Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

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Self-adaptive software: Landscape and research challenges

We describe a novel framework for specifying and monitoring Service Level Agreements (SLA) for Web Services. SLA monitoring and enforcement become increasingly important in a Web Service environment where enterprise applications and services rely on services that may be subscribed dynamically and on-demand. For economic and practical reasons, we want an automated provisioning process for both the service itself as well as the SLA managment system that measures and monitors the QoS parameters, checks the agreed-upon service levels, and reports violations to the authorized parties involved in the SLA management process. Our approach to these issues is presented in this paper. The Web Service Level Agreement (WSLA) framework is targeted at defining and monitoring SLAs for Web Services. Although WSLA has been designed for a Web Services environment, it is applicable as well to any inter-domain management scenario, such as business process and service management, or the management of networks, systems and applications in general. The WSLA framework consists of a flexible and extensible language based on XML Schema and a runtime architecture comprising several SLA monitoring services, which may be outsourced to third parties to ensure a maximum of objectivity. WSLA enables service customers and providers to unambiguously define a wide variety of SLAs, specify the SLA parameters and the way they are measured, and relate them to managed resource instrumentations. Upon receipt of an SLA specification, the WSLA monitoring services are automatically configured to enforce the SLA. An implementation of the WSLA framework, termed SLA Compliance Monitor, is publicly available as part of the IBM Web Services Toolkit.

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The WSLA Framework: Specifying and Monitoring Service Level Agreements for Web Services

Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. We present a dynamic analysis methodology that automates problem determination in these environments by 1) coarse-grained tagging of numerous real client requests as they travel through the system and 2) using data mining techniques to correlate the believed failures and successes of these requests to determine which components are most likely to be at fault. To validate our methodology, we have implemented Pinpoint, a framework for root cause analysis on the J2EE platform that requires no knowledge of the application components. Pinpoint consists of three parts: a communications layer that traces client requests, a failure detector that uses traffic-sniffing and middleware instrumentation, and a data analysis engine. We evaluate Pinpoint by injecting faults into various application components and show that Pinpoint identifies the faulty components with high accuracy and produces few false-positives.

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Pinpoint: problem determination in large, dynamic Internet services

There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.

Power and performance management of virtualized computing environments via lookahead control

Many interesting large-scale systems are distributed systems of multiple communicating components. Such systems can be very hard to debug, especially when they exhibit poor performance. The problem becomes much harder when systems are composed of "black-box" components: software from many different (perhaps competing) vendors, usually without source code available. Typical solutions-provider employees are not always skilled or experienced enough to debug these systems efficiently. Our goal is to design tools that enable modestly-skilled programmers (and experts, too) to isolate performance bottlenecks in distributed systems composed of black-box nodes.We approach this problem by obtaining message-level traces of system activity, as passively as possible and without any knowledge of node internals or message semantics. We have developed two very different algorithms for inferring the dominant causal paths through a distributed system from these traces. One uses timing information from RPC messages to infer inter-call causality; the other uses signal-processing techniques. Our algorithms can ascribe delay to specific nodes on specific causal paths. Unlike previous approaches to similar problems, our approach requires no modifications to applications, middleware, or messages.

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Performance debugging for distributed systems of black boxes

As businesses have grown, so has the need to deploy I/T applications rapidly to support the expanding business processes. Often, this growth was achieved in an unplanned way: each time a new application was needed a new server along with the application software was deployed and new storage elements were purchased. In many cases this has led to what is often referred to as "server sprawl", resulting in low server utilization and high system management costs. An architectural approach that is becoming increasingly popular to address this problem is known as server virtualization. In this paper we introduce the concept of server consolidation using virtualization and point out associated issues that arise in the area of application performance. We show how some of these problems can be solved by monitoring key performance metrics and using the data to trigger migration of Virtual Machines within physical servers. The algorithms we present attempt to minimize the cost of migration and maintain acceptable application performance levels.

Application Performance Management in Virtualized Server Environments

Techniques for managing information in a computing environment. Information associated with components of the computing environment is obtained. Then, from at least a portion of the obtained information, a determination is made as to the existence of one or more relationships associated with at least a portion of the components of the computing environment. The determination of the existence of one or more relationships is capable of accounting for a full lifecycle (e.g., including deployment, installation and runtime) associated with at least one component of the computing environment. Thus, techniques for managing runtime dependencies between the various components of computing systems are disclosed which provide a level of abstraction from individual systems and allow the computation of service/component (wherein the component may, for example, be an application, middleware, hardware, a device driver, an operating system and a system associated with the computing environment) dependencies that are related to end-to-end services, as perceived by a customer. By way of example, the inventive techniques may be applied to a distributed computing environment. The computing environment may also be an autonomic computing environment.

Methods and apparatus for managing dependencies in distributed systems

We describe a methodology for identifying and characterizing dynamic dependencies between system components in distributed application environments such as e-commerce systems. The methodology relies on active perturbation of the system to identify dependencies and the use of statistical modeling to compute dependency strengths. Unlike more traditional passive techniques, our active approach requires little initial knowledge of the implementation details of the system and has the potential to provide greater coverage and more direct evidence of causality for the dependencies it identifies. We experimentally demonstrate the efficacy of our approach by applying it to a prototypical e-commerce system based on the TPC-W Web commerce benchmark, for which the active approach correctly identifies and characterizes 41 of 42 true dependencies out of a potential space of 140 dependencies. Finally, we consider how the dependencies computed by our approach can be used to simplify and guide the task of root-cause analysis, an important part of problem determination.

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An active approach to characterizing dynamic dependencies for problem determination in a distributed environment

Computer-based methods and systems for performing automated distribution of a software package to one or more target machines in one or more regions of a distributed network of target machines, comprises the following steps. First, a base software package is prepared for each of the one or more regions based on at least one of: (i) policy data indicating which of the one or more regions are candidates for receiving the software package, (ii) dependency information indicating requisites for a service provided by the software package, and (iii) configuration information for each of the candidate regions. The base software package is then distributed to each of the candidate regions of the distributed network. The base software package received at each of the candidate regions is then customized based on at least one of: (i) regional distribution policies, (ii) dependency information specific to one or more roles performed by the target machines in that region, and (iii) individual target machine configuration information. Lastly, the software package customized in each of the candidate regions is distributed to at least one of the target machines in the candidate regions of the distributed network.

Systems and methods for service and role-based software distribution

Described are methods, apparatus and computer programs for determining run-time dependencies between logical components of a data processing environment. Components of the data processing environment are monitored by monitoring agents accessing run-time activity data via APIs of the managed system. A dependency generator identifies correlations between the run-time activity of the monitored components. For synchronous monitored systems, the dependency generator calculates an activity period for monitored components and determines which component's activity periods contain the activity periods of other components. Containment is used as an indicator of a likely dependency relationship, and a weighting is computed for each dependency relationship based on the consistency of containment.

Gautam Kar

Papers

Application Performance Management in Virtualized Server Environments

Methods and apparatus for managing dependencies in distributed systems

An active approach to characterizing dynamic dependencies for problem determination in a distributed environment

Systems and methods for service and role-based software distribution

Systems, methods and computer programs for determining dependencies between logical components in a data processing system or network