledge of burstiness can also be used to forecast the length of idle intervals in storage systems. In practice, background activities are scheduled during system idle times. The scheduling of background jobs is crucial in terms of the performance degradation of foreground jobs and the utilization of idle times. In this dissertation, new background scheduling schemes are designed to determine when and for how long idle times can be used for serving background jobs, without violating predefined performance targets of foreground jobs. Extensive trace-driven simulation results illustrate that the proposed schemes are effective and robust in a wide range of system conditions. Furthermore, if there is burstiness within idle times, then maintenance features like disk scrubbing and intra-disk data redundancy can be successfully scheduled as background activities during idle times

A Praise for Defensive Programming: Leveraging Uncertainty for Effective Malware Mitigation

A promising avenue for improving the effectiveness of behavioral-based malware detectors would be to combine fast traditional machine learning detectors with high-accuracy, but time-consuming deep learning models. The main idea would be to place software receiving borderline classifications by traditional machine learning methods in an environment where uncertainty is added, while software is analyzed by more time-consuming deep learning models. The goal of uncertainty would be to rate-limit actions of potential malware during the time consuming deep analysis. In this paper, we present a detailed description of the analysis and implementation of CHAMELEON, a framework for realizing this uncertain environment for Linux. CHAMELEON offers two environments for software: (i) standard - for any software identified as benign by conventional machine learning methods and (ii) uncertain - for software receiving borderline classifications when analyzed by these conventional machine learning methods. The uncertain environment adds obstacles to software execution through random perturbations applied probabilistically on selected system calls. We evaluated CHAMELEON with 113 applications and 100 malware samples for Linux. Our results showed that at threshold 10%, intrusive and non-intrusive strategies caused approximately 65% of malware to fail accomplishing their tasks, while approximately 30% of the analyzed benign software to meet with various levels of disruption. With a dynamic, per-system call threshold, CHAMELEON caused 92% of the malware to fail, and only 10% of the benign software to be disrupted. We also found that I/O-bound software was three times more affected by uncertainty than CPU-bound software. Further, we analyzed the logs of software crashed with non-intrusive strategies, and found that some crashes are due to the software bugs

The management of academic workloads: full report on findings

The pressures on UK higher education (from explicit competition and growth in student numbers, to severe regulatory demands) are greater than ever, and have resulted in a steady increase in measures taken by universities to actively manage their finances and overall quality. These pressures are also likely to have impacted on staff and, indeed, recent large surveys in the sector have indicated that almost half of respondents find their workloads unmanageable. Against this background it would seem logical that the emphasis on institutional interventions to improve finance and quality, should be matched by similar attention given to the allocation of workloads to staff, and a focus on how best to utilise people’s time - the single biggest resource available within universities. Thus the aim of this piece of research was to focus on the processes and practices surrounding the allocation of staff workloads within higher education. Ten diverse organisations were selected for study: six universities in the UK, two overseas universities and two non higher education (but knowledge-intensive) organisations. In each, a crosssection of staff was selected, and in-depth interviews carried out. A total of 59 such interviews were carried out across the ten organisations. By identifying typical practices, as well as interesting alternatives, views on the various strengths and weaknesses of each of their workload allocation approaches was collated; and associated factors requiring attention identified. Through an extensive process of analysis, approaches which promoted more equitable loads for individuals, and which might provide synergies for institutions were also investigated

Calidad de servicio en computación en la nube: técnicas de modelado y sus aplicaciones

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management

Management and Organizational Influences on the Compliance Behavior of Employees to Reduce Non-malicious IT Misuse Intention

The widespread use of information technology and information systems (IT) throughout corporations, too often includes employees who choose not to follow the stated policies and procedures in performing their job tasks. In many cases, this encompasses employees who mean no harm, but choose not to comply with IT policies and procedures. The present study frames such compliance behavior as non-malicious IT misuse. Non-malicious IT misuse by an employee occurs when the employee improvises, takes short cuts, or works around IT procedures and guidelines in order to perform their assigned tasks. As expressed, they do not intend to cause internal control or compliance problems but may simply want to meet their assigned task objectives with the use of IT applications. Studies usually address this phenomenon with deterrence and punishment/reward theories, but literature suggests additional theoretical approaches to further understand non-malicious IT misuse. This study proposes management driven policy approaches, along with organizational factors to reduce intention of non-malicious IT misuse

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

Model-Based Dynamic Resource Management for Service Oriented Clouds

Cloud computing is a flexible platform for software as a service, as more and more applications are deployed on cloud. Major challenges in cloud include how to characterize the workload of the applications and how to manage the cloud resources efficiently by sharing them among many applications. The current state of the art considers a simplified model of the system, either ignoring the software components altogether or ignoring the relationship between individual software services. This thesis considers the following resource management problems for cloud-based service providers: (i) how to estimate the parameters of the current workload, (ii) how to meet Quality of Service (QoS) targets while minimizing infrastructure cost, (iii) how to allocate resources considering performance costs of virtual machine reconfigurations. To address the above problems, we propose a model-based feedback loop approach. The cloud infrastructure, the services, and the applications are modelled using Layered Queuing Models (LQM). These models are then optimized. Mathematical techniques are used to reduce the complexity of the models and address the scalability issues. The main contributions of this thesis are: (i) Extended Kalman Filter (EKF) based techniques improved by dynamic clustering for scalable estimation of workload parameters, (ii) combination of adaptive empirical models (tuned during runtime) and stepwise optimizations for improving the overall allocation performance, (iii) dynamic service placement algorithms that consider the cost of virtual machine reconfiguration