Akshay Narayan

Power-Aware Cloud Metering

The cost of electricity contributes significantly to the operating expense incurred in hosting cloud services. It is necessary to consider this cost while charging the consumers for their service utilization. In this work, we arrive at a metering mechanism for cloud services, in which the price of a cloud service tracks the variable input cost of electricity from a smart grid. The power-aware cloud metering developed here is a dynamic pricing and billing model where tariff for a cloud service is varied in accordance with the input electricity cost. We arrive at a model for power consumption of virtual machines hosted on the cloud infrastructure. This power consumption model is used in calculating the cost of operation of the service. A cloud instance leased by a consumer is billed based on the cost of operation obtained, and its resource utilization. Experimental results validate the approach presented.

System of Systems for Quality-of-Service Observation and Response in Cloud Computing Environments

As military, academic, and commercial computing systems evolve from autonomous entities that deliver computing products into network centric enterprise systems that deliver computing as a service, opportunities emerge to consolidate computing resources, software, and information through cloud computing. Along with these opportunities come challenges, particularly to service providers and operations centers that struggle to monitor and manage quality of service (QoS) for these services in order to meet customer service commitments. Traditional approaches fall short in addressing these challenges because they examine QoS from a limited perspective rather than from a system-of-systems (SoS) perspective applicable to a net-centric enterprise system in which any user from any location can share computing resources at any time. This paper presents a SoS approach to enable QoS monitoring, management, and response for enterprise systems that deliver computing as a service through a cloud computing environment. A concrete example is provided for application of this new SoS approach to a real-world scenario (viz., distributed denial of service). Simulated results confirm the efficacy of the approach.

The curse of 140 characters: evaluating the efficacy of SMS spam detection on android

Many applications are available on Android market place for SMS spam filtering. In this paper, we conduct a detailed study of the methods used in spam filtering in these applications by reverse engineering them. Our study has three parts. First, we perform empirical tests to valuate accuracy and precision of these apps. Second, we test if we can use email spam classifiers on short text messages effectively. Empirical test results show that these email spam classifiers do not yield optimal accuracy (like they do on emails) when used with SMS data. Finally, in this work we develop a two-level stacked classifier for short text messages and demonstrate the improvement in accuracy over traditional Bayesian email spam filters. Our experimental results show that spam filtering precision and accuracy of nearly 98% (which is comparable with those of email classifiers) can be obtained using the stacked classifier we develop.

System of Systems to provide Quality of Service monitoring, management and response in cloud computing environments

As military, academic, and commercial computing systems evolve from autonomous entities that deliver computing products into network centric enterprise systems that deliver computing as a service, opportunities emerge to consolidate computing resources, software and information through cloud computing. Along with these opportunities come challenges, especially to service providers and operations centers that struggle to monitor and manage Quality of Service (QoS) for these services in order to meet customer service commitments. Traditional approaches fall short in addressing these challenges because they examine QoS from a limited perspective rather than from a System of Systems (SoS) perspective applicable to a net-centric enterprise system in which any user from any location can share computing resources at any time. This paper presents a SoS approach to provide QoS monitoring, management, and response for enterprise systems that delivers computing as a service through a cloud computing environment. Concrete examples are provided that identify the key components of this SoS and their application to real-world scenarios. Simulated results demonstrate the effectiveness of the approach for a representative scenario.

Analysis of SaaS Business Platform Workloads for Sizing and Collocation

Sharing of physical infrastructure using virtualization presents an opportunity to improve the overall resource utilization. It is extremely important for a Software as a Service (SaaS) provider to understand the characteristics of the business application workload in order to size and place the virtual machine (VM) containing the application. A typical business application has a multi-tier architecture and the application workload is often predictable. Using the knowledge of the application architecture and statistical analysis of the workload, one can obtain an appropriate capacity and a good placement strategy for the corresponding VM. In this paper we propose a tool iCirrus-WoP that determines VM capacity and VM collocation possibilities for a given set of application workloads. We perform an empirical analysis of the approach on a set of business application workloads obtained from geographically distributed data centers. The iCirrus-WoP tool determines the fixed reserved capacity and a shared capacity of a VM which it can share with another collocated VM. Based on the workload variation, the tool determines if the VM should be statically allocated or needs a dynamic placement. To determine the collocation possibility, iCirrus-WoP performs a peak utilization analysis of the workloads. The empirical analysis reveals the possibility of collocating applications running in different time-zones. The VM capacity that the tool recommends, show a possibility of improving the overall utilization of the infrastructure by more than 70% if they are appropriately collocated.

Resource Procurement, Allocation, Metering, and Pricing in Cloud Computing

Cloud computing is not only a popular paradigm for services offered over the Internet, but has also captured the interest of both academia and industry.

Cloud-based Mission Observation, Response, Exploitation (CMORE) System

DoD, government agency, and commercial information technology systems are rapidly evolving from isolated enclaves to fully interoperable, net-centric, cloud-based enterprise systems where the vast amount of available data on which to make decisions is overwhelming end-users (e.g., mission commanders, operators and analysts). Many of these data are not useful for the mission at hand and must be extracted from the mission-relevant information before end-users can make decisions. These end-users seek a solution that allows them to observe relevant information in a timely manner and then to use this information to appropriately respond through information exploitation. This paper proposes a new and innovative solution to the above challenges that enables end-users to make timely mission decisions using a Cloud-based Mission Observation, Response, and Exploitation (CMORE) System that includes a mobile situational awareness (SA) console to identify information and to determine the correct response to mission events through information exploitation over the cloud.