Custom Windows Performance Counters Monitoring Mechanism for Measuring Quality of Service Attributes and Stability Coefficient Service-Oriented Architecture

Service-Oriented Architecture (SOA) has been widely used for different types of systems as their underlying architecture. The most popular technology that implements the SOA is web service. When several web services provide same functionalities, Quality of Service (QoS) of web services turn to be an important issue. In this study, monitoring is used in order to measure QoS attributes of web services in SOA. Several monitoring mechanisms have been proposed. Windows Performance Counters (WPC) is one of approaches for monitoring services at provider-side. However, WPC monitoring approach has a limitation and it can be employed just for WCF services. Moreover, predefined system counter values do not map to QoS values properly. In this research, a new provider-side monitoring mechanism which is based on Custom Windows Performance Counters (CWPC) is proposed in order to overcome current limitations. CWPC will be set to measure QoS attributes of web services such as response time, throughput and reliability properly. The results of CWPC monitoring are useful in taking decision in adjusting suitable monitoring interval for the system. Additionally, the result verifies that CWPC is an accurate monitoring approach for measuring QoS attributes. Besides that, this study also focuses on variability of QoS values which are obtained by monitoring of web services at different service invocation time. QoS values are variable and service consumers may experience various QoS values due to the fact that web services run in a distributed, dynamic, and unreliable environment which makes them exposed to faults and failures. In this research, a new Stability Coefficient is introduced to measure stability of a service based on historical QoS values that were obtained by monitoring the web service. Such a measure enables service consumers to find a stable and trustable service based on QoS attributes and it can increase consumer’s satisfaction. In this study, the Stability Coefficient is defined based on an average of different QoS attributes of service stability. The results confirm that the proposed Stability Coefficient is a proper criterion for determining stability of services in terms of their QoS attributes and a stable service with less QoS values variation has a high Stability Coefficient which may lead to more satisfaction to service consumer

Employing performance counters and software wrapper for measuring QoS attributes of web services

Web services have got popular for developing Service-Oriented Architectures recently. As several web services are available to execute the same function, Quality of Service (QoS) turns into a discriminative factor which is significantly considered in service selection and service composition approaches. In different approaches, monitoring of services is used for evaluating QoS attributes. Custom Windows Performance Counters (CWPC) is one of the approaches for monitoring performance of services at server-side. However, it has some limitations and it needs to access and change a service implementation which is not always possible in practice. In this paper, CWPC along with software wrapper is employed for measuring different QoS attributes such as response time, throughput and reliability in order to overcome current limitations. Additionally, it discusses how the proposed monitoring mechanism can be employed to optimize the service provider performance. The results show that the proposed monitoring approach is accurate in measuring QoS attributes

PROVENANCE FOR TRANSACTIONAL UPDATES

Database provenance explains how results are derived by queries. However, many use cases such as auditing and debugging of transactions require understanding of how the current state of a database was derived by a transactional history. We introduce an approach for capturing the provenance of transactions. Our approach does not just work for serializable concurrency control protocols but also for non-serializable protocols including snapshot isolation. The main drivers of our approach are a provenance model for queries, updates, and transactions and reenactment, a novel technique for retroactively capturing the provenance of tuple versions. We introduce the MV-semirings provenance model for updates and transactions as an extension of the existing semiring provenance model for queries. Our reenactment technique exploits the time travel and audit logging capabilities of modern DBMS to replay parts of a transactional history using queries. Importantly, our technique requires no changes to the transactional workload or underlying DBMS and results in only moderate runtime overhead for transactions. We discuss how our MV-semirings model and reenactment approach can be used to serve a wide variety of applications and use cases including answering of historical what-if queries which determine the effect of hypothetical changes to past operations of a business, post-mortem debugging of transactions, and to create private data workspaces for exploration. We have implemented our approach on top of a commercial DBMS and our experiments confirm that by applying novel optimizations we can efficiently capture provenance for complex transactions over large data sets