Said Elnaffar

LinkedWS: A Novel Web Services Discovery Model Based on the Metaphor of Social Networks

Abstract Web services are increasingly becoming the de facto implementation for the service-oriented architecture paradigm for enterprises due to their ease of use. Nevertheless, discovering these Web services is still hindered by many challenges that are partially attributed to shortcomings found in the discovery registry models (e.g., UDDI) used nowadays. These registries do not, for example, capture the rich information resulting from the various types of interactions between Web services. To address these shortcomings, and inspired by the conventional human social networks on the net, like Facebook and Twitter, we develop LinkedWS , a social networks discovery model to capture the different interactions that occur between Web services. Based on these interactions, specialized relationships are spawned and discerned. Examples of these relationships are collaboration and substitution. This paper describes LinkedWS and its potential, and reports on its implementation status.

Automatically classifying database workloads

The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS, and then use the model to identify any change in the workload type. We construct a workload classifier from the Browsing and Ordering profiles of the TPC-W benchmark. Experiments with an industry-supplied workload show that our classifier accurately identifies the mix of OLTP and DSS work within an application workload.

Reputation of Communities of Web Services - Preliminary Investigation

Web services communities can be seen as virtual clusters that agglomerate Web services with the same functionality (e.g., FlightBooking). However, selecting a community to deal with is a challenging task to users and providers. Reputation, besides other selection criteria, has been widely used for evaluating and ranking candidates. Interestingly, the definition of community reputation from the perspective of users differs from the perspective of Web service providers. In this paper, we introduce a reputation-based Web services community architecture and define some of the performance metrics that are needed to assess the reputation of a Web service community as perceived by users and providers.

Today's DBMSs: how autonomic are they

The increasing complexity of database management systems (DBMSs) and the dearth of their experienced administrators make an urgent call for an autonomic DBMS that is capable of managing and maintaining itself. In this paper, we examine the characteristics that a DBMS should have in order to be considered autonomic and assess the position of todays commercial DBMSs such as DB2, SQL Server, and Oracle.

Workload Models for Autonomic Database Management Systems

Autonomic computing is a promising approach to the problem of effectively managing large complex software systems such as database management systems (DBMSs). In order to be self-managing, an autonomic DBMS (ADBMS) must understand key aspects of its workload, including composition, frequency patterns, intensity and resource requirements. It must therefore use and maintain different characterizations, or models, of the workload to support its various kinds of decision-making. Our research into various aspects of ADBMSs has led us to develop a number of different workload models. In this paper, we examine the importance of workload models to ADBMSs. We discuss the types of workload models needed by ADBMSs and describe examples from our research. We then outline the requirements for an infrastructure to develop and maintain the workload models needed by an ADBMS

The Sky: A Social Approach to Clouds Federation

Abstract With all the controversy about Cloud computing reality, its promising benefits and enduring challenges, service provisioning remains the heart of this contentious Internet phenomenon. In this paper, we solidify the description of the Sky model that aims to facilitate the provisioning of composite Cloud services by promoting collaboration among Cloud vendors and through the adoption of the social networking infrastructure. The proposed Sky model introduces a novel social-based approach towards federation of Clouds by providing Cloud vendors with the means to advertise their services, form interconnections, synergize, and provide value-added services to the end users.

Social Networks: Challenges and New Opportunities

Social networks are playing an important role in personal as well as corporate environments. However, perceived issues and evolving challenges may hinder further expansion of social networks to meet new opportunities. In this paper, we review inherent concepts and properties of social networks and highlight major analytical evaluation criteria, which are used to identify key findings that reveal degrees of benefits and shortcomings of social networks. We also discuss some proposed solutions related to decentralized social networks in the context of business implications as well as their effects on privacy, identity and trust issues. Future perspectives of social networks are also envisioned in this paper.

Is it DSS or OLTP: automatically identifying DBMS workloads

The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). A DBMS also typically experiences changes in the type of workload it handles during its normal processing cycle. Database administrators must therefore recognize the significant shifts of workload type that demand reconfiguring the system in order to maintain acceptable levels of performance. We envision intelligent, autonomic DBMSs that have the capability to manage their own performance by automatically recognizing the workload type and then reconfiguring their resources accordingly. In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. Using data mining techniques, we build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS and then use the model to identify any change in the workload type. We construct and compare classifiers built from two different sets of workloads, namely the TPC-C and TPC-H benchmarks and the Browsing and Ordering profiles from the TPC-W benchmark. We demonstrate the feasibility and success of these classifiers with TPC-generated workloads and with industry-supplied workloads.

A Dynamic Scheduling Algorithm for Divisible Loads in Grid Environments

Divisible loads are those workloads that can be partitioned by a scheduler into any arbitrary chunks. The problem of scheduling divisible loads has been defined for a long time, however, a handful of solutions have been proposed. Furthermore, almost all proposed approaches attempt to perform scheduling in dedicated environments such as LANs, whereas scheduling in non-dedicated environments such as Grids remains an open problem. In Grids, the incessant variation of a workers computing power is a chief difficulty of splitting and distributing workloads to Grid workers efficiently. In this paper, we first introduce a computation model that explains the impact of local (internal) tasks and Grid (external) tasks that arrive at a given worker. This model helps estimate the available computing power of a worker under the fluctuation of the number of local and Grid applications. Based on this model, we propose the CPU power prediction strategy. Additionally, we build a new dynamic scheduling algorithm by incorporating the prediction strategy into a static scheduling algorithm. Lastly we demonstrate that the proposed dynamic algorithm is superior to the existing dynamic and static algorithms by a comprehensive set of simulations.

Even Web Services Can Socialize: A New Service-Oriented Social Networking Model

The incessant growth of the number of Web services (WSs) makes their discovery more difficult. Standard discovery registries, such as UDDI and ebXML, have their own inherent limitations as they only describe the functionality aspect of each WS and not how it relates to others. Capturing the relationships between WSs as they interact with each other can be useful in many ways. In this paper, we present a novel model that captures such relationships using social networks. We describe how these WSs social networks can be initiated and how they evolve. We also discuss the types of associations (edges) that can exist among WSs (nodes). Traversing a WS social network makes it possible to identify a community of homogeneous WSs that are functionally similar. It can also enable us to use the model as a recommender system in case we need to replace a faulty WS, find a partner WS, or an add-on WS that may enrich the current business scenario.