Sajib Mistry

Long-Term QoS-Aware Cloud Service Composition Using Multivariate Time Series Analysis

We propose a cloud service composition framework that selects the optimal composition based on an end users long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multivariate QoS analysis to predict the long-term QoS provisions from service providers historical QoS data and short-term advertisements represented using Time Series. The quality of the QoS prediction is improved by incorporating QoS attributes intra correlations into the multivariate analysis. To select the optimal service composition, the proposed framework uses QoS time series inter correlations and performs a novel time series group similarity approach on the predicted QoS values. Experiments are conducted on real QoS dataset and results prove the efficiency of the proposed approach.

A service computing manifesto: the next 10 years

Mapping out the challenges and strategies for the widespread adoption of service computing.

Predicting Dynamic Requests Behavior in Long-Term IaaS Service Composition

We propose a novel composition framework for an Infrastructure-as-a-Service (IaaS) provider that selects the optimal set of long-term service requests to maximize its profit. Existing solutions consider an IaaS providers economic benefits at the time of service composition and ignore the dynamic nature of the consumer requests in a long-term period. The proposed framework deploys a new multivariate HMM and ARIMA model to predict different patterns of resource utilization and Quality of Service fluctuation tolerance levels of existing service consumers. The dynamic nature of new consumer requests with no history is modelled using a new community based heuristic approach. The predicted long-term service requests are optimized using Integer Linear Programming to find a proper configuration that maximizes the profit of an IaaS provider. Experimental results prove the feasibility of the proposed approach.

Metaheuristic Optimization for Long-term IaaS Service Composition

We propose a novel dynamic metaheuristic optimization approach to compose an optimal set of IaaS service requests to align with an IaaS provider’s long-term economic expectation. This approach is designed for the context that the IaaS provisioning subjects to resource and QoS constraints. In addition, the IaaS service requests have the features of dynamic resource and QoS requirements and variable arrival times. A new economic model is proposed to evaluate the similarity between the provider’s long-term economic expectation and a composition of service requests. The evaluation incorporates the factors of dynamic pricing and operation cost modeling of the service requests. An innovative hybrid genetic algorithm is proposed that incorporates the economic inter-dependency among the requests as a heuristic operator and performs repair operations in local solutions to meet the resource and QoS constraints. The proposed approach generates dynamic global solutions by updating the heuristic operator at regular intervals with the runtime behavior data of an existing service composition. Experimental results preliminarily prove the feasibility of the proposed approach.

Crowdsourced Coverage as a Service: Two-Level Composition of Sensor Cloud Services

We present a new two-level composition model for crowdsourced Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of sensors to select and compose Sensor-Cloud services. The quality model defines Coverage as a Service which is formulated as a composition of crowdsourced Sensor-Cloud services. We present two new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. Experimental results validate the performance of the proposed algorithms.

Qualitative Economic Model for Long-Term IaaS Composition

We propose a new qualitative economic model based optimization approach to compose an optimal set of infrastructure service requests over a long-term period. The economic model is represented as a temporal CP-Net to capture the provider’s dynamic business strategies in qualitative service provisions. The multidimensional qualitative preferences are indexed in a k-d tree to compute the preference ranking of a set of incoming requests. We propose a heuristic based sequential optimization process to select the most preferred composition without the knowledge of historical request patterns. Experimental results prove the feasibility of the proposed approach.

Probabilistic Qualitative Preference Matching in Long-Term IaaS Composition

We propose a qualitative similarity measure approach to select an optimal set of probabilistic Infrastructure-as-a-Service (IaaS) requests according to the provider’s probabilistic preferences over a long-term period. The long-term qualitative preferences are represented in probabilistic temporal CP-Nets. The preferences are indexed in a k-d tree to enable the multidimensional similarity measure using tree matching approaches. A probabilistic range sampling approach is proposed to reduce the large multidimensional search space in temporal CP-Nets. A probability distribution matching approach is proposed to reduce the approximation error in the similarity measure. Experimental results prove the feasibility of the proposed approach.

Social-Sensor Cloud Service for Scene Reconstruction

We propose a new social-sensor cloud services selection framework for scene reconstruction. The proposed research represents social media data streams, i.e., images’ metadata and related posted information, as social sensor cloud services. The functional and non-functional aspects of social sensor cloud services are abstracted from images’ metadata and related posted information. The proposed framework is a 4-stage algorithm, to select social-sensor cloud services based on the user queries. The selection algorithm is based on spatio-temporal indexing, spatio-temporal and textual correlations, and quality of services. Analytical results are presented to prove the efficiency of the proposed approach in comparison to a traditional approach of image processing.

A CP-Net Based Qualitative Composition Approach for an IaaS Provider

We propose a novel CP-Net based composition approach to qualitatively select an optimal set of consumers for an IaaS provider. The IaaS provider’s and consumers’ qualitative preferences are captured using CP-Nets. We propose a CP-Net composability model using the semantic congruence property of a qualitative composition. A greedy-based and a heuristic-based consumer selection approaches are proposed that effectively reduce the search space of candidate consumers in the composition. Experimental results prove the feasibility of the proposed composition approach.

Long-Term Qualitative IaaS Composition

User preferences are one of the key research subjects in developing personalized applications [126]. In many real life service composition scenarios, the target is to achieve the desired functional goal while ensuring user-provided preferences. For example, a travel planner usually composes services from different transportation and accommodation services. The functional goal of the planner is to find a trip from a source to a destination for its users. However, such a composition usually takes into account user preferences such as total costs, journey times and modes of transportation. A user may specify that he/she is flexible on tour dates but wishes to travel on business class or on a flight with a lower price.