Marin Litoiu

Engineering Self-Adaptive Systems through Feedback Loops

To deal with the increasing complexity of software systems and uncertainty of their environments, software engineers have turned to self-adaptivity. Self-adaptive systems are capable of dealing with a continuously changing environment and emerging requirements that may be unknown at design-time. However, building such systems cost-effectively and in a predictable manner is a major engineering challenge. In this paper, we explore the state-of-the-art in engineering self-adaptive systems and identify potential improvements in the design process. Our most important finding is that in designing self-adaptive systems, the feedback loops that control self-adaptation must become first-class entities. We explore feedback loops from the perspective of control engineering and within existing self-adaptive systems in nature and biology. Finally, we identify the critical challenges our community must address to enable systematic and well-organized engineering of self-adaptive and self-managing software systems.

Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Performance Model Estimation and Tracking Using Optimal Filters

To update a performance model, its parameter values must be updated, and in some applications (such as autonomic systems) tracked continuously over time. Direct measurement of many parameters during system operation requires instrumentation which is impractical. Kalman filter estimators can track such parameters using other data such as response times and utilizations, which are readily observable. This paper adapts Kalman filter estimators for performance model parameters, evaluates the approximations which must be made, and develops a systematic approach to setting up an estimator. The estimator converges under easily verified conditions. Different queueing-based models are considered here, and the extension for state-based models (such as stochastic Petri nets) is straightforward.

Resource provisioning for cloud computing

In resource provisioning for cloud computing, an important issue is how resources may be allocated to an application mix such that the service level agreements (SLAs) of all applications are met. A performance model with two interactive job classes is used to determine the smallest number of servers required to meet the SLAs of both classes. For each class, the SLA is specified by the relationship: Prob [response time ≤ x] ≥ y. Two server allocation strategies are considered: shared allocation (SA) and dedicated allocation (DA). For the case of FCFS scheduling, analytic results for response time distribution are used to develop a heuristic algorithm that determines an allocation strategy (SA or DA) that requires the smallest number of servers. The effectiveness of this algorithm is evaluated over a range of operating conditions. The performance of SA with non-FCFS scheduling is also investigated. Among the scheduling disciplines considered, a new discipline called probability dependent priority is found to have the best performance in terms of requiring the smallest number of servers.

Performance model driven QoS guarantees and optimization in clouds

This paper presents a method for achieving optimization in clouds by using performance models in the development, deployment and operations of the applications running in the cloud. We show the architecture of the cloud, the services offered by the cloud to support optimization and the methodology used by developers to enable runtime optimization of the clouds. An optimization algorithm is presented which accommodates different goals, different scopes and timescales of optimization actions, and different control algorithms. The optimization here maximizes profits in the cloud constrained by QoS and SLAs across a large variety of workloads.

Introducing STRATOS: A Cloud Broker Service

This paper introduces a cloud broker service (STRATOS) which facilitates the deployment and runtime management of cloud application topologies using cloud elements/services sourced on the fly from multiple providers, based on requirements specified in higher level objectives. Its implementation and use is evaluated in a set of experiments.

The Landscape of Service-Oriented Systems: A Research Perspective

Service orientation has been touted as one of the most important technologies for designing, implementing and deploying large scale service provision software systems. In this position paper we attempt to investigate an initial classification of challenge areas related to service orientation and service-oriented systems. We start by organizing the research issues related to service orientation in three general categories- business, engineering and operations, plus a set of cross-cutting concerns across domain. We further propose the notion of Service Strategy as a binding model for these three categories. Finally, concluding this position paper, we outline a set of emerging opportunities to be used for further discussion.

Exploring Alternative Approaches to Implement an Elasticity Policy

An elasticity policy governs how and when resources (e.g., application server instances at the PaaS layer) are added to and/or removed from a cloud environment. The elasticity policy can be implemented as a conventional control loop or as a set of heuristic rules. In the control-theoretic approach, complex constructs such as tracking filters, estimators, regulators, and controllers are utilized. In the heuristic, rule-based approach, various alerts(e.g., events) are defined on instance metrics (e.g., CPU utilization), which are then aggregated at a global scale in order to make provisioning decisions for a given application tier. This work provides an overview of our experiences designing and working with both approaches to construct an auto scaler for simple applications. We enumerate different criteria such as design complexity, ease of comprehension, and maintenance upon which we form an informal comparison between the different methods. We conclude with a brief discussion of how these approaches can be used in the governance of resources to better meet a high-level goal over time.

Hierarchical model-based autonomic control of software systems

Various control algorithms are used in autonomic control to maintain Quality of Service (QoS) and Service Level Agreements (SLAs). Controllers are all based to some extent on models of the relationship between resources, QoS measures, and the workload imposed by the environment. This work discusses the range of algorithms with an emphasis on richer and more powerful models to describe non-linear performance relationships, and strong interactions among the system resources. A hierarchical framework is described which accommodates different scopes and timescales of control actions, and different control algorithms. The control algorithms and architectures can be considered in three stages: tuning, load balancing and provisioning. Different situations warrant different solutions, so this work shows how different control algorithms and architectures at the three stages can be combined to fit into different autonomic environments to meet QoS and SLAs across a large variety of workloads.

Service System Resource Management Based on a Tracked Layered Performance Model

Autonomic computer systems adapt themselves to cope with changes in the operating conditions and to meet the service-level agreements with a minimum of resources. Changes in operating conditions include hardware and software failures, load variation and variations in user interaction with the system. The self adaptation can be achieved by tuning the software, balancing the load or through hardware provisioning. This paper investigates a feed-forward adaptation scheme in which tuning and provisioning decisions are based on a dynamic predictive performance model of the system and the software. The model consists of a layered queuing network whose parameters are tuned by tracking the system with an Extended Kalman Filter. An optimization algorithm searches the system configuration space by using the predictive performance model to evaluate every configuration.