Understanding the performance difference of a multi-tier Cloud application between different provisioning plans and workloads is difficult to achieve. A typical IaaS provider offers a variety of virtual server instances with different performance capacities and rental rates. Such instances are often marked with a high level description of their hardware/software configuration (e.g. 1 or 2 vC-PUs) which provides insufficient information on the performance of the virtual server instances. Furthermore, as each tier of an application can be independently provisioned with different types and numbers of VMs, the number of possible provisioning plans grows exponentially with each additional tier.

Distributed state monitoring plays a critical role in Cloud datacenter management. One fundamental problem in distributed state monitoring is to minimize the monitoring cost while maximizing the monitoring accuracy at the same time. In this paper, we present Volley, a violation likelihood based approach for efficient distributed state monitoring in Cloud datacenters. Volley achieves both efficiency and accuracy with a flexible monitoring framework which uses dynamic monitoring intervals determined by the likelihood of detecting state violations. Volley consists of three unique techniques. It utilizes efficient node-level adaptation algorithms that minimize monitoring cost with controlled accuracy. Volley also employs a distributed scheme that coordinates the adaptation on multiple monitor nodes of the same task for optimal task- level efficiency. Furthermore, it enables multi-task level cost reduction by exploring state correlation among monitoring tasks. We perform extensive experiments to evaluate Volley with system, network and application monitoring tasks in a virtualized datacenter environment. Our results show that Volley can reduce considerable monitoring cost and still deliver user specified monitoring accuracy under various scenarios.

State monitoring is widely used for detecting critical events and abnormalities of distributed systems. As the scale of such systems grows and the degree of workload consolidation increases in Cloud data centers, node failures and performance interferences, especially transient ones, become the norm rather than the exception. Hence, distributed state monitoring tasks are often exposed to impaired communication caused by such dynamics on different nodes. Unfortunately, existing distributed state monitoring approaches are often designed under the assumption of always-online distributed monitoring nodes and reliable inter-node communication. As a result, these approaches often produce misleading results which in turn introduce various problems to Cloud users who rely on state monitoring results to perform automatic management tasks such as auto-scaling. This paper introduces a new state monitoring approach that tackles this challenge by exposing and handling communication dynamics such as message delay and loss in Cloud monitoring environments. Our approach delivers two distinct features. First, it quantitatively estimates the accuracy of monitoring results to capture uncertainties introduced by messaging dynamics. This feature helps users to distinguish trustworthy monitoring results from ones heavily deviated from the truth, yet significantly improves monitoring utility compared with simple techniques that invalidate all monitoring results generated with the presence of messaging dynamics. Second, our approach also adapts to non-transient messaging issues by reconfiguring distributed monitoring algorithms to minimize monitoring errors. Our experimental results show that, even under severe message loss and delay, our approach consistently improves monitoring accuracy, and when applied to Cloud application auto-scaling, outperforms existing state monitoring techniques in terms of the ability to correctly trigger dynamic provisioning.

Replication is commonly used to address the scalability and availability requirements of collaborative web applications in domains such as computer supported cooperative work, social networking, e-commerce and e-banking. While providing substantial benefits, replication also introduces the ov

To tackle SOA projects that span across various boundaries, enterprises are adopting a federated SOA approach in order to manage reuse across service domains. Managing service reuse involves sharing a subset of the services that are provided within a domain and fulfilling references to services required by applications or other services in a domain. While this is a major goal, it is also important for a federated enterprise to enable the autonomy of its multiple service domains. This paper proposes an approach for cross-domain service management that enables domain autonomy and that preserves across domains properties that are taken for granted by services within a domain. We introduce a cross-domain service management capability and show how this capability allows for services to be shared and reused without the need for a federation architect, and how it preserves intra-domain properties, thus enabling domain autonomy in a federation.

This paper proposes an approach for cross-domain connectivity that enables domain autonomy and that preserves across domains properties that are taken for granted by services within a domain.

Many reputation management systems have been developed under the assumption that each entity in the system will use a variant of the same scoring function. Much of the previous work in reputation management has focused on providing robustness and improving performance for a given reputation scheme. In this paper, we present a reputation-based trust management framework that supports the synthesis of trust-related feedback from many different entities while also providing each entity with the flexibility to apply different scoring functions over the same feedback data for customized trust evaluations. We also propose a novel scheme to cache trust values based on recent client activity. To evaluate our approach, we implemented our trust management service and tested it on a realistic application scenario in both LAN and WAN distributed environments. Our results indicate that our trust management service can effectively support multiple scoring functions with low overhead and high availability.

Service composition has emerged as a fundamental technique for developing Web applications. Multiple services, often from different organizations or trust domains, may be dynamically composed to satisfy a user's request. Access control in the presence of service compositions is a challenging security problem. In this paper, we present an access control model and techniques for specifying and enforcing access control rules on Web service compositions. A key advantage of our approach is that past histories of service invocations can be used to make access control decisions. Our approach allows role hierarchies and separation of duty constraints. Access controls rules may be parameterized by one or more arguments. We have implemented our access control model via a declarative policy specification language which uses pure-past linear temporal logic (PPLTL). We describe an implementation of our approach using a supply chain management (SCM) application. Our experiments show that our approach can enforce expressive and flexible access control policies while incurring reasonable performance overhead on the application.

A major goal of service-oriented architectures is to enable software interoperability in heterogeneous and dynamic environments. Web services standards and protocols aim to support this goal and middleware systems implementing these standards and protocols consequently are needed. Maintenance and administration of middleware is made difficult due to variations in standards and their constant evolution. In this paper, we introduce a new service-oriented middleware architecture for runtime Web services interoperability. Different from other middleware systems our approach applies service-oriented computing principles on the middleware layer, thereby establishing an on-demand model for middleware features. Clients can use middleware as services, dynamically discovering and using the services as interoperability requirements are determined. Further, middleware as services allows middleware to be provided and managed separately from its clients. We present the policy-based programming model, architecture, and details of our middleware, and discuss new challenges that arise in this context, such as distribution of middleware services. The approach is validated through a scenario integrating Web service transaction middleware

Distributed applications composed of collections of Web services may call for diverse levels of reliability in different parts of the system. Byzantine fault tolerance (BFT) is a general strategy that has recently been shown to be practical for the development of certain classes of survivable, client-server, distributed applications; however, little research has been done on incorporating it into selective parts of multi-tier, distributed applications like Web services that have heterogeneous reliability requirements. To understand the impacts of combining BFT and Web services, we have created Thema, a new BFT middleware system that extends the BFT and Web services technologies to provide a structured way to build Byzantine-fault-tolerant, survivable Web services that application developers can use like other Web services. From a reliability perspective, our enhancements are also novel in that they allow Byzantine-fault-tolerant services: (1) to support the multi-tiered requirements of Web services, and (2) to provide standardized Web services support for their own clients (through WSDL interfaces and SOAP communication). In this paper we study key architectural implications of combining BFT with Web services and provide a performance evaluation of Thema using the TPC-W benchmark.

In order to make software components more flexible and reusable, it is desirable to provide business users with facilities to assemble and control them without their needing programming knowledge. This paper describes a fully functional prototype middleware system where variability is externalized so that core applications need not be altered for anticipated changes. In this system, application behavior modification is fast and easy, making this middleware suitable for frequently changing programs.

A holy grail of component-based software engineering is write-once, reuse everywhere. However, in modern distributed, component-based systems supporting emerging application areas such as service-oriented e-business (where Web services are viewed as components) and peer-to-peer computing, this is difficult. Non-functional requirements (related to quality-of-service (QoS) issues such as security, reliability, and performance) vary with deployment context, and sometimes even at run-time, complicating the task of re-using components. In this paper, we present a middleware-based approach to managing dynamically changing QoS requirements of components. Policies are used to advertise non-functional capabilities and vary at run-time with operating conditions. We also provide middleware enhancements to match, interpret, and mediate QoS requirements of clients and servers at deployment time and/or runtime.

Standard messaging middleware guarantees the delivery of messages to intermediary destinations like message queues, but does not guarantee the receipt or the processing of a message by final recipients. Conditional messaging is an extension to standard messaging middleware that addresses this shortcoming by allowing an application to define, monitor, and evaluate various conditions on messages, such as time constraints on the receipt or the processing of a message by a set of final recipients. In this paper, we introduce the notion of conditional messaging, and present the design and implementation of a flexible and reliable system that supports conditional messaging for use in Java 2 Enterprise Edition and message queuing environments. Our solution uniquely shifts the responsibilities for implementing the management of conditions on messages from the application to the middleware. We further discuss the grouping of multiple conditional messages into atomic units-of-work, which can also integrate requests to transactional resources like distributed objects using object middleware. Conditional messaging serves to implement various kinds of backward dependencies for distributed object transactions that integrate messaging.

Many enterprise systems employ both object-oriented middleware (OOM) and message-oriented middleware (MOM). However support for the integration of object and messaging services, in particular for transaction processing across object and messaging components, is very limited. In this paper we introduce the concept of Dependency-Spheres (D-Spheres), a global transaction context for distributed objects and messages. A D-Sphere integrates standard distributed object transactions and conditional asynchronous messages in one single unit-of-work. It is a new service for transaction processing that enhances two-phase-commit ACID transactions with pre-commit message delivery, concurrent evaluations of transaction-critical message conditions, and message compensation support for recovery. D-Spheres enrich standard OOM and MOM services, uniquely providing for an increased level of reliability for their use in combination in enterprise systems.

This paper addresses the problem of identifying a probabilistic FSM from its output. The authors propose a formal description of the FSM and its output, using a regular phrase-structured grammar. This description is associated with a cost measuring its information content. The authors then state the inference problem as a combinatorial optimization problem with an objective function define as the cost of the description of the FSM and its output. A heuristic algorithm is proposed which processes the output "on line" and yields a local minimum of the authors' criterion. At each step (i.e. as new data is observed and processed), the procedure searches locally through the space of non-probabilistic FSMs, i.e., the transitions are initially regarded as being only present or absent. When a non-probabilistic model has been generated, the transition probabilities are determined from their relative frequencies given the behavior indicated by the data. This yields maximum likelihood estimates of the probabilities. The costs of the obtained probabilistic FSMs are then computed, and the K minimum cost FSMs are kept as starting points for the next step search. At each step, the generation of the FSMs is done via a local search through the neighborhoods of the K best FSMs obtained at the previous step. This algorithm is compared with similar algorithms proposed previously, and tested on a range of examples. It is found to work for a wider range of FSMs than the previous methods, and it is much more practical for large problems than previously proposed exhaustive search techniques.>

The authors address the problem of identifying the topology of a network from data collected at a designated node (possibly one of the network nodes). The data considered are descriptions of the local neighborhood about each node, which specify the identities of the node and of its neighbors. These data are neither required to be correct nor complete. The authors propose a model that describes and relates the network topology and the data. They define costs associated with this model, and reduce the identification problem to a combinational optimization problem with and an objective function based on these costs. A pseudo-polynomial-time algorithm is derived, which yields a local minimum of the objective function. The local minimum appears to be a reasonable solution, as shown by a range of examples.>

Emerging networks such as multihop radio networks or reconfigurable lightwave networks have a dynamically changing topology as part of their normal operation. In such networks, a precise and timely knowledge of the topology becomes critical for network management and control.If every network node could instantly provide reliable information about itself and its neighbors, it would be relatively easy to assemble this information and derive the overall network topology. However, both the assumptions of correctness and instantaneous delivery of the information do not hold in practice. We address the problem of identifying the topology of a network from possibly incorrect and/or incomplete data collected at a designated node (possibly one of the network nodes). We propose a model that describes and relates the network topology and the data, define costs associated with this model, and reduce the identification problem to a combinatorial optimization problem with an objective function based on these costs. A pseudo polynomial-time algorithm is derived, which yields a local minimum of our objective function. This local minimum appears to be a reasonable solution, as shown by a range of examples