Design and Implementation of a Distributed Middleware for Parallel Execution of Legacy Enterprise Applications

A typical enterprise uses a local area network of computers to perform its business. During the off-working hours, the computational capacities of these networked computers are underused or unused. In order to utilize this computational capacity an application has to be recoded to exploit concurrency inherent in a computation which is clearly not possible for legacy applications without any source code. This thesis presents the design an implementation of a distributed middleware which can automatically execute a legacy application on multiple networked computers by parallelizing it. This middleware runs multiple copies of the binary executable code in parallel on different hosts in the network. It wraps up the binary executable code of the legacy application in order to capture the kernel level data access system calls and perform them distributively over multiple computers in a safe and conflict free manner. The middleware also incorporates a dynamic scheduling technique to execute the target application in minimum time by scavenging the available CPU cycles of the hosts in the network. This dynamic scheduling also supports the CPU availability of the hosts to change over time and properly reschedule the replicas performing the computation to minimize the execution time. A prototype implementation of this middleware has been developed as a proof of concept of the design. This implementation has been evaluated with a few typical case studies and the test results confirm that the middleware works as expected

Maintaining consistency in distributed systems

In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability

Event notification services: analysis and transformation of profile definition languages

The integration of event information from diverse event notification sources is, as with meta-searching over heterogeneous search engines, a challenging task. Due to the complexity of profile definition languages, known solutions for heterogeneous searching cannot be applied for event notification. In this technical report, we propose transformation rules for profile rewriting. We transform each profile defined at a meta-service into a profile expressed in the language of each event notification source. Due to unavoidable asymmetry in the semantics of different languages, some superfluous information may be delivered to the meta-service. These notifications are then post-processed to reduce the number of spurious messages. We present a survey and classification of profile definition languages for event notification, which serves as basis for the transformation rules. The proposed rules are implemented in a prototype transformation module for a Meta-Service for event notification

Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed