MAPE-K Control Loop Research Articles

A component framework for the runtime enforcement of safety properties

Safety assurance of a complex system cannot be completely ensured at design/development time since most uncertainties and unknowns are revealed when the system is deployed in a real environment. Safety assurance at runtime can be addressed by using models formalizing those safety assertions the system has to guarantee during operation, and specifying enforcement strategies aimed at preserving or eventually restoring safety.This paper presents an approach to runtime safety enforcement of software systems based on the MAPE-K control loop architecture for system monitoring and control, and on the State Machine as runtime model to specify safety assertions and enforcement strategies for steering the correct system behavior. The enforcer software is designed to act as a proxy system which wraps around the software system to realize safety enforcement, both as black-box enforcement on unsafe I/O events and as gray-box enforcement on unsafe internal system changes. The proposed approach is supported by a component framework called RSE (Runtime Safety Enforcement) that is here illustrated by means of two real case studies in the health-care domain.

Online machine learning for auto-scaling in the edge computing

The evolution of edge computing devices has enabled machine intelligence techniques to process data close to its producers (the sensors) and end-users. Although edge devices are usually resource-constrained, the distribution of processing services among several nodes enables a processing capacity similar to cloud environments. However, the edge computing environment is highly dynamic, impacting the availability of nodes in the distributed system. In addition, the processing workload for each node can change constantly. Thus, the scaling of processing services needs to be rapidly adjusted, avoiding bottlenecks or wasted resources while meeting the applications’ QoS requirements. This paper presents an auto-scaling subsystem for container-based processing services using online machine learning. The auto-scaling follows the MAPE-K control loop to dynamically adjust the number of containers in response to workload changes. We designed the approach for scenarios where the number of processing requests is unknown beforehand. We developed a hybrid auto-scaling mechanism that behaves reactively while a prediction online machine learning model is continuously trained. When the prediction model reaches a desirable performance, the auto-scaling acts proactively, using predictions to anticipate scaling actions. An experimental evaluation has demonstrated the feasibility of the architecture. Our solution achieved fewer service level agreement (SLA) violations and scaling operations to meet demand than purely reactive and no scaling approaches using an actual application workload. Also, our solution wasted fewer resources compared to the other techniques.

Self-Adaptation Through Reinforcement Learning Using a Feature Model

Typically, self-adaptation is achieved by implementing the MAPE-K Control Loop. The researchers agree that multiple control loops should be assigned if the system is complex and large-scale. The hierarchical control has proven to be a good compromise to achieve SAS goals, as there is always some degree of decentralization but it also retains a degree of centralization. The decentralized entities must be coordinated to ensure the consistency of adaptation processes. The high cost of data transfer between coordinating entities may be an obstacle to achieving system scalability. To resolve this problem, coordination should only define between entities that require communication between them. However, most of the current SAS relies on static MAPE-K. In this article, authors present a new method that allows changing the structure and behavior of loops. Authors base on exploration strategies for online reinforcement learning, using the feature model to define the adaptation space.

Variability Management in Dynamic Software Product Lines for Self-Adaptive Systems—A Systematic Mapping

Context: Dynamic software product lines (DSPLs) have considerably increased their adoption for variability management for self-adaptive systems. The most widely used models for managing the variability of DSPLs are the MAPE-K control loop and context-aware feature models (CFMs). Aim: In this paper, we review and synthesize evidence of using variability constraint approaches, methodologies, and challenges for DSPL. Method: We conducted a systematic mapping, including three research questions. This study included 84 papers published from 2010 to 2021. Results: The main results show that open-dynamic variability shows a presence in 57.1% of the selected papers, and on the other hand, closed-dynamic variability appears in 38.1%. The most commonly used methodology for managing a DSPL environment is based on proprietary architectures (60.7%), where the use of CFMs predominates. For open-dynamic variability approaches, the MAPE-K control loop is mainly used. The main challenges in DSPL management are based on techniques (28.6%) and open variation (21.4%). Conclusions: Open-dynamic variability has prevailed over the years as the primary approach to managing variability in DSPL, where its primary methodology is the MAPE-K control loop. Response RQ3 requires further review.

MAPE-K/MAPE-SAC: An interaction framework for adaptive systems with security assurance cases

Security certification establishes that a given system satisfies properties and constraints as specified in the system security profile. Mechanisms and techniques have been developed to assess if and how well the system complies with the properties, thereby providing a degree of confidence in the security certification. Generally, certification of security controls defined by NIST SP800-53 is performed at design time to provide confidence in a system’s trustworthiness to achieve the organization’s mission and business requirements. Assuring confidence in a self-adaptive system’s security profile is challenging when both functional and security conditions may change at run time. Static security solutions are insufficient, given that dynamic application of defense mechanisms often needs to dynamically adapt security functionality at run time as part of self-protection. This security adaptation may hinder maintaining functional constraints or vice versa. In addition, adaptation capabilities may give rise to the need for dynamic certification, which can be a difficult procedure given the complexity of the security dependencies. Confidence in an information system’s compliance with security constraints can be expressed using security assurance cases (SACs). NIST security controls are defined with a hierarchical structure that makes them amenable to being specified in terms of SACs. A collection of SACs for related security controls form a network that can be used to measure the confidence of security compliance through certification-based evidence. Once the system is deployed, environmental and functional uncertainties may require the coordination of functional and security adaptations. This paper introduces the MAPE-SAC, a security-focused feedback control loop, and its interaction with a MAPE-K, function and performance-focused control loop, to dynamically manage run-time adaptations in response to changes in functional and security conditions. We illustrate the use of both control loops and their interaction with an example of two independent systems that need to cooperate to facilitate autonomous search and rescue in the aftermath of a natural disaster.

Towards self-optimisation in fog computing environments

In recent years, the number of smart devices has grown exponentially. However, the computational demand for latency-sensitive applications has grown and the traditional model of cloud computing is no longer able to meet alone all the needs of this type of application. In this direction, a new paradigm of computation was introduced, it is called Fog Computing. Many challenges need to be overcome, especially those regarding issues such as security, power consumption, high latency in communication with critical IoT applications, and need for QoS. We have presented a container migration mechanism to Fog and the cloud computing that supports the implementation of optimisation strategies to achieve different objectives and solutions to problems of resources allocation. In addition, our work emphasises the performance and latency optimisation, through an autonomic architecture based on the MAPE-K control loop, and provides a foundation for the analysis and optimisation architectures design to support IoT applications.

An Investigation of the Monitoring Activity in Self Adaptive Systems

Runtime monitoring is essential for the violation detection during the underlying software system execution. In this paper, an investigation of the monitoring activity of MAPE-K control loop is performed which aims at exploring:(1) the architecture of the monitoring activity in terms of the involved components and control and data flow between them; (2) the standard interface of the monitoring component with other MAPE-K components; (3) the adaptive monitoring and its importance to the monitoring overhead issue; and (4) the monitoring mode and its relevance to some specific situations and systems. This paper also presented a Java framework for the monitoring process for self adaptive systems.

Autonomic Trust Management in Cloud-Based and Highly Dynamic IOT Applications

In this paper, we propose an autonomic trust management framework for cloud based and highly dynamic Internet of Things (IoT) applications and services. IoT is creating a world where physical objects are seamlessly integrated in order to provide advanced and intelligent services for humanbeings in their day-to-day life style. Therefore, trust on IoT devices plays an important role in IoT based services and applications. Cloud computing has been changing the way how provides are looking into these issues. Many studies have proposed different techniques to address trust management although non of them addresses autonomic trust management in cloud based highly dynamic IoT systems. To our understanding, IoT cloud ecosystems help to solve many of these issues while enhancing robustness and scalability. On this basis, we came up with an autonomic trust management framework based on MAPE-K feedback control loop to evaluate the level of trust. Finally, we presents the results that verify the effectiveness of this framework.

Design Patterns for Self Adaptive Systems Engineering

Self adaptation has been proposed to overcome the complexity of today's software systems which results from the uncertainty issue. Aspects of uncertainty include changing systems goals, changing resource availability and dynamic operating conditions. Feedback control loops have been recognized as vital elements for engineering self-adaptive systems. However, despite their importance, there is still a lack of systematic way of the design of the interactions between the different components comprising one particular feedback control loop as well as the interactions between components from different control loops . Most existing approaches are either domain specific or too abstract to be useful. In addition, the issue of multiple control loops is often neglected and consequently self adaptive systems are often designed around a single loop. In this paper we propose a set of design patterns for modeling and designing self adaptive software systems based on MAPE-K Control loop of IBM architecture blueprint which takes into account the multiple control loops issue. A case study is presented to illustrate the applicability of the proposed design patterns.

A Cloud Service Broker with Legal-Rule Compliance Checking and Quality Assurance Capabilities

Abstract The ICT industry, and specifically critical sectors such as healthcare, transportation, energy and government require as mandatory the compliance of the ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, and particularly in a public cloud scenario, this compliance management issue is exacerbated by the distributed nature of the system and by the limited control of the customer on the infrastructure/services. Also if the cloud industry is aware of this legislation/regulation compliance issue (e.g. the compliance program of Amazon, Google and Microsoft Azure), right now, there are nor reference architectures neither mechanisms capable to check and to assure, off-line and at run-time, that the compliance is guaranteed during the whole life cycle of a cloud service. Cloud service brokerage can play an important role in law/regulation compliance management of cloud services. In this paper we propose a broker-based solution for the management of law/regulation compliance. In the specific first we define a reference architecture for a legislation-aware cloud service broker, and second we propose an autonomic manager that integrate the MAPE-K control loop with the LegEx framework for the management of the legal compliance checking lifecycle.