Soumi Chattopadhyay

A Scalable and Approximate Mechanism for Web Service Composition

In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary QoS guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose services optimally. In this paper, we present the design of a scalable but approximate QoS-aware service composition mechanism which balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability for dynamic service composition. We present experimental results to show the efficiency of our proposed mechanism.

QSCAS: QoS Aware Web Service Composition Algorithms with Stochastic Parameters

In recent times, automated business processes and web service technologies have become popular and ubiquitous for catering to diverse user needs. While providing a service, the service providers are typically expected to furnish promised QoS values for the services they deliver. However, when the services are physically deployed and invoked during a query resolution, these parameter values vary largely depending on different factors like network load, number of applications running in the server etc. In this work, we present a stochastic model of the web service composition problem. Experimental results on Web Service Challenge (WSC) benchmarks show the efficiency of our proposed mechanism.

CoCOA: A Framework for Comparing Aggregate Client Operations in BPO Services

Operational efficiency is a major indicator by which the profitability of a business process outsourcing (BPO) service is evaluated. To measure such operational efficiency, BPO service providers define and monitor a set of key performance indicators (KPI) (e.g., productivity of employees, turn-around-time). While a pair of clients can be directly compared using a KPI, comparing the aggregate client operations across multiple KPIs is non-trivial. This is primarily because KPIs are disparate in nature (e.g., cost is measured in dollar while turn-around-time is measured in minutes). In this paper, we present CoCOA, a framework that compares aggregate operations of clients in BPO services so that they can be viewed in a single pane of glass. Two key modules of CoCOA are: (a) client rank aggregator and (b) KPI importance classifier. For a given time period, the rank aggregator module determines an aggregate ranking of clients using variety of inputs (e.g., individual KPI rank, priority of a KPI). When the aggregate rank of a client deteriorates over successive time periods, KPI importance classifier identifies the responsible KPIs for such deterioration. Thus, CoCOA not only helps in comparing the aggregate operation of clients, but also provides prescriptive analytics for improving organizational performance for a given client. We evaluate our approach using anonymized data set collected from a real BPO business and show how responsible KPIs can be identified when there is a deterioration in aggregate client rank.

A Data Distribution Model for Large-Scale Context Aware Systems

Very large scale context aware systems are becoming a reality with the emerging paradigms such as machine-to-machine communications, crowdsensing, etc. Scalable data distribution is a critical requirement in such large scale systems for optimal usage of computing and communication resources. In this paper, we present a novel theoretical model for middleware design for such large-scale context aware systems that distributes only relevant data based on its effective utility. We also present extensive experimental results to validate the efficacy of our proposed model.

A utility-driven data transmission optimization strategy in large scale cyber-physical systems

In this paper, we examine the problem of data dissemination and optimization in the context of a large scale distributed cyber-physical system (CPS), and propose a novel rule-based mechanism for effective observation collection and transmission. Our work rests on the idea that all observations on all parameters are not required at all times, and thereby, selective data transmission can reduce sensor workload significantly. Experiments show the efficacy of our proposal.

QoS constrained Large Scale Web Service Composition using Abstraction Refinement

Efficient service composition in real time, while satisfying desirable Quality of Service (QoS) guarantees for the composite solution has been one of the topmost research challenges in the domain of services computing. On one hand, optimal QoS aware service composition algorithms, that come with the promise of solution optimality, are inherently compute intensive, and therefore, often fail to generate the optimal solution in real time for large scale web services. On the other hand, heuristic solutions that have the ability to generate solutions fast and handle large and complex service spaces, settle for sub-optimal solution quality. The problem of balancing the trade-off between computation efficiency and optimality in service composition has alluded researchers since quite some time, and several proposals for taming the scale and complexity of web service composition have been proposed in literature. In this paper, we present a new perspective towards this trade-off in service composition based on abstraction refinement, which can be seamlessly integrated on top of any off-the-shelf service composition method to tackle the space complexity, thereby, making it more time and space efficient. Instead of considering services individually during composition, we propose a set of abstractions and corresponding refinements to form service groups based on functional characteristics. The composition and QoS satisfying solution construction steps are carried out in the abstract service space. Our abstraction refinement methods give a significant speed-up compared to traditional composition techniques, since we end up exploring a substantially smaller space on average. Experimental results on benchmarks show the efficiency of our proposed mechanism in terms of time and the number of services considered for building the QoS satisfying composite solution.

A Fast and Scalable Mechanism for Web Service Composition

In recent times, automated business processes and web services have become ubiquitous in diverse application spaces. Efficient composition of web services in real time while providing necessary Quality of Service (QoS) guarantees is a computationally complex problem and several heuristic based approaches have been proposed to compose the services optimally. In this article, we present the design of a scalable QoS-aware service composition mechanism that balances the computational complexity of service composition with the QoS guarantees of the composed service and achieves scalability. Our design guarantees a single QoS parameter using an intelligent search and pruning mechanism in the composed service space. We also show that our methodology yields near optimal solutions on real benchmarks. We then enhance our proposed mechanism to guarantee multiple QoS parameters using aggregation techniques. Finally, we explore search time versus solution quality tradeoff using parameterized search algorithms that produce better-quality solutions at the cost of delay. We present experimental results to show the efficiency of our proposed mechanism.

A Scalable Rule Engine Architecture for Service Execution Frameworks

Rule engines form an essential component of most service execution frameworks in a Service Oriented Architecture (SOA) ecosystem. The efficiency of a service execution framework critically depends on the performance of the rule engine it uses to manage its operations. Most common rule engines suffer from the fundamental performance issues of the Rete algorithm that they internally use for faster matching of rules against incoming facts. In this paper, we present the design of a scalable architecture of a service rule engine, where a rule clustering and hashing based mechanism is employed for lazy loading of relevant service rules and a prediction based technique for rule evaluation is used for faster actuation of the rules. We present experimental results to demonstrate the efficacy of the proposed rule engine framework over contemporary ones.

A Framework for Top Service Subscription Recommendations for Service Assemblers.

It is common practice today for small and medium business houses to assemble and host services, than hosting everything themselves. To cater to diverse market needs, these houses often need to subscribe to different services from different information providers. The service contracts and the range of features and facilities supported and provided by the providers vary widely. A non-trivial challenge for a service assembler is in deciding the set of information providers to subscribe to, given the heterogeneity in the offerings provided, the economics of the business model, the target set of customers in the market place and most importantly, the profit margin. We present in this paper, an automated framework that addresses this challenge and aids a service assembler with a cost-feature-performance balanced recommendation of the providers that can best serve his needs. The problem gets exacerbated since there can be multiple dimensions/categories of services (e.g., hotel, flight, and local conveyance in the travel domain) and there can be multiple relevant recommendations which may be of use for the service assemblers. We examine the service subscription recommendation problem from different perspectives and present algorithms for service assembly. Experimental results on small-scale real data as well as large-scale simulation data show the efficacy of our proposal.

EAST: Efficient Assertion Simulation techniques

In the context of simulation-based verification, the Assertion-based Verification (ABV) methodology has become the technology of choice, with increasing proliferation of Verification / Assertion IPs for most commonly used protocols. To support the ABV flow, current generation simulators typically create threads for the assertions and evaluate each assertion separately by converting them into finite state automatons and monitoring their states during simulation. In this paper, we propose a different technique for assertion evaluation in a simulation-based verification flow. The proposed technique, EAST (Efficient Assertion Simulation Techniques), handles assertions in groups, instead of examining them in isolation, and achieves significant performance benefits. To this effect, our algorithm has a preprocessing phase (prior to simulation) which creates a shared data structure from the set of assertions using some simple rules, based on the assertion language operators. This is attached with the simulator and during simulation, at each evaluation cycle, EAST infers the decision of the assertions by a combination of lookup and substitution. We present our proposal using Linear Temporal Logic (LTL) assertions in this paper. Our prototype, EAST, achieves promising performance numbers in terms of both runtime and peak memory for both random and standard benchmark protocol designs.